Dissertations / Theses on the topic 'Multi-criteria Decision Making (MCDM) techniques'

Information technology (IT) investment decision makers are required to process large volumes of complex data. An existing body of knowledge relevant to IT portfolio management (PfM), decision analysis, visual comprehension of large volumes of information, and IT investment decision making suggest Multi-Criteria Decision Making (MCDM) and hypervariate display techniques can reduce cognitive load and improve decision confidence in IT PfM decisions. This dissertation investigates improving the decision confidence by reducing cognitive burden of the decision maker through greater comprehension of relevant decision information. Decision makers from across the federal government were presented with actual federal IT portfolio project lifecycle costs and durations using hypervariate displays to better comprehend IT portfolio information more quickly and make more confident decisions. Other information economics attributes were randomized for IT portfolio projects to generate Balanced Scorecard (BSC) values to support MCDM decision aids focused on IT investment alignment with specific business objectives and constraints. Both quantitative and qualitative measures of participant comprehension, confidence, and efficiency were measured to assess hypervariate display treatment and then MCDM decision aid treatment effectiveness. Morae Recorder Autopilot guided participants through scenario tasks and collected study data without researcher intervention for analysis using Morae Manager. Results showed improved comprehension and decision confidence using hypervariate displays of federal IT portfolio information over the standard displays. Both quantitative and qualitative data showed significant differences in accomplishment of assigned IT portfolio management tasks and increased confidence in decisions. MCDM techniques, incorporating IT BSC, Monte Carlo simulation, and optimization algorithms to provide cost, value, and risk optimized portfolios improved decision making efficiency. Participants did not find improved quality and reduced uncertainty from optimized IT portfolio information. However, on average participants were satisfied and confident with the portfolio optimizations. Improved and efficient methods of delivering and visualizing IT portfolio information can reduce decision maker cognitive load, improve comprehension efficiency, and improve decision making confidence. Study results contribute to knowledge in the area of comprehension and decision making cognitive processes, and demonstrate important linkages between Human-Computer Interaction (HCI) and Decision Support Systems (DSS) to support IT PfM decision making.

Cette thèse vise à contribuer à la compréhension des cycle de vie d’une opération humanitaire (HOLC). Gestion de la chaîne d'approvisionnement humanitaire (HSCM) dans un contexte de mise en perspective et dans l’objectif de proposer un modèle décisionnel qui s'applique aux phases de HOLC lors d’une situation réelle. Cela inclut la mise en oeuvre du modèle proposé pour concevoir et développer un outil d'aide à la décision afin d'améliorer les performances de la logistique humanitaire tant dans les opérations de secours nationaux qu’internationaux.Cette recherche est divisée en trois phases. La première partie vise à présenter le sens de l'étude ; la zone de recherche prise en compte pour la gestion de la chaîne d'approvisionnement (SCM) doit être clairement définie. La première phase consiste à clarifier et définir le HSCM HL, la gestion de la chaîne d'approvisionnement commerciale (CSCM) et le SCM, ainsi que la relation entre ces différents éléments. La gestion du cycle de vie du projet (PLCM) et les différentes approches sont également présentés. La compréhension de la différence entre la gestion du cycle de vie du projet (PLM) et la PLCM est également nécessaire, cela ne peut être abordé dans la phase de cycle de vie de l'opération humanitaire. De plus, les modèles Multiple-Criteria Decision Making (MCDM) et l’aide à la décision concernant le HL sont analysés pour établir le fossé existant en matière de recherche. Les approches MCDM qui mettent en oeuvre le système d'aide à la décision (DSS) et la manière dont