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Journal articles on the topic 'Bi-Criteria decision making'
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Dahong, Tang, and Chen Ting. "Multi-criteria Decision Making Problems with Bi-level Multiagent." IFAC Proceedings Volumes 22, no. 10 (1989): 275–79. http://dx.doi.org/10.1016/s1474-6670(17)53185-7.
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Fazlollahtabar, Hamed, Ermia Aghasi, and Peter Forte. "Bi-Objective Two-Stage Decision-Making Process for Service Marketing." International Journal of Strategic Decision Sciences 3, no. 3 (2012): 24–39. http://dx.doi.org/10.4018/jsds.2012070103.
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The authors propose a bi-objective two-stage decision-making process to help the marketing team of a company to determine which services make more profit. The decision is based on customer satisfaction measures which are related to the different company services. Thus, they constitute a multi-criteria assessment of the company’s performances. The first stage of the authors’ proposed process is to evaluate the services with respect to certain criteria using a stochastic multi-criteria acceptability analysis. Then, a bi-objective mathematical model is utilized to determine which services are more profitable. An analytical hierarchy process is applied to aggregate the bi-objective model. The applicability and validity of the proposed process is illustrated in a case study.
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Zhang, Ling, Yan Xu, Chung-Hsing Yeh, Le He, and De-Qun Zhou. "Bi-TOPSIS: A New Multicriteria Decision Making Method for Interrelated Criteria With Bipolar Measurement." IEEE Transactions on Systems, Man, and Cybernetics: Systems 47, no. 12 (2017): 3272–83. http://dx.doi.org/10.1109/tsmc.2016.2573582.
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Malakooti, Behnam. "Double Helix Value Functions, Ordinal/Cardinal Approach, Additive Utility Functions, Multiple Criteria, Decision Paradigm, Process, and Types (Z Theory I)." International Journal of Information Technology & Decision Making 14, no. 06 (2015): 1353–400. http://dx.doi.org/10.1142/s0219622014500412.
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Z Utility Theory refers to a class of nonlinear utility functions for solving Risk and Multiple Criteria Decision-Making problems. Z utility functions are hybrids of additive and nonadditive (nonlinear) functions. This paper addresses the concepts and assessment methods for the additive part of Z-utility functions for multiple criteria problems that satisfy the efficiency (nondominancy) principle. We provide a decision paradigm and guidelines on how to approach, formulate, and solve decision-making problems. We, also, overview the modeling of decision process based on four types of decision-making styles. For multi-criteria problems, a new definition of convex efficiency is introduced. Also polyhedral efficiency is developed for presenting multi-criteria efficiency (nondominancy) graphically. New double helix quasi-linear value functions for multi-criteria are developed. Two types of double helix value functions for solving bi-criteria (Advantages versus Disadvantages) and also risk problems are introduced: Food–Fun curves for expected values and Fight-Flight curves for expected risk values. Ordinal/Cardinal Approach (OCA) for assessment of additive utility functions is developed. Simple consistency tests to determine whether the assessed utility function satisfies ordinal and/or cardinal properties are provided. We show that OCA can also be used to solve outranking problems. We provide a critique of Analytic Hierarchy Process (AHP) for assessing additive value functions and show that the developed Ordinal/Cardinal Approach overcomes the shortcomings of AHP. We also develop a unified/integrated approach for simultaneous assessment of nonlinear value and additive (multi-criteria) utility functions. These results in an additive utility function that can be concave, convex, or hybrid concave/convex based on the nonlinear value function. Finally, we show an interactive paired comparisons approach for solving nonadditive and nonlinear utility functions for bi-criteria decision-making problems. Several illustrative examples are provided. The paper provides reliable and robust approaches for modeling the utility preferences of heterogeneous economic agents in macro and micro-economics.
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Huang, Deng Kui, Huan Neng Chiu, Ruey Huei Yeh, and Jen Huei Chang. "A fuzzy multi-criteria decision making approach for solving a bi-objective personnel assignment problem." Computers & Industrial Engineering 56, no. 1 (2009): 1–10. http://dx.doi.org/10.1016/j.cie.2008.03.007.
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ABO-SINNA, MAHMOUD A., and AZZA H. AMER. "TOPSIS Approach for Solving Bi-Level Non-Linear Fractional MODM Problems." JOURNAL OF ADVANCES IN MATHEMATICS 13, no. 4 (2018): 7353–70. http://dx.doi.org/10.24297/jam.v13i4.6243.
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TOPSIS (technique for order preference similarity to ideal solution) is considered one of the known classical multiple criteria decision making (MCDM) methods to solve bi-level non-linear fractional multi-objective decision making (BL-NFMODM) problems, and in which the objective function at each level is considered nonlinear and maximization type fractional functions. The proposed approach presents the basic terminology of TOPSIS approach and the construction of membership function for the upper level decision variable vectors, the membership functions of the distance functions from the positive ideal solution (PIS) and of the distance functions from the negative ideal solution (NIS). Thereafter a fuzzy goal programming model is adopted to obtain compromise optimal solution of BL-NFMODM problems. The proposed approach avoids the decision deadlock situations in decision making process and possibility of rejecting the solution again and again by lower level decision makers. The presented TOPSIS technique for BL-NFMODM problems is a new fuzzy extension form of TOPSIS approach suggested by Baky and Abo-Sinna (2013) (Applied Mathematical Modelling, 37, 1004-1015, 2013) which dealt with bi -level multi-objective decision making (BL-MODM) problems. Also, an algorithm is presented of the new fuzzy TOPSIS approach for solving BL-NFMODM problems. Finally, an illustrative numerical example is given to demonstrate the approach.
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Ghazanfari, Mehdi, Saeed Rouhani, and Mostafa Jafari. "A fuzzy TOPSIS model to evaluate the Business Intelligence competencies of Port Community Systems." Polish Maritime Research 21, no. 2 (2014): 86–96. http://dx.doi.org/10.2478/pomr-2014-0023.
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Abstract Evaluation of the Business Intelligence (BI) competencies of port community systems before they are bought and deployed is a vital importance for establishment of a decision-support environment for managers. This study proposes a new model which provides a simple approach to the assessment of the BI competencies of port community systems in organization. This approach helps decision-makers to select an enterprise system with appropriate intelligence requirements to support the managers’ decision-making tasks. Thirtyfour criteria for BI specifications are determined from a thorough review of the literature. The proposed model uses the fuzzy TOPSIS technique, which employs fuzzy weights of the criteria and fuzzy judgments of port community systems to compute the evaluation scores and rankings. The application of the model is realized in the evaluation, ranking and selecting of the needed port community systems in a port and maritime organization, in order to validate the proposed model with a real application. With utilizing the proposed model organizations can assess, select, and purchase port community systems which will provide a better decision-support environment for their business systems.
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Araz, Ozgur M., Tim Lant, John W. Fowler, and Megan Jehn. "Simulation modeling for pandemic decision making: A case study with bi-criteria analysis on school closures." Decision Support Systems 55, no. 2 (2013): 564–75. http://dx.doi.org/10.1016/j.dss.2012.10.013.
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Gadomski, Jan, and Lech Kruś. "Objectives of an enterprise. Bi-criteria analysis and negotiation problems." Control and Cybernetics 50, no. 1 (2021): 169–93. http://dx.doi.org/10.2478/candc-2021-0010.
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Abstract A decision-making process is considered for a firm, in which two coexisting groups of interests pursue different goals. An original model based on a non-neoclassical production function is proposed. The function satisfies the conditions formulated by R. Frisch, which makes it possible to investigate firms operating in the environment far from the perfect competition and pursuing goals other than profit maximization. A two-criteria optimization problem is formulated with the two criteria representing the goals of the groups: maximization of profit and maximization of income generated by the firm with respect to capital and labor. The problem is considered in two variants of the product market, namely the perfect and the imperfect competition. Solutions of the problem are analyzed including the derived Pareto sets. The importance of knowledge about the Pareto set in negotiations between the groups of interests in the firm is illustrated and discussed.
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Krivulin, Nikolai. "Algebraic Solution to Constrained Bi-Criteria Decision Problem of Rating Alternatives through Pairwise Comparisons." Mathematics 9, no. 4 (2021): 303. http://dx.doi.org/10.3390/math9040303.
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We consider a decision-making problem to evaluate absolute ratings of alternatives from the results of their pairwise comparisons according to two criteria, subject to constraints on the ratings. We formulate the problem as a bi-objective optimization problem of constrained matrix approximation in the Chebyshev sense in logarithmic scale. The problem is to approximate the pairwise comparison matrices for each criterion simultaneously by a common consistent matrix of unit rank, which determines the vector of ratings. We represent and solve the optimization problem in the framework of tropical (idempotent) algebra, which deals with the theory and applications of idempotent semirings and semifields. The solution involves the introduction of two parameters that represent the minimum values of approximation error for each matrix and thereby describe the Pareto frontier for the bi-objective problem. The optimization problem then reduces to a parametrized vector inequality. The necessary and sufficient conditions for solutions of the inequality serve to derive the Pareto frontier for the problem. All solutions of the inequality, which correspond to the Pareto frontier, are taken as a complete Pareto-optimal solution to the problem. We apply these results to the decision problem of interest and present illustrative examples.
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Pramanik, Dipika, Samar Chandra Mondal, and Anupam Haldar. "Resilient supplier selection to mitigate uncertainty: soft-computing approach." Journal of Modelling in Management 15, no. 4 (2020): 1339–61. http://dx.doi.org/10.1108/jm2-01-2019-0027.
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Purpose In recent years, determining the effective and suitable supplier in the supply chain management (SCM) has become a key strategic consideration to the success of any manufacturing organization in terms of business intelligence (BI), as many quantitative and qualitative critical factors are measured from big data. In today’s competitive business scenario, the main purpose of this study is to determine suitable and sustainable suppliers during supplier selection process is to reduce the risk of investment along with maximize overall value to the customer and develop closeness and long-term relationships between customers and suppliers to build a resilient SCM to mitigate uncertainty for automotive organizations. Design/methodology/approach As these types of decisions generally involve more than a few criteria and often necessary to compromise among possibly conflicting factors, the multiple-criteria decision-making becomes a useful approach to solve this kind of problem. Considering both tangible and intangible criteria, the aim of this paper is the presentation of a new integrated fuzzy analytic hierarchy process and fuzzy additive ratio assessment method with fuzzy entropy using linguistic values to solve the supplier selection problem to build the resilient SCM under uncertain data. Fuzzy entropy is used to obtain the entropy weights of the criteria. Findings Organizations gather massive amounts of information known as BD on the basis of historical records of uncertainties from several internal and external sources to manage uncertainty to improve the overall performance of organizations using BI strategy for analyzing and making effective decision to support the managements of automotive manufacturing organizations in an information system. Research limitations/implications Although this study tries to represent a full analysis on suitable and resilient global supplier selection under various types of uncertainty, still there are some improvements that can be made in the future by developing a more refined and more sophisticated approach to further enhance the performance of the proposed scheme to calculate overall rating scores of the alternatives. Originality/value The novelty of this paper is to propose a framework of BI in SCM to determine a suitable and resilient global supplier where all the meaningful information, relevant knowledge and visualization retrieved by analyzing the huge and complex set of data or data streams, i.e. BD based on decision-making, to develop any manufacturing organizational performance worldwide.
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Gandhi, Kanika, Kannan Govindan, and P. C. Jha. "Fuzzy bi-criteria decision making approach for supplier selection and distribution network planning in supply chain management." Journal of Information and Optimization Sciences 37, no. 5 (2016): 653–79. http://dx.doi.org/10.1080/02522667.2016.1191184.
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Town, Paul, and Fadi Thabtah. "Data Analytics Tools: A User Perspective." Journal of Information & Knowledge Management 18, no. 01 (2019): 1950002. http://dx.doi.org/10.1142/s0219649219500023.
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Business Intelligence Tools (BI Tools) can be an intelligent way for individuals to undertake data analysis and reporting for guiding decision-making processes. There are many different BI Tools available in the market today, as well as information to assist organisations in evaluating their effectiveness. This paper focusses on two commercially available BI Tools: Tableau and Microsoft Power BI. It aims to determine which BI Tool is better for data analysis and reporting from an end user’s point of view. This paper undertakes an evaluation of both tools and compares which is more suitable for students using interface (navigation), cost, presence in the market, and available training and help as the evaluative criteria. Results produced in this paper found that overall, Tableau was more highly ranked than Power BI based on the evaluative criteria for end users for data analysis and reporting at least among the samples of the study. Tableau ranked higher than Power BI with its presence in the market, and available training and help. Power BI was rated more highly on its interface and both BI Tools were ranked the same in terms of cost to end users. This research is exploratory and may assist in formulating future research on BI Tools for specific user groups.
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Kuehne, Flora, Linda Sanftenberg, Tobias Dreischulte, and Jochen Gensichen. "Shared Decision Making Enhances Pneumococcal Vaccination Rates in Adult Patients in Outpatient Care." International Journal of Environmental Research and Public Health 17, no. 23 (2020): 9146. http://dx.doi.org/10.3390/ijerph17239146.
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Insufficient vaccination rates against pneumococcal disease are a major problem in primary health care, especially in adult patients. Shared decision-making (SDM) may address major barriers to vaccination. The objective of this review was to assess the impact of SDM on pneumococcal vaccination rates in adult patients. We conducted a systematic literature search in MEDLINE, EMBASE, CENTRAL, PsycINFO, and ERIC. RCTs and cluster RCTs were included, if they aimed to enhance pneumococcal vaccination rates in adult patients and comprised a personal interaction between health care provider (HCP) and patient. Three further aspects of the SDM process (patient activation, bi-directional exchange of information and bi-directional deliberation) were assessed. A meta-analysis was conducted for the effects of interventions on vaccination rates. We identified eight studies meeting the inclusion criteria. The pooled effect size was OR (95% CI): 2.26 (1.60–3.18) comparing intervention and control groups. Our findings demonstrate the efficacy of interventions that enable a SDM process to enhance pneumococcal vaccination rates; although, the quality of evidence was low. In exploratory subgroup analyses, we concluded that an impersonal patient activation and an exchange of information facilitated by nurses are sufficient to increase vaccination rates against pneumococcal disease in adult patients. However, the deliberation of options between physicians and patients seemed to be more effective than deliberation of options between nurses and patients.
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Asadollahi-Yazdi, Elnaz, Julien Gardan, and Pascal Lafon. "Multi-objective optimization approach in design for additive manufacturing for fused deposition modeling." Rapid Prototyping Journal 25, no. 5 (2019): 875–87. http://dx.doi.org/10.1108/rpj-07-2018-0186.
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Purpose This paper aims to provide a multi-objective optimization problem in design for manufacturing (DFM) approach for fused deposition modeling (FDM). This method considers the manufacturing criteria and constraints during the design by selecting the best manufacturing parameters to guide the designer and manufacturer in fabrication with FDM. Design/methodology/approach Topological optimization and bi-objective optimization problems are suggested to complete the DFM approach for design for additive manufacturing (DFAM) to define a product. Topological optimization allows the shape improvement of the product through a material distribution for weight gain based on the desired mechanical behavior. The bi-objective optimization problem plays an important role to evaluate the manufacturability by quantification and optimization of the manufacturing criteria and constraint simultaneously. Actually, it optimizes the production time, required material regarding surface quality and mechanical properties of the product because of two significant parameters as layer thickness and part orientation. Findings A comprehensive analysis of the existing DFAM approaches illustrates that these approaches are not developed sufficiently in terms of manufacturability evaluation in quantification and optimization levels. There is no approach that investigates the AM criteria and constraints simultaneously. It is necessary to provide a decision-making tool for the designers and manufacturers to lead to better design and manufacturing regarding the different AM characteristics. Practical implications To assess the efficiency of this approach, a wheel spindle is considered as a case study which shows how this method is capable to find the best design and manufacturing solutions. Originality/value A multi-criteria decision-making approach as the main contribution is developed to analyze FDM technology and its attributes, criteria and drawbacks. It completes the DFAM approach for FDM through a bi-objective optimization problem which deals with finding the best manufacturing parameters by optimizing production time and material mass because of the product mechanical properties and surface roughness.
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Jankowski, Marcin, Aleksandra Borsukiewicz, and Kamel Hooman. "Development of Decision-Making Tool and Pareto Set Analysis for Bi-Objective Optimization of an ORC Power Plant." Energies 13, no. 20 (2020): 5280. http://dx.doi.org/10.3390/en13205280.
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Power plants based on organic Rankine cycle (ORC) are known for their capacity in converting low and medium-temperature energy sources to electricity. To find the optimal operating conditions, a designer must evaluate the ORC from different perspectives including thermodynamic performance, technological limits, economic viability, and environmental impact. A popular approach to include different criteria simultaneously is to formulate a bi-objective optimization problem. This type of multi-objective optimization (MOO) allows for finding a set of optimal design points by defining two different objectives. Once the optimization is completed, the decision-making analysis shall be carried out to identify the final design solution. This study aims to develop a decision-making tool for facilitating the choice of the optimal design point. The proposed procedure is coded in MATLAB based on the commonly used Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). By providing the capability to graphically identify the decisions taken, the tool developed in the study is called Tracking and Recognizing Alternative Design Solutions (TRADeS). Analysis of our data shows that certain regions of Pareto set points should be excluded from the design space. It was noted that in these regions a high rate at which one of the objectives moves away from its ideal value coincides with a low rate at which the second criterion approaches its ideal solution. Hence, it was recommended that the criteria weights corresponding to excluded regions of the Pareto set should be discarded when selecting the final design point. By comparing the results obtained using the proposed model to those of existing decision-making techniques, it was concluded that while the known approaches are appropriate for an easy and fast selection of the final design point, the presented procedure allows for a more comprehensive and well-rounded design. It was shown that our design tool can be successfully applied in the decision-making analysis for problems that aim at optimizing the ORC using two design criteria. Finally, the proposed software benefits from a generic structure and is easy to implement which will facilitate its use in other industrial applications.
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Javed, Maham, and Waqas Haider Bukhari. "Is History a Useful Guide for Decision Making: A Comparative Study of Decision Making in Bi-Polar and Uni-Polar World in the Context of Suez Crisis and Falkland Island War." Journal of Social & Organizational Matters 2, no. 1 (2023): 11–18. http://dx.doi.org/10.56976/jsom.v2i1.15.
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To develop better understanding of world, history is necessary as it enables us to understand the world in which we live in more precise way. History can proved to be a driving force as well. It can play a very important role in decision making. But it’s not right to totally rely on it as circumstances changes with time. With changing circumstances requirements and criteria also change. New world structure which has emerged in last century is more state and ego centric than ever involved in competition under the umbrella of democracy which seems to be very fascinating but actually it’s leading world towards drastic end by creating trust deficit among states. So a decision which was a best suitable decision in one circumstance can’t always be a perfect fit for other situations because we know with changing circumstances requirements also change e.g. US has different policies in unipolar world than in bi-polar world. Developing knowledge and understanding of historical events and trends, specially over the past century enables us to develop a much greater understanding for current events but proper calculation is necessary. That’s why a critical rationality with proper reasoning is required for better and more relying decisions by decision makers. Before implementation it’s necessary to make sure that you are right and then go ahead as stated by ‘Davy Crockett’.