le MAS a été utilisé dans le contexte HSCM sont étudiées.La deuxième phase consiste en la proposition d’un modèle décisionnel fondé sur l’approche MCDM à l'appui de la décision du décideur avant qu'il/elle prenne des mesures. Ce modèle prévoit le classement des alternatives concernant l'entrepôt, le fournisseur et le transport au cours des phases de HOLC. Le modèle décisionnel proposé est réalisée en 3 scénarios. I. La décision en 4phases HOLC – opération de secours internationale de la Croix-Rouge Française (CRF). II. La décision en3phases HOLC – opération nationale dela Croix-Rouge thaïlandaise (TRC). III. La décision au niveau de la phase de réponse HOLC – opération internationale du TRC dans quatre pays. Dans cette phase, le scénario I et II sont réalisés étape par étape au travers de calculs numériques et formules mathématiques. Le scénario III sera présenté dans la troisième phase. Pour établir trois scénarios, les données internes recueillies lors des entretiens avec le chef de la logistique de la Croix-Rouge Française, et le vice-président de la fondation de la Coix-Rouge thaïlandaise, seront utilisées. Les données externes proviennent de chercheurs qui sont des experts dans le domaine HL ou le champ du HSCM, de la littérature, et de sources issues des organismes humanitaires (documents d’ateliers, rapports, informations publiées sur leurs sites officiels).Dans la troisième phase, une application Internet multi-critères (decision support system MCDSS WB) mettant en oeuvre le modèle proposé est élaborée. Afin d'atteindre une décision appropriée en temps réel, le WB-MCDSS est développé sur la base d’un protocole client-serveur et est simple à utiliser. Le dernier mais non le moindre ; une application de validation du modèle est réalisée à l'aide de l'approche de l'analyse de sensibilité
This thesis aims to contribute to the understanding of HOLC in context of the HSCM and to propose a decision model which applies to the phases of HOLC the decision making regarding a real situation . This include the implementation of the proposed model to design and develop a decision support tool in order to improve the performance of humanitarian logistics in both national and international relief operations.This research is divided into three phases; the first phase is to clarify and define HL among HSCM, commercial supply chain management (CSCM) and SCM and their relationship. Project Life Cycle Management (PLCM) approaches are also presented. The difference between project life cycle management (PLM) and PLCM is also required to distinguish a clear understanding which can be addressed in the phase of humanitarian operation life cycle. Additionally, the literature of Multiple-Criteria Decision Making (MCDM) models and existing decision aid system for HL are analyzed to establish the research gap. The MCDM approaches which implement the decision support system (DSS) and lastly how DSS has been used in the HSCM context.The second phase is to propose a decision model based on MCDM approaches to support the decision of the decision maker before he/she takes action. This model provides the ranking alternatives to warehouse, supplier and transportation over the phases of HOLC. The proposed decision model is conducted in 3 scenarios; I. The decision in 4-phase HOLC, international relief operation of French Red Cross (FRC). II. The decision on 3-phase HOLC, national operation by the Thai Red Cross (TRC). III. The decision on response phase HOLC, international operation by the FRC in four countries. In this phase, the scenario I and II are performed step by step though numerical calculation and mathematical formulas. The scenario III will be presented in the third phase.In the third phase, an application of web-based multi-criteria decision support system (WB-MCDSS) which implement the proposed model is developed. The web-based multi-criteria decision support system is developed based on the integration of analytical hierarchy process (AHP) and TOPSIS approaches. In order to achieve an appropriate decision in a real time response, the WB-MCDSS is developed based on server-client protocol and is simple to operate. Last but not least, a validation application of the model is performed using the sensitivity analysis approach