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Emmanuel Osamuyimen Eboigbe, Oluwatoyin Ajoke Farayola, Funmilola Olatundun Olatoye, Obiageli Chinwe Nnabugwu, and Chibuike Daraojimba. "BUSINESS INTELLIGENCE TRANSFORMATION THROUGH AI AND DATA ANALYTICS." Engineering Science & Technology Journal 4, no. 5 (2023): 285–307. http://dx.doi.org/10.51594/estj.v4i5.616.
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This paper delves into the transformative role of Artificial Intelligence (AI) and Data Analytics in the realm of Business Intelligence (BI), marking a significant shift in the landscape of business decision-making and strategic planning. The study's purpose was to comprehensively explore the evolution of BI, underscored by the integration of AI and advanced data analytics, and to project the future trajectory of these technologies within the business context. Adopting a systematic literature review as its methodology, the study meticulously analyzed a wide array of scholarly articles and industry reports. This approach facilitated a deep understanding of the historical development of BI, the current synergy between AI, Data Analytics, and BI, and the emerging trends shaping their future. The inclusion and exclusion criteria for sources were rigorously applied to ensure the relevance and quality of the information gathered. The findings of the study highlighted a paradigm shift from traditional data processing methods to AI-driven predictive analytics, significantly enhancing the efficiency, accuracy, and predictive capabilities of BI tools. This evolution has redefined business operations, offering unprecedented insights and fostering more informed decision-making processes. Conclusively, the study posits that the integration of AI and Data Analytics into BI is a fundamental, rather than a transient, shift in business operations. It recommends further exploration into the ethical implications of AI in BI, the development of user-friendly AI tools for non-technical users, and an examination of the long-term impacts of AI-driven BI across various industries. The study's classical and engaging tone aims to captivate and inform a diverse audience, from academic researchers to industry practitioners.
 Keywords: Artificial Intelligence, Business Intelligence, Data Analytics, Predictive Analytics.
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Liu, Jia, and Shuwei Wang. "A method based on TODIM technique for multi-criteria two-sided matching and its application in person-position matching." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 467–80. http://dx.doi.org/10.3233/jifs-202087.
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It is impossible for agents on both sides to achieve complete rationality in the decision-making process of two-sided matching (TSM). The TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method considering the psychological behavior of decision-makers is well applied in the multiple criteria decision making (MCDM) problems. The TSM is a MCDM problem. Therefore, in this paper, a method based on TODIM technique is introduced to solve the TSM problem, in which the intuitionistic linguistic numbers are utilized to describe the mutual evaluation between candidates and hiring managers. The focus of this paper is to develop a method for the multi-criteria TSM problem under intuitionistic linguistic environment. First, the evaluation matrices of each agent with respect to each criterion are provided by agents on the opposite side, and the weight assigned to each criterion is determined according to the importance of the evaluation criterion to the matching agent. Then, the dominance measurement of each agent over another one can be calculated based on the intuitionistic linguistic TODIM method. Next, a bi-objective optimization model which aims to maximize the overall satisfaction degree of agents on both sides is constructed to attain the optimal matching pair. Furthermore, the feasibility of the solution method is verified by a case study of person-position matching (PPM), and the matching result demonstrates that the proposed method is effective in dealing with multi-criteria PPM problem. Finally, the sensitivity of parameters and some comparative studies are discussed.
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Khalilzadeh, Mohammad, Sayyid Ali Banihashemi, and Darko Božanić. "A Step-By-Step Hybrid Approach Based on Multi-Criteria Decision-Making Methods And A Bi-Objective Optimization Model To Project Risk Management." Decision Making: Applications in Management and Engineering 7, no. 1 (2024): 442–72. http://dx.doi.org/10.31181/dmame712024884.
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Project success and achieving project objectives and goals highly depend on effective and thorough risk management implementation. This study provides a comprehensive and practical methodology for project risk management. In this paper, firstly, the risks were collected by analyzing the historical documents and literature. Then, the collected risks were screened using brainstorming and categorized into five groups. Subsequently, a questionnaire was made and the identified risks were validated using the Fuzzy Delphi technique. Also, the relationships between risks were determined using the Interpretive Structural Modelling (ISM) method. Moreover, the weights of the criteria used to rank the risks were calculated through the Fuzzy Best-Worst Method. Subsequently, the major risks were determined using the fuzzy WASPAS method. Furthermore, a novel bi-objective mathematical programming model was developed and solved using the Augmented Epsilon-Constraint (AEC) method to choose the optimal risk response strategies for each critical risk. The results demonstrated that the proposed framework is effective in dealing with construction project risks.
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Seyfi-Shishavan, Seyed Amin, Fatma Kutlu Gündoğdu, Yaser Donyatalab, Elmira Farrokhizadeh, and Cengiz Kahraman. "A Novel Spherical Fuzzy Bi-Objective Linear Assignment Method and Its Application to Insurance Options Selection." International Journal of Information Technology & Decision Making 20, no. 02 (2021): 521–51. http://dx.doi.org/10.1142/s0219622021500073.
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Spherical fuzzy sets are the latest extension of the ordinary fuzzy sets. The main characteristic of the spherical fuzzy sets is satisfying the condition that the squared sum of the membership, nonmembership, and hesitancy degrees must be at least zero and at most one. In this research, by extending the classical linear assignment method to bi-objective linear assignment and integrating it with cosine similarity measure, we presented a novel beneficial method for solving multiple criteria group decision-making problems in the spherical fuzzy environment. A new concept for weighting the criteria, which is composed of positive and negative impacts (weights), is introduced. The proposed bi-objective model tries to maximize positive impacts and minimize the negative impacts simultaneously. In order to solve the bi-objective linear assignment model, [Formula: see text]-constraint method is applied. Therefore, a trade-off solution is formed between maximizing positive impacts and minimizing negative impacts. The applicability and validity of the proposed method are shown through an insurance options selection problem. To test the reliability and validity of the proposed method, comparative and sensitivity analysis are performed.
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Pourmohammadreza, Nima, and Mohammad Reza Akbari Jokar. "Efficient last-mile logistics with service options: A multi-criteria decision-making and optimization methodology." International Journal of Industrial Engineering Computations 15, no. 2 (2024): 367–86. http://dx.doi.org/10.5267/j.ijiec.2024.2.003.
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The rapid growth of online shopping has intensified the need for cost-effective and efficient delivery systems, posing a significant challenge for businesses worldwide. This study proposes an innovative two-phase methodology that uses a hybrid multi-criteria decision-making (MCDM) approach for efficient last-mile logistics with service options (ELMLSO) such as home delivery, self-pickup, and differently-priced services. This approach aims to streamline last-mile logistics by integrating these service options, resulting in a more comprehensive and effective delivery network that enhances customer satisfaction and maintains a competitive edge. The first phase employs the Ordinal Preference Analysis - Evaluation based on Distance from Average Solution (OPA-EDAS) method to select optimal pickup and delivery centers. The second phase identifies the optimal route using a bi-objective mixed-integer mathematical model, striving to balance cost minimization and customer satisfaction maximization. The Normalized Normal Constraint Method (NNCM) is utilized to solve this model. The application of these methods results in considerable cost savings and improved customer satisfaction, offering valuable insights for managers within the last-mile logistics industry.
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Mirdda, Habib Ali, Somnath Bera, Masood Ahsan Siddiqui, and Bhoop Singh. "Analysis of bi-variate statistical and multi-criteria decision-making models in landslide susceptibility mapping in lower Mandakini Valley, India." GeoJournal 85, no. 3 (2019): 681–701. http://dx.doi.org/10.1007/s10708-019-09991-3.
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Solano, Maria C., and Juan C. Cruz. "Integrating Analytics in Enterprise Systems: A Systematic Literature Review of Impacts and Innovations." Administrative Sciences 14, no. 7 (2024): 138. http://dx.doi.org/10.3390/admsci14070138.
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Recent advancements in Enterprise Information Systems (EISs) have transitioned from primarily supporting operational and tactical processes to enabling strategic decision-making through integrated analytics. This systematic review critically examines global literature from 2010 to 2023, focusing on the factors influencing the adoption of analytical components in EISs and assessing their impact on strategic decision-making in organizations. Following the PRISMA 2020 guidelines, we meticulously selected and reviewed articles from the Scopus database, employing a robust taxonomy based on the technology–organization–environment (TOE) framework to categorize findings. Our methodology involved a thorough screening of 234 studies, leading to a final analysis of 45 peer-reviewed articles that met our stringent criteria. These studies collectively underscore a significant gap in organizational capabilities, notably in the business ecosystems surrounding EISs, which hampers the effective adoption and utilization of advanced analytics. The results highlight a distinct need for improved understanding and implementation strategies for integrated analytics within EISs to enhance strategic decision-making processes. This review identifies critical factors for integrating analytics into Enterprise Information Systems (EISs), emphasizing technological, organizational, and environmental dimensions. It highlights a significant gap in models guiding ERP systems with Business Intelligence (BI) capabilities and underscores the need for robust research to enhance strategic decision-making through analytics.
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VIRVOU, M., G. A. TSIHRINTZIS, E. ALEPIS, I. O. STATHOPOULOU, and K. KABASSI. "EMOTION RECOGNITION: EMPIRICAL STUDIES TOWARDS THE COMBINATION OF AUDIO-LINGUAL AND VISUAL-FACIAL MODALITIES THROUGH MULTI-ATTRIBUTE DECISION MAKING." International Journal on Artificial Intelligence Tools 21, no. 02 (2012): 1240001. http://dx.doi.org/10.1142/s0218213012400015.
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In this paper, we present and discuss a novel approach for the integration of audio-lingual and visual-facial modalities in a bi-modal user interface for affect recognition. As it is widely acknowledged, two or more modalities of interaction can provide complementary information to each other with respect to affect recognition. However, satisfactory progress has not yet been achieved towards the integration of these modalities, since the problem of combining them effectively is quite complicated. In our research, we combine the two modalities from the perspective of a human observer by employing a multi-criteria decision making theory for dynamic affect recognition of computer users. An important research milestone that is required in our approach is the specification of the strengths and weaknesses of each modality with respect to affect recognition concerning 6 basic emotion states. These emotion states are happiness, sadness, surprise, anger and disgust as well as the emotionless state which we refer to as neutral. For this purpose, we describe two empirical studies that we have conducted involving human users and human observers concerning the recognition of emotions from audio-lingual and visual-facial modalities. The results of the empirical studies have been used to assign weights to criteria for the application of a multi-criteria decision making theory. Moreover, the results of the empirical studies provide information that may be used by other researchers in the field of affect recognition.
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Liu, Xiaoyue, and Dawei Ju. "Hesitant Fuzzy 2-Dimension Linguistic Programming Technique for Multidimensional Analysis of Preference for Multicriteria Group Decision Making." Mathematics 9, no. 24 (2021): 3196. http://dx.doi.org/10.3390/math9243196.
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The hesitant fuzzy 2-dimension linguistic element (HF2DLE) allows decision makers to express the importance or reliability of each term included in a hesitant fuzzy linguistic element as a linguistic term. This paper investigates a programming technique for multidimensional analysis of preference for hesitant fuzzy 2-dimension linguistic multicriteria group decision making. Considering the flexibility of HF2DLEs in expressing hesitant qualitative preference information, we first adopt HF2DLEs to depict both the evaluation values of alternatives and the truth degrees of alternative comparisons. To calculate the relative closeness degrees (RCDs) of alternatives, the Euclidean distances between HF2DLEs are defined. Based on RCDs and preference relations on alternatives, the group consistency and inconsistency indices are constructed, and a bi-objective hesitant fuzzy 2-dimension linguistic programming model is established to derive the criteria weights and positive and negative ideal solutions. Since the objective functions and partial constraint coefficients of the established model are HF2DLEs, an effective solution is developed, through which the RCDs can be calculated to obtain the individual rankings of alternatives. Furthermore, a single-objective assignment model is constructed to determine the best alternative. Finally, a case study is conducted to illustrate the feasibility of the proposed method, and its effectiveness is demonstrated by comparison analyses.
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Ep Kallel, Mariem Belghith, Sana Bouajaja, and Abdelkarim Elloumi. "Developing a Sales Dashboard with Power BI – A Case Study in a Pharmaceutical Company." Decision Making Advances 2, no. 1 (2024): 142–47. http://dx.doi.org/10.31181/dma21202438.
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A dashboard is a steering tool for data-driven decision-making and a lever for innovation in the pharmaceutical industry. It makes it possible to integrate relevant performance measurement indicators, chosen according to the objectives to be achieved and according to specific criteria. In this context, our work is inscribed, which aims to develop a dashboard for the sales department of a pharmaceutical company. The choice of this tool is motivated by its importance and the strategic challenge it presents for companies today. The implementation of this dashboard will be broken down into four phases. First, we collect sales department sales and develop the data marts. Then, to generate the data model, we have to import the database into Power BI (Microsoft Power Business Intelligent). We select the adequate KPIs (Key Performance Indicators) to create the interactive sales dashboard. This dashboard allows the decision makers to analyze and visualize data, moving from traditional sales data storage to big data analytics. Based on this visualization tool, the sales manager can propose potential improvements and essential investments and develop the management review to be presented as an annual report. This report is important for managing and monitoring pharmaceutical activities. Existing data are used and visualized to target the goals of decision-makers.
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Hunt, Brian J., Vincent Y. Blouin, and Margaret M. Wiecek. "Modeling Relative Importance of Design Criteria With a Modified Pareto Preference." Journal of Mechanical Design 129, no. 9 (2006): 907–14. http://dx.doi.org/10.1115/1.2747634.
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Engineering design problems are studied within a multicriteria optimization and decision-making framework. A methodology is developed that modifies the traditional Pareto preference to model designer’s preferences reflected in the relative importance of criteria. The intent is to reduce the number of candidate designs to facilitate the selection of a preferred design. The versatility of this preference model allows it to be incorporated into the problem solution process either a priori, a posteriori, or iteratively, each offering different advantages. In the a priori approach, all Pareto efficient designs that do not satisfy the designer’s preferences are never computed. In the a posteriori approach, a set of Pareto efficient designs is computed and then easily reduced based on the designer’s preferences. Finally, the iterative approach offers the ability to adjust the designer’s preferences by exploring their impact on the reduction of the Pareto efficient design set. The methodology is based on the concepts of convex cones and allowable tradeoff values between criteria. The theoretical foundation of the preference model is presented in the context of engineering design and the methodology is illustrated using a bi-criteria structural design problem and a tri-criteria vehicle dynamics design problem.
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Liu, Feiran, Jun Liu, and Xuedong Yan. "Solving the Asymmetry Multi-Objective Optimization Problem in PPPs under LPVR Mechanism by Bi-Level Programing." Symmetry 12, no. 10 (2020): 1667. http://dx.doi.org/10.3390/sym12101667.
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Optimizing the cost and benefit allocation among multiple players in a public-private partnership (PPP) project is recognized to be a multi-objective optimization problem (MOP). When the least present value of revenue (LPVR) mechanism is adopted in the competitive procurement of PPPs, the MOP presents asymmetry in objective levels, control variables and action orders. This paper characterizes this asymmetrical MOP in Stackelberg theory and builds a bi-level programing model to solve it in order to support the decision-making activities of both the public and private sectors in negotiation. An intuitive algorithm based on the non-dominated sorting genetic algorithm III (NSGA III) framework is designed to generate Pareto solutions that allow decision-makers to choose optimal strategies from their own criteria. The effectiveness of the model and algorithm is validated via a real case of a highway PPP project. The results reveal that the PPP project will be financially infeasible without the transfer of certain amounts of exterior benefits into supplementary income for the private sector. Besides, the strategy of transferring minimum exterior benefits is more beneficial to the public sector than to users.
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Sirbiladze, Gia, Bidzina Matsaberidze, Bezhan Ghvaberidze, Bidzina Midodashvili, and David Mikadze. "Fuzzy TOPSIS based selection index in the planning of emergency service facilities locations and goods transportation." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 1949–62. http://dx.doi.org/10.3233/jifs-210636.
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The attributes influencing the decision-making process in planning transportation of goods from selected facilities locations in disaster zones are considered. Experts evaluate each candidate for humanitarian aid distribution centers (HADCs) (service centers) against each uncertainty factor in q-rung orthopair fuzzy sets (q-ROFS). For representation of experts’ knowledge in the input data for planning emergency service facilities locations a q-rung orthopair fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach is developed. Based on the offered fuzzy TOPSIS aggregation a new innovative objective function is introduced which maximizes a candidate HADC’s selection index and reduces HADCs opening risks in disaster zones. The HADCs location and goods transportation problem is reduced to the bi-criteria problem of partitioning the set of customers by the set of service centers: 1) Minimization of opened HADCs and goods transportation total costs; 2) Maximization of HADCs selection index. Partitioning type transportation constraints are also constructed. Our approach for solving the constructed bi-criteria partitioning problem consists of two phases. In the first phase, based on the covering’s matrix, we generate a new matrix with columns allowing to find all possible partitioning of the demand points with the opened HADCs. In the second phase, using the generated matrix and our exact algorithm we find the partitioning –allocations of the HADCs to the centers corresponded to the Pareto-optimal solutions. The constructed model is illustrated with a numerical example.
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Herdiana, Oding. "Perencanaan Business Intelligence untuk Strategi Pengembangan Produk Unggulan Menggunakan Algoritma Support Vector Machine." Jurnal Informatika 2, no. 2 (2023): 28–34. http://dx.doi.org/10.57094/ji.v2i2.1088.
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The government continues to encourage the government to accelerate Small and Medium Enterprises (SMEs) flagship products to enter the digital market, because the current digitalization of SMEs cannot be ignored, SMEs must be able to change their business to the digital realm. The strategy for developing superior SME products needs to be well planned, because a good and measurable strategy is very important for policy making. Development uses an approach that emphasizes that the driving force of development is commodities that are considered to be superior, both at the domestic, national and international levels. Superior products that have quality and are needed by customers require the right selection process, so we need a method to produce this information. Business Intelligence (BI) is a global term for all processes, techniques, and tools that support business decision making based on information technology. The strategy for developing superior products for SME products uses the criteria of production data, income, and SME respondent data in determining the priority list. A good product assessment based on priority can be done by BI using the classification method with the Support Vector Machine (SVM) algorithm. The research method carried out for planning the development of superior products for MSMEs in Tasikmalaya City is a research development research. To complete the classification of the right superior product development strategy, then Support Vector Machine algorithm can be used with test results using the Confusion Matrix with an average accuracy of 97%.
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Andrade, Renata Machado de, Suhyung Lee, Paul Tae-Woo Lee, Oh Kyoung Kwon, and Hye Min Chung. "Port Efficiency Incorporating Service Measurement Variables by the BiO-MCDEA: Brazilian Case." Sustainability 11, no. 16 (2019): 4340. http://dx.doi.org/10.3390/su11164340.
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Data envelopment analysis (DEA) has many advantages for analyzing the efficiency of decision-making units, as well as drawbacks, such as a lack of discrimination power. This study applied bi-objective multiple-criteria data envelopment analysis (BiO-MCDEA), a programming approach used to overcome the limitations of traditional DEA models, to analyze the efficiency of 20 Brazilian ports with a consideration of six input and one output variables from 2010 to 2016. Two time-related variables were included to reflect current problems faced by Brazilian ports experiencing long wait times. The results reveal a significant disparity in port efficiency among Brazilian ports. The top five most efficient ports are those with the highest cargo throughput. A clustering analysis also confirmed a strong correlation between cargo throughput and port efficiency scores. Total time of stay, pier length, and courtyard also had strong correlations with the efficiency scores. The clustering method divided Brazilian ports into three groups: efficient ports, medium efficient ports, and inefficient ports.
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Liu, Feng, Jian-Jun Wang, Haozhe Chen, and De-Li Yang. "Machine scheduling with outsourcing." International Journal of Logistics Management 25, no. 1 (2014): 133–59. http://dx.doi.org/10.1108/ijlm-12-2012-0142.
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Purpose – The purpose of this paper is to study the use of outsourcing as a mechanism to cope with supply chain uncertainty, more specifically, how to deal with sudden arrival of higher priority jobs that require immediate processing, in an in-house manufacturer's facility from the perspective of outsourcing. An operational level schedule of production and distribution of outsourced jobs to the manufacturer's facility should be determined for the subcontractor in order to achieve overall optimality. Design/methodology/approach – The problem is of bi-criteria in that both the transportation cost measured by number of delivery vehicles and schedule performance measured by jobs’ delivery times. In order to obtain the problem's Pareto front, we propose dynamic programming (DP) heuristic solution procedure based on integrated decision making, and population-heuristic solution procedures using different encoding schemes based on sequential decision making. Computational studies are designed and carried out by randomly generating comparative variations of numerical problem instances. Findings – By comparing several existing performance metrics for the obtained Pareto fronts, it is found that DP heuristic outperforms population-heuristic in both solutions diversity and proximity to optimal Pareto front. Also in population-heuristic, sub-range keys representation appears to be a better encoding scheme for the problem than random keys representation. Originality/value – This study contributes to the limited yet important knowledge body on using outsourcing approach to coping with possible supply chain disruptions in production scheduling due to sudden customer orders. More specifically, we used modeling methodology to confirm the importance of collaboration with subcontractors to effective supply chain risk management.
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Salehizadeh, Mohammad Reza, Mahdi Amidi Koohbijari, Hassan Nouri, Akın Taşcıkaraoğlu, Ozan Erdinç, and João P. S. Catalão. "Bi-Objective Optimization Model for Optimal Placement of Thyristor-Controlled Series Compensator Devices." Energies 12, no. 13 (2019): 2601. http://dx.doi.org/10.3390/en12132601.
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Exposure to extreme weather conditions increases power systems’ vulnerability in front of high impact, low probability contingency occurrence. In the post-restructuring years, due to the increasing demand for energy, competition between electricity market players and increasing penetration of renewable resources, the provision of effective resiliency-based approaches has received more attention. In this paper, as the major contribution to current literature, a novel approach is proposed for resiliency improvement in a way that enables power system planners to manage several resilience metrics efficiently in a bi-objective optimization planning model simultaneously. For demonstration purposes, the proposed method is applied for optimal placement of the thyristor controlled series compensator (TCSC). Improvement of all considered resilience metrics regardless of their amount in a multi-criteria decision-making framework is novel in comparison to the other previous TCSC placement approaches. Without loss of generality, the developed resiliency improvement approach is applicable in any power system planning and operation problem. The simulation results on IEEE 30-bus and 118-bus test systems confirm the practicality and effectiveness of the developed approach. Simulation results show that by considering resilience metrics, the performance index, importance of curtailed consumers, congestion management cost, number of curtailed consumers, and amount of load loss are improved by 0.63%, 43.52%, 65.19%, 85.93%, and 85.94%, respectively.
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Jayakrishnan, Mailasan, Abdul Karim Mohamad, and Mokhtar Mohd Yusof. "Developing railway supplier selection excellence using business intelligence knowledge management framework." International Review of Applied Sciences and Engineering 12, no. 3 (2021): 257–68. http://dx.doi.org/10.1556/1848.2021.00267.
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AbstractIn a broad scope, the term Information System (IS) is a scientific field of research study that approaches the scope of managerial, strategic, and operational activities complex in the storing, processing, distributing, gathering, and utilizing of knowledge and its associated technologies in organizations and industry. The model of railway supplier selection using BI-KM framework is situated on a horizontal structure of the organization and its technology transformation to execute the organization goal, with technology as enabler and driver (technology adoption), organization as the principal environment (business process analysis), and Information Management (data modeling). This study is significant in supporting data scenarios by focusing on the heuristic view of an industry approach to problem-solving management issues. Furthermore, the research development was to identify integrated framework adoption that contributes to strategic performance diagnostics dashboard. By understanding the factors of theoretical framework adoption, these conceptual frameworks assure competitive advantage. Besides, this railway supplier selection excellence model analyzed the extent and provides a potential solution to strategic decision-making issues. The study directs to regulate the adoption of the theoretical framework towards conceptual framework by using the role of Business Intelligence (BI) to analyze the quality of data presented as the railway supplier selection criteria from operational management through data analytics. Moreover, this will be united to help the best cycles and instruments in essential execution by the executives of a railway supplier selection dashboard for simulating data as interactive supplier performance.
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Kurniawan, Fachrul, Sarina Sulaiman, Siaka Konate, and Modawy Adam Ali Abdalla. "Deep learning approaches for MIMO time-series analysis." International Journal of Advances in Intelligent Informatics 9, no. 2 (2023): 286. http://dx.doi.org/10.26555/ijain.v9i2.1092.
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This study presents a comparative analysis of various deep learning (DL) methods for multi-input and multi-output (MIMO) time-series forecasting of stock prices. The analysis is conducted on a dataset comprising the stock price of Bitcoin. The dataset consists of 2950 rows from December 2017 to December 2021. This study aims to evaluate the performance of multiple DL methods, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU). The evaluation criteria for selecting the best-performing methods in this research are based on two performance metrics: Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). These metrics were chosen for specific reasons related to assessing the accuracy and reliability of the forecasting models. MAPE is used to assess accuracy, while RMSE helps detect outliers in the system. Results show that the LSTM method achieves the best performance, outperforming other methods with an average MAPE value of 8.73% and Bi-LSTM has the best average RMSE value of 0.02216. The findings of this study have practical implications for time-series forecasting in the field of stock trading. The superior performance of LSTM highlights its potential as a reliable method for accurately predicting stock prices. The Bi-LSTM model's ability to detect outliers can aid in identifying abnormal stock market behavior. In summary, this research provides insights into the performance of various DL models of MIMO for stock price forecasting. The results contribute to the field of time-series forecasting and offer valuable guidance for decision-making in stock trading by identifying the most effective methods for predicting stock prices accurately and detecting unusual market behavior.
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Zhang, Wenying, Xifu Wang, and Kai Yang. "Incentive Contract Design for the Water-Rail-Road Intermodal Transportation with Travel Time Uncertainty: A Stackelberg Game Approach." Entropy 21, no. 2 (2019): 161. http://dx.doi.org/10.3390/e21020161.
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In the management of intermodal transportation, incentive contract design problem has significant impacts on the benefit of a multimodal transport operator (MTO). In this paper, we analyze a typical water-rail-road (WRR) intermodal transportation that is composed of three serial transportation stages: water, rail and road. In particular, the entire transportation process is planned, organized, and funded by an MTO that outsources the transportation task at each stage to independent carriers (subcontracts). Due to the variability of transportation conditions, the travel time of each transportation stage depending on the respective carrier’s effort level is unknown (asymmetric information) and characterized as an uncertain variable via the experts’ estimations. Considering the decentralized decision-making process, we interpret the incentive contract design problem for the WRR intermodal transportation as a Stackelberg game in which the risk-neutral MTO serves as the leader and the risk-averse carriers serve as the followers. Within the framework of uncertainty theory, we formulate an uncertain bi-level programming model for the incentive contract design problem under expectation and entropy decision criteria. Subsequently, we provide the analytical results of the proposed model and analyze the optimal time-based incentive contracts by developing a hybrid solution method which combines a decomposition approach and an iterative algorithm. Finally, we give a simulation example to investigate the impact of asymmetric information on the optimal time-based incentive contracts and to identify the value of information for WRR intermodal transportation.
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Afanador Franco, Fernando, Maria P. Molina Jiménez, Lady T. Pusquin Ospina, et al. "Coastal Marine Planning: Vision of the Maritime Authority. Case of the Department of Bolivar, Colombia." Revista Costas 6, Vol Esp. 2 (2021): 137–64. http://dx.doi.org/10.26359/costas.e0721.
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Marine Spatial Planning is a tool that has acquired significant importance worldwide. Around 70 countries have implemented this initiative given the increased activity within the maritime sector and pressure on marine resources. The methods used are adapted to each country’s characteristics and articulated with other management processes. Although Colombia has progressed through on the processes regarding this issue, through different agencies, marine spatial planning related to maritime activities is absent. Therefore, the General Maritime Directorate (DIMAR in Spanish) through its commitment to turning Colombia into a bi-oceanic power, under a holistic and comprehensive maritime safety approach, contributes to marine and coastal areas management with a methodology for Marine and Coastal Management with a Maritime Authority Vision (MCM: MAV), focused on analyzed current and future conditions using Geographic Information Systems (GIS), multi-criteria analysis, and an Allocation and Co-location Model (ACM). The method was applied to Bolivar Department marine and coastal area, resulting in the identification of 55 uses/activities, and obtaining zoning by index and by the number of conflicts, as well as a map of free areas. This information is intended to improve monitoring, evaluation, and updating of maritime activities in these areas, and because it is applicable throughout the Colombian territory, it facilitates decision-making by several national governmental agencies.
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Usman, Muhammad, and Georg Frey. "Multi-Objective Techno-Economic Optimization of Design Parameters for Residential Buildings in Different Climate Zones." Sustainability 14, no. 1 (2021): 65. http://dx.doi.org/10.3390/su14010065.
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The comprehensive approach for a building envelope design involves building performance simulations, which are time-consuming and require knowledge of complicated processes. In addition, climate variation makes the selection of these parameters more complex. The paper aims to establish guidelines for determining a single-family household’s unique optimal passive design in various climate zones worldwide. For this purpose, a bi-objective optimization is performed for twenty-four locations in twenty climates by coupling TRNSYS and a non-dominated sorting genetic algorithm (NSGA-III) using the Python program. The optimization process generates Pareto fronts of thermal load and investment cost to identify the optimum design options for the insulation level of the envelope, window aperture for passive cooling, window-to-wall ratio (WWR), shading fraction, radiation-based shading control, and building orientation. The goal is to find a feasible trade-off between thermal energy demand and the cost of thermal insulation. This is achieved using multi-criteria decision making (MCDM) through criteria importance using intercriteria correlation (CRITIC) and the technique for order preference by similarity to ideal solution (TOPSIS). The results demonstrate that an optimal envelope design remarkably improves the thermal load compared to the base case of previous envelope design practices. However, the weather conditions strongly influence the design parameters. The research findings set a benchmark for energy-efficient household envelopes in the investigated climates. The optimal solution sets also provide a criterion for selecting the ranges of envelope design parameters according to the space heating and cooling demands of the climate zone.
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Maniah, Khalid, Islam Nour, Atif Hanif, et al. "Application of the Human Viral Surrogate Pepper Mild Mottle Virus for Wastewater Fecal Pollution Management." Water 14, no. 24 (2022): 4033. http://dx.doi.org/10.3390/w14244033.
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Global water scarcity has led to significant dependence on reclaimed or recycled water for potable uses. Effluents arising from human and animal gut microbiomes highly influence water quality. Wastewater pollution is, therefore, frequently monitored using bacterial indicators (BI). However, threats to public health arise from the frequent incidence of wastewater-mediated viral infections–undetected by BI. Moreover, the enteric viromes contaminating wastewater are characterized by high abundance, genetic diversity and persistence in various water environments. Furthermore, humans usually suffer a minimum of a single acute diarrheal episode over their lifetime arising from extraneously acquired enteric microbiomes. A wide range of management methods are employed—in particular, microbial source tracking (MST) approaches to confront infections arising from exposure to contaminated wastewater. This review elaborates the viral contamination of treated wastewater and associated public health issues. Latterly, we discuss the various management strategies of wastewater pollution using conventional fecal indicators, viral indicators and human viral surrogates, with particular interest in the pepper mild mottle virus (PMMoV). Globally, PMMoV has been detected in rivers, aquifers, irrigation systems, and coastal and marine waters at high prevalence rates and concentrations greater than 105 genome copies per liter (gc/L). PMMoV was also found in almost all untreated wastewater environments. PMMoV concentrations in wastewater vary from 103 to 107 gc/L. These values are more than the maximum recorded viral indicator concentrations in wastewater for other proposed indicators. Limited variability in the daily concentrations of PMMoV in fecal wastewater has been studied, with an estimated average concentration of 105 gc/L with insignificant seasonal variability. The information summarized in this article offers fundamental knowledge for decision making in terms of defining the suitability criteria of candidate fecal indicators, risk assessment application and efficient wastewater management.
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Adambekova, A. A., M. M. Mukan, B. U. Turebekova, and R. A. Salimbayeva. "Regional resource provision map: methodology and key approaches." Bulletin of "Turan" University, no. 2 (June 30, 2024): 124–38. http://dx.doi.org/10.46914/1562-2959-2024-1-2-124-138.
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The achievement of sustainable development goals with the help of the implementation of a systematic approach to managing the resource potential of regions through sustainable development goals is one of the actual objectives in regional management. Mapping is known as an approach, which allows combining several data sources with different scaling. This study aims to develop regional resource provision map for creating sustainable development conditions. Multidisciplinary research is a valuable source of this research that allows to unit ESG criteria and their regional commitment through cartographic science tools. The methodology is presented in the form of a sequence of actions to draw up a resource supply map. Using the presented map of Western Kazakhstani region confirms the validity of the scientific and applied methodology. The research outcomes contain proven arguments for the further research based on the issues of constructing integrated resource provision maps for the Kazakhstani regions. Key cartography approaches make it possible to form recommendations for similar maps use in terms of decision-making based on interregional interaction, taking into account resource potential, consisting of natural, labor, financial, and infrastructural capabilities of the regions and environmental risk assessments. Developed recommendations were tested with the help of Microsoft Power BI and SuperMap (laboratory of “Geoinformation Cartography” of Kazakh National University named after al-Farabi Kazakh).
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Lanino, Luca, Somedeb Ball, Jan Philipp Bewersdorf, et al. "Data-Driven Harmonization of 2022 Who and ICC Classifications of Myelodysplastic Syndromes/Neoplasms (MDS): A Study By the International Consortium for MDS (icMDS)." Blood 142, Supplement 1 (2023): 998. http://dx.doi.org/10.1182/blood-2023-186580.
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Background. The inclusion of gene mutations and chromosomal abnormalities in the 2022 WHO and ICC Classifications of MDS has enhanced diagnostic precision and is expected to improve clinical decision-making process. Although these two systems share similarities, clinically relevant discrepancies still exist and potentially cause inconsistency in their adoption in a clinical setting. In this study on behalf of the International Consortium for MDS (icMDS), we adopted a data-driven approach to provide a harmonization roadmap between the 2022 WHO and ICC classification for MDS. A modified Delphi Process consensus approach is currently ongoing among icMDS experts to finalize a harmonized MDS classification scheme. Methods. We analyzed retrospective international cohorts of patients with a diagnosis of MDS (n=7017) and AML (n=1002) according to WHO 2016 criteria. Hierarchical Dirichlet Processes were applied to define clusters capturing broad dependencies among all gene mutations and cytogenetic abnormalities. To investigate the features of importance and their impact on the clustering process, we employed the SHapley Additive exPlanations approach (SHAP). This allowed to define harmonized labels for each clinical entity. The clinical relevance of the unsupervised clustering was assessed through the analysis of phenotypic features and outcomes among each group. ( Blood 2022;140: 9828-9830) Results. Patients' characteristics are summarized in Table 1. We identified 9 clusters, defined by specific genomic features. The cluster of highest hierarchical importance was characterized by biallelic inactivation of TP53 (biTP53). According to SHAP analysis, bi TP53 was defined as 2 or more TP53 mutations, or 1 mutation with copy number loss or cnLOH. Most patients assigned to bi TP53 cluster had TP53 VAF>10% (77.9%) and complex karyotype (70.1%). Assignment to bi TP53 cluster was irrespective of blast count. Patients with monoallelic TP53 mutation segregated into other clusters. Hierarchically, the second cluster included patients with del(5q). SHAP analysis highlighted 5q deletion alone, or with one other chromosomal abnormality other than -7, and absence of bi TP53, as the most relevant features. Most of these patients had blast counts <5% (88.1%). The third distinct cluster included patients with SF3B1 mutations (in the absence of concurrent del(7q), abn3q26.2, complex karyotype or RUNX1 mutation). Most patients with MDS and SF3B1 mutation had <5% blasts (94.2%). Common co-mutated variants in the SF3B1 cluster included mutant DNMT3A (25.2%) and TET2 (38.3%). Morphologically defined MDS cases (i.e., not meeting criteria for bi TP53, del(5q) or SF3B1) were preferentially assigned to the following additional clusters: SF3B1 and concurrent higher-risk mutations (e.g., RUNX1 and ASXL1); SRSF2 and concomitant TET2 mutations; U2AF1 mutations with del(20q), del(7q) or -7; SRSF2 with TET2 mutations and co-mutational patterns including RUNX1 and ASXL1; and AML-like genomic signatures. Our analyses suggest that morphologically defined MDS is characterized by a large heterogeneity in terms of mutation profiles, not entirely captured by the presence of unilineage versus multilineage dysplasia, percentage of bone marrow blasts, and presence of hypocellularity and fibrosis. To better investigate the continuum between high risk MDS (i.e., patients with ≥10% blasts) and AML, an exploratory comparison was made using a cohort of AML (defined according to WHO 2016) patients analyzed using the same statistical methods. Only a partial overlap in genetic signatures was observed between MDS with ≥10% blasts and AML. However, similarities were observed between the AML-like MDS clusters (characterized by mutant NPM1, bZIP CEBPA, and Core Binding Factor abnormalities) and AML clusters defined by the same genetic signature, thus supporting the classification of these entities as AML, irrespective of blast count. Conclusion. Our study demonstrated the utility of a data-driven approach based on advanced statistical methods to generate a harmonized classification for MDS. Table 2 shows a provisional, hierarchical classification algorithm. Further refinement of entity labels and classification criteria is the subject of the ongoing modified Delphi Process consensus approach among icMDS experts.
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Naderi, Mobin, Diane Palmer, Matthew J. Smith, et al. "Techno-Economic Planning of a Fully Renewable Energy-Based Autonomous Microgrid with Both Single and Hybrid Energy Storage Systems." Energies 17, no. 4 (2024): 788. http://dx.doi.org/10.3390/en17040788.
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This paper presents both the techno-economic planning and a comprehensive sensitivity analysis of an off-grid fully renewable energy-based microgrid (MG) intended to be used as an electric vehicle (EV) charging station. Different possible plans are compared using technical, economic, and techno-economic characteristics for different numbers of wind turbines and solar panels, and both single and hybrid energy storage systems (ESSs) composed of new Li-ion, second-life Li-ion, and new lead–acid batteries. A modified cost of energy (MCOE) index including EVs’ unmet energy penalties and present values of ESSs is proposed, which can combine both important technical and economic criteria together to enable a techno-economic decision to be made. Bi-objective and multi-objective decision-making are provided using the MCOE, total met load, and total costs in which different plans are introduced as the best plans from different aspects. The number of wind turbines and solar panels required for the case study is obtained with respect to the ESS capacity using weather data and assuming EV demand according to the EV population data, which can be generalized to other case studies according to the presented modelling. Through studies on hybrid-ESS-supported MGs, the impact of two different global energy management systems (EMSs) on techno-economic characteristics is investigated, including a power-sharing-based and a priority-based EMS. Single Li-ion battery ESSs in both forms, new and second-life, show the best plans according to the MCOE and total met load; however, the second-life Li-ion shows lower total costs. The hybrid ESSs of both the new and second-life Li-ion battery ESSs show the advantages of both the new and second-life types, i.e., deeper depths of discharge and cheaper plans.
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Roquemore, Joyce, and Leslie Kian. "MD Anderson Cancer Center Ongoing Professional Practice Evaluation (OPPE): We can see clearly now." Journal of Clinical Oncology 30, no. 34_suppl (2012): 277. http://dx.doi.org/10.1200/jco.2012.30.34_suppl.277.
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277 Background: Reviewing clinical practice by provider has been an ongoing process for quite some time. The metrics reviewed and how the review was conducted were not clearly defined and, as a result, has varied widely between organizations. Recredentialing or some self-imposed process were the main drivers until The Joint Commission required a more regular review of the clinicians’ practice in a hospital and clinics setting. MD Anderson embraced the requirement as an opportunity to improve its clinical review process and create a more robust infrastructure. Methods: Organizational and technological support was critical for success. The OPPE effort is led by the V.P. for Medical Affairs. A new faculty role, Quality Officer (QO), was established in each clinical department. The Office of Performance Improvement (OPI) was enlisted for data support and provided educational resources to the QOs. The Information Services staff maintains the technology to support the project long term. The Medical Staff Office has administrative responsibility for the program. Results: Two bi-annual OPPE reviews for credentialed providers (physicians and mid-levels) have been completed under the new process. Electronic display and processing has eliminated volumes of paper needed to track OPPE for 1,000+ faculty. External audit requirements are easily supported. Early adopters of the new OPPE tools have been complimentary of the support provided by display of values in control chart versus table formats. The application of statistical process control rules help QOs understand variation in performance among faculty and aid in identifying statistically valid quality issues. Conclusions: MD Anderson leveraged the OPPE requirements to make organizational changes and create processes that enhance review of clinical practice with electronic tools that improve decision making and analytical capabilities. The implementation of clinical quality metrics has made it clearer to see how each practice is doing based on criteria they defined, as well as create a path towards developing oncology performance metrics that can be considered nationally.
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Schreiner, James. "Foreword by Guest Editor LTC James H. Schreiner, PhD, PMP, CPEM." Industrial and Systems Engineering Review 8, no. 1 (2021): 1. http://dx.doi.org/10.37266/iser.2020v8i1.pp1.
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FOREWORD
 This special issue of the Industrial and Systems Engineering Review highlights top papers from the 2020 annual General Donald R. Keith memorial capstone conference held at the United States Military Academy in West Point, NY. The conference was certainly a first of its kind virtual conference including asynchronous delivery of paper presentations followed by synchronous question and answer sessions with evaluation panels. Following a careful review of 63 total submissions, eleven were selected for publication in this journal. Unique to this year’s special edition is the mixed selection of seven project team capstone papers, and four honors research papers. Each paper incorporated features of systems or industrial engineering and presented detailed and reflective analysis on the topic. Although there are many elements which cut across the works, three general bodies of knowledge emerged in the papers including: systems engineering and decision analysis, systems design, modeling and simulation, and system dynamics.
 Systems Engineering and Decision Analysis topics included three unique contributions. Recognized as ‘best paper’ at the 2020 virtual conference, the work of Robinson et al. designed a multi-year predictive cost engineering model enabled through an MS O365 Power BI decision support interface to support U.S. Army Corps of Engineer (USACE) inland waterway national investment strategies. Schloo and Mittal’s work presents research in testing and evaluation of the Engagement Skills Trainer (EST) 2000 towards improving real-world soldier performance. Gerlica et al. employs a robust and scalable K-means clustering methodology to improve decision making in defensive shift schemes for Air Force Baseball outfield personnel.
 Systems Design works included three unique contributions. Binney et al. worked to design evaluation criteria for military occupational specialties associated with open-source intelligence (OSINT) analysts for the Army’s OSINT Office. Hales et al. interdisciplinary work aided in the design of search and identification systems to be incorporated on autonomous robotics to enable survivability improvements for the Army’s chemical, biological, radiological, nuclear, and explosives (CBRNE) units. Burke and Connell evaluated and designed a performance measurement-based assessment methodology for U.S. Pacific Command’s Key Leader Engagement process.
 System modeling and simulation included three unique contributions: Arderi et al. simulated and assessed how the Hyper-Enabled Operator (HEO) project improves situational awareness for U.S. Special Forces using the Infantry Warrior Simulation (IWARS). Blanks et al. employed a VBA module and Xpress software for a scheduling optimization model for enhancement of final exam scheduling at the U.S. Air Force Academy. Kelley and Mittal utilized a Batch Run Analysis and Simulation Studio (BRASS) program to batch multiple iterations of IWARS scenarios to study the integration of autonomous systems alongside military units.
 Finally, two unique contributions utilizing system dynamics (SD) modeling is presented: Dixon and Krueger developed a Vensim SD model to examine how policy recommendations across Central America could restrict gang activities while positively promoting women’s involvement in society. Cromer et al. utilized systems design approaches and a K-means clustering machine learning techniques to develop SD models in support of the U.S. Africa Command and Defense Threat Reduction Agency to examine the interdependence of threats across the Horn of Africa.
 Thank you and congratulations to the 2020 undergraduate scholars and all authors who provided meaningful contributions through steadfast intellectual efforts in their fields of study! Well done!
 
 LTC James H. Schreiner, PhD, PMP, CPEM
 Program Director, Systems and Decision Sciences (SDS)
 Department of Systems Engineering
 United States Military Academy
 Mahan Hall, Bldg 752, Room 423
 West Point, NY 10996, USA
 james.schreiner@westpoint.edu
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Benidir, Tarik, Zaeem M. Lone, Andrew Wood, et al. "The impact of focal therapy tumor boards, including prostate magnetic resonance imaging overreads, in refining the selection candidacy for focal therapy patients: A prospective study." Journal of Clinical Oncology 41, no. 6_suppl (2023): 324. http://dx.doi.org/10.1200/jco.2023.41.6_suppl.324.
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324 Background: Focal therapy (FT) for prostate cancer is increasingly recognized as an adequate therapeutic option in well selected men. Nevertheless, in-field and out-of-field recurrences at one year from treatment are notable. A multidisciplinary tumor board geared towards improving focal therapy (FTTB) patient selection is a novel concept which has not been explored. The impact of conducting prostate MRI-overreads during FTTB may also help refine FT candidacy and potentially reduce failure rates. We aimed to explore the value of a dedicated FTTB. Therein, we also evaluated the impact of prostate MRI overreads and its overall influence on FT candidacy. Methods: Single center, prospective study, incorporating a multidisciplinary bi-weekly FTTB (2021-2022) on patients being considered for FT. All prostate MRIs were re-reviewed by a single GU radiologist with >10 years prostate MR imaging experience. Outside pathology, when requested, was also re-reviewed. The impact of such tumor boards, and specifically MRI overreads on patient candidacy are presented. Results: Forty-five patients were presented at our FTTB over the span of one year. Patient demographics are presented. Thirty-nine patients were treatment naïve while six had prior radiation +/- ADT. MRI overread was performed on all treatment naïve patients (39/45, 87%) while pathology overreads on 11/45 (24.4%) Four patients were excluded upfront due to not meeting safety criteria for FT (Urolift device (n=1), J-pouch (n=1), suspicions of metastasis at time of consideration (n=2). Among those with MRI overreads 44.7% (17/39) were found to have findings that negatively impacted eligibility for FT like lesion crossing anteriorly to the urethra occurred in (9/17), multifocal disease (8/17), discordance of lesion on prior MRI (3/17) and lesion abutting rectal wall (1/17). Pathology re-review changed management for 3/11 patients with 2/3 being downgraded to GG1 disease and opting for active surveillance. Following multidisciplinary tumor boards, twelve patients (26.7%) were deemed candidates for FT. Conclusions: FTTB increases the selection scrutiny for FT candidates. MRI overreads are a meaningful part of FT tumor boards and can change candidacy based on new or meaningful findings. FTTB with prostate MR overreads should be considered as part of the selection process in FT decision making. [Table: see text]
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Junazli, N. I., D. Kamaruddin, S. S. Sabu, et al. "Factors Associated With an Abnormal Mammogram Finding in Women Undergoing Screening in Kuala Lumpur, Malaysia." Journal of Global Oncology 4, Supplement 2 (2018): 55s. http://dx.doi.org/10.1200/jgo.18.47500.
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Background: Breast cancer is the most common cancer among Malaysian women, with a rising incidence from 16.5% in 2006%–17.7% in 2011. One of the key strategies in breast cancer control is early screening; of which mammography is a highly accurate tool, having been shown to reduce the number of mortality rates due to breast cancer up to 30%. However, mammography is often not as widely available in Malaysia; and in many local settings, healthcare providers have to limit screenings to only particular groups such as those with abnormal clinical breast examinations due to limited resources. Knowledge of other predictive factors may assist in further decision-making to prioritize patients for screening mammography in a low-resource setting. Aim: This study aimed to determine such predictive factors for abnormal mammogram findings among women who underwent mammography examination at a center in Kuala Lumpur, Malaysia. Methods: This was a cross-sectional study of women (n = 5491) who underwent a three-dimensional tomosynthesis mammography procedure at the Cancer and Health Screening Clinic, National Cancer Society of Malaysia (NCSM) in Kuala Lumpur, from Jan 2016 until Dec 2017 (2 years). Patients were surveyed on: i) age, ii) ethnicity, iii) family history of breast or any cancers, if any, iv) reproductive history (age of menarche, age of first delivery, age of menopause); and v) history of postmenopausal estrogen and hormone replacement therapy (HRT). Bivariate analysis was conducted by using χ2 tests in determining associations between variables and a multiple logistic regression model built to identify factors which were predictive of an abnormal mammogram finding (BI-RADS 4 & 5). Results: From the bivariate analysis; nulliparous status ( P = 0.02), a family history of breast cancer ( P = 0.04), and a history of postmenopausal hormone replacement therapy (HRT) ( P = 0.01) were determined to significantly associated with an abnormal mammogram finding. There were also significant ethnic differences between women who had abnormal mammogram findings; with Chinese women having highest odds of this (OR:3.22; 95% CI 1.86-5.74). Women within the age group of 45-54 (OR:1.84, 95% CI 1.19-3.12), a family history of breast cancer (OR 2.03, 95% CI 1.31-3.27) or any cancer (OR 1.56, 95% CI 1.06-2.94), age of menopause (OR 2.86; 95% CI 1.43-4.02) and age of first delivery above 30 (OR 1.73, 95% CI 1.26-3.45) were significantly associated with abnormal mammogram findings. Conclusion: Factors which predict abnormal mammogram findings in a Malaysian setting can be used as baseline evidence to formulate criteria which can be used to carry out targeted screening programs or even as cutoff criteria for focusing screening resources in resource-limited settings. This data may be of benefit in aiding healthcare providers in provisioning of services at a macro level as well as for frontline healthcare personnel in helping them profile women who should be focused on to be screened for the disease.
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RAZAVI HAJIAGHA, Seyed Hossein, Meisam SHAHBAZI, Hannan AMOOZAD MAHDIRAJI, and Hossein PANAHIAN. "A BI-OBJECTIVE SCORE-VARIANCE BASED LINEAR ASSIGNMENT METHOD FOR GROUP DECISION MAKING WITH HESITANT FUZZY LINGUISTIC TERM SETS." Technological and Economic Development of Economy 24, no. 3 (2018): 1125–48. http://dx.doi.org/10.3846/20294913.2016.1275878.
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Decision makers usually prefer to express their preferences by linguistic variables. Classic fuzzy sets allowed expressing these preferences using a single linguistic value. Considering inevitable hesitancy of decision makers, hesitant fuzzy linguistic term sets allowed them to express individual evaluation using several linguistic values. Therefore, these sets improve the ability of humans to determine believes using their own language. Considering this feature, in this paper a method upon linear assignment method is proposed to solve group decision making problems using this kind of information, when criteria weights are known or unknown. The performance of the proposed method is illustrated in a numerical example and the results are compared with other methods to delineate the models efficiency. Following a logical and well-known mathematical logic along with simplicity of execution are the main advantages of the proposed method.
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Wengert, G. J., F. Pipan, J. Almohanna, et al. "Impact of the Kaiser score on clinical decision-making in BI-RADS 4 mammographic calcifications examined with breast MRI." European Radiology 30, no. 3 (2019): 1451–59. http://dx.doi.org/10.1007/s00330-019-06444-w.
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Abstract Objectives To investigate whether the application of the Kaiser score for breast magnetic resonance imaging (MRI) might downgrade breast lesions that present as mammographic calcifications and avoid unnecessary breast biopsies Methods This IRB-approved, retrospective, cross-sectional, single-center study included 167 consecutive patients with suspicious mammographic calcifications and histopathologically verified results. These patients underwent a pre-interventional breast MRI exam for further diagnostic assessment before vacuum-assisted stereotactic-guided biopsy (95 malignant and 72 benign lesions). Two breast radiologists with different levels of experience independently read all examinations using the Kaiser score, a machine learning–derived clinical decision-making tool that provides probabilities of malignancy by a formalized combination of diagnostic criteria. Diagnostic performance was assessed by receiver operating characteristics (ROC) analysis and inter-reader agreement by the calculation of Cohen’s kappa coefficients. Results Application of the Kaiser score revealed a large area under the ROC curve (0.859–0.889). Rule-out criteria, with high sensitivity, were applied to mass and non-mass lesions alike. The rate of potentially avoidable breast biopsies ranged between 58.3 and 65.3%, with the lowest rate observed with the least experienced reader. Conclusions Applying the Kaiser score to breast MRI allows stratifying the risk of breast cancer in lesions that present as suspicious calcifications on mammography and may thus avoid unnecessary breast biopsies. Key Points • The Kaiser score is a helpful clinical decision tool for distinguishing malignant from benign breast lesions that present as calcifications on mammography. • Application of the Kaiser score may obviate 58.3–65.3% of unnecessary stereotactic biopsies of suspicious calcifications. • High Kaiser scores predict breast cancer with high specificity, aiding clinical decision-making with regard to re-biopsy in case of negative results.
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Chanu, Pascal, Sandhya Balasubramanian, Divya Samineni, et al. "Multiple Myeloma Disease Model to Predict PFS Outcomes across Lines of Therapy, an Analysis Based on Commpass Observational Study." Blood 142, Supplement 1 (2023): 4711. http://dx.doi.org/10.1182/blood-2023-187678.
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Introduction Multiple Myeloma (MM) remains incurable despite advances in treatment. Model-based approaches based on tumor dynamics have successfully been used to optimize drug-development in oncology. Model-derived tumor dynamics also called tumor growth inhibition (TGI) metrics capture the treatment effect as tumor biomarkers and are predictive of the overall survival (OS) benefit, based on TGI-OS models, this mathematical framework has been shown to be drug-independent in most cases. M-protein is measured longitudinally during clinical trials, it reflects tumor burden and is one of the criteria used to assess clinical response according to the International Myeloma Working Group Uniform Response Criteria. A drug-independent link between M-protein dynamic metrics and OS was previously established and confirmed for refractory MM patients. The primary endpoint in MM clinical trials is usually progression free survival (PFS). New immunotherapies are currently in development and there is a need for early and robust drug development decision making. Therefore there is strong interest to expand this modeling framework to leverage M-protein dynamics to predict PFS outcomes. Methods CoMMpass (https://www.themmrf.org/finding-a-cure/personalized-treatment-approaches/) is a prospective, longitudinal, observational study in newly diagnosed symptomatic patients with multiple myeloma. It represents a unique source of clinical data to investigate the link between M-protein dynamic and survival outcomes across lines of therapy and treatments. Individual M-protein and PFS Data from 804 patients within the CoMMpass study were used. Given patients are enrolled at their initial treatment regimen and observed over 8 years, they can receive several lines of therapy. Due to the characteristics of the modeling framework, allowing to analyze only one line of therapy for each patient, the latest line of therapy for each subject was selected for which they had at least 2 M-protein assessments available. M-protein dynamic metrics (M-protein ratio to baseline at different time points, time to growth, growth rate, shrinkage rate) derived using an empirical bi-exponential model, 42 prognostic factors and potential treatment effects were first tested in a univariate analysis using a Cox proportional hazards regression model. Statistically significant covariates in the univariate analysis (p<0.05) were included in a full multivariate parametric distribution survival model. A backward deletion stepwise procedure was performed (p<0.01) to retain covariates in the final PFS model. Model qualification was performed using posterior predictive checks based on Kaplan Meier plots. Results Among 804 patients, 490 patients received only 1 line of therapy (LoT), 157 received 2 LoTs, 93 received 3 LoTs, 31 received 4 LoTs,18 received 5 LoTs, 10 received 6 LoTs, finally 7 patients got up to 7 to 11 LoTs. PFS distribution followed a log-normal distribution. Table 1 shows the parameter estimates of the PFS model. Among all tested M-protein dynamic metrics and prognostic factors, the logarithmic value of the growth rate (log(KG)), in red in Table 1, was found to be the best predictor of PFS. Patients with slower growth rate, who received a transplantation, with higher albumin and lower lactate dehydrogenase values had longer PFS. Those findings are consistent with previous modeling works conducted in MM. Also, the PFS model was confirmed to be drug-independent. As shown in Figure 1, prediction intervals derived from model-based simulations well captured observed PFS data for each line of treatment although the line of therapy is not included as a predictor in the PFS model. Similar results were obtained across treatments. In a next step, external validations assessing the model performance to reproduce Phase 3 clinical trial outcomes will be performed. Conclusions A model linking M-protein dynamic to PFS in MM was developed across a wide range of lines of therapy and treatments. The model can support drug development in MM: enabling early predictions of clinical trial outcomes and inform study design e.g. by simulating the probability of success of upcoming pivotal clinical trials.