Significant investments are being made in the construction sector in order to raise the quality of the buildings and make them more sustainable and energy-efficient. The key aspect of these investments should be the purposeful optimization of the possible renovation and construction measures. However, this important matter usually is being pushed aside in favor of construction price and/or quality. Nevertheless, there are plenty of criteria that play a major role in building sustainable development. The main purpose of this study is to present a tool that combines multi-criteria decision making (MCDM) methods and building certification systems in order to make weighted decisions in complicated construction tasks. For this, a decision making model was developed with a focus on sustainability, buildings’ life cycle, MCDM methods, and building certification. The first section of this thesis, the introduction, discusses the importance of the investigated area, and the main objectives, tasks, and structure of the thesis. A literature review is presented in Section 2 – Theory. The main works in the area of sustainability, LCA, building certification, and MCDM are collected to show their role and importance and how they interact in the construction industry. Section 3 presents and discusses the main ideas and instructions of the proposed decision making model. Section 4 (Methodology) introduces the main existing and proposed techniques that I have used to implement the study. Sections 5 and 6 are the case studies, which demonstrate how the proposed methods can be used in practice. Final conclusions and recommendations are presented in Section 7.

QC 20170209

Funded by L.E. Lundberg foundation

Background: Despite the competition for achieving lower costs and higher profits, companies are nowadays becoming increasingly aware of the environmental and social impacts of their operations. This is unveiled by growing numbers of sustainable activities in companies’ reports. Companies’ are also under more pressure from authorities and public to increase their business’s environmental performance, a so-called sustainable management process. Besides the pressure for developing environmental responsible activities, companies still remain with their low cost measures. Hence, they are concerned with the issue of identifying suitable measures to assess their sustainability.Supplier selection, processes by which companies evaluate, select and maintain their suppliers, regarded as one of the major operations affecting companies sustainable performance. While supplier selection was often performed in the past using conventional measures, there is an essential need to develop a framework for sustainable supplier selection in supply chain. By doing this research the company can ensure its operations are in line with environmental and social regulations and requirements. Research questions: - What is the important and applicable sustainable supplier evaluation criteria (SSEC) that could be applied? - What Multi criteria decision making method (MCDM) is suitable for evaluating and ranking the suppliers? - How would Renault group in Iran rank the supplier against the sustainable supplier evaluation criteria (SSEC)? - Which Sustainable supplier evaluation criteria (SSEC) could help the lowest ranked supplier to improve its sustainability performance? Purpose: The purpose of this research is to develop important and applicable sustainable supplier evaluation criteria (SSEC) and find out the multi criteria decision making method (MCDM) for ranking the suppliers. Furthermore, it also suggests to the manger to improve the sustainability performance of lower ranked suppliers. Method: The empirical data and the conclusions made from Renault Group in Iran are based on qualitative case study. This thesis is written from a positivistic perspective with a deductive approach. Conclusions: The main conclusions of this study are as below: - Developing a comprehensive list of attributes including the most important and applicable Sustainable Supplier Evaluation Criteria (SSEC) for evaluating sustainability performance of supplier for Renault Company in Iran. - TOPSIS is evaluated MCDM method to rank the suppliers against SSEC in Renault group in Iran. - The suppliers of the company are ranked and the best and the weakest supplier are determined using the TOPSIS. - The lowest ranked supplier is showed which SSEC could improve its performance by applying a parametric analysis.

The decision-making process in highway construction projects identifies and selects the optimal alternative based on the user requirements and evaluation criteria. The current practice of the decision-making process does not consider all construction impacts in an integrated decision-making process. This dissertation developed a multi-criteria evaluation framework to support the decision-making process in highway construction projects. In addition to the construction cost and mobility impacts, reliability, safety, and emission impacts are assessed at different evaluation levels and used as inputs to the decision-making process. Two levels of analysis, referred to as the planning level and operation level, are proposed in this research to provide input to a Multi-Criteria Decision-Making (MCDM) process that considers user prioritization of the assessed criteria. The planning level analysis provides faster and less detailed assessments of the inputs to the MCDM utilizing analytical tools, mainly in a spreadsheet format. The second level of analysis produces more detailed inputs to the MCDM and utilizes a combination of mesoscopic simulation-based dynamic traffic assignment tool, and microscopic simulation tool, combined with other utilities. The outputs generated from the two levels of analysis are used as inputs to a decision-making process based on present worth analysis and the Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Situation) MCDM method and the results are compared.

New Zealand faces a range of food safety hazards. Microbial hazards alone were estimated to cause over 2,000 years of lost healthy life in 2011 (Cressey, 2012) and $62m in medical costs and lost productivity in 2009 (Gadiel & Abelson, 2010). Chemical hazards are thought to be well managed through existing controls (Vannoort & Thomson, 2009) whereas microbial hazards are considered harder to control, primarily due to their ability to reproduce along the food production chain. Microbial hazards are thought to cause the majority of acute foodborne gastroenteritis. This research reviewed food safety literature and official documentation, and conducted 55 interviews, mostly with food safety experts from different stakeholder groups, to examine the food safety decision-making environment in New Zealand. This research explores the concept of the ‘stakeholder’ in the context of food safety decision-making and proposes an inclusive ‘stakeholder’ definition as any group which is able to affect, or be affected by, the decision-making process. Utilising this definition, and guided by interviews, New Zealand stakeholders in food safety decision-making were identified and classified as follows: •Regulators •Public health authorities •Food safety scientists/academics •Consumers •Māori •Food Businesses (further classified as): o Farmers o Processors o Food retailers o Exporters Interviews with stakeholders from these groups highlighted twelve criteria as being relevant to multiple groups during food safety intervention evaluation: •Effectiveness •Financial cost •Market Access •Consumer Perceptions •Ease of Implementation •Quality or Suitability •Quality of Science •Equity of Costs •Equity of Benefits •Workplace Safety •Cultural Impact •Animal Welfare There are a number of different ways to measure or assess performance on these criteria. Some are able to be quantitatively measured, while others may require the use of value judgements. This thesis used the Disability-Adjusted Life Year (DALY) metric for quantifying effectiveness during the testing of different MCDA models. This thesis reviews the MCDA process and the food safety specific MCDA literature. There are different ways of conducting MCDA. In particular, there are a large number of models available for the aggregation phase; the process of converting model inputs, in the form of criteria scores and weights, into model recommendations. This thesis has described and reviewed the main classes of model. The literature review and interview process guided the construction and testing of three classes of MCDA model; the Weighted Sum, Analytic Hierarchy Process (AHP) and PROMETHEE models. These models were selected due to their having different characteristics and degrees of complexity, as well as their popularity in the food safety and Health Technology Assessment (HTA) literature. Models were tested on the problem of selecting the most appropriate intervention to address the historic Campylobacter in poultry problem in New Zealand during the mid-2000s. Experimentation was conducted on these models to explore how different configurations utilise data and produce model outputs. This experimentation included: •Varying the format of input data •Exploring the effects of including/excluding criteria •Methods for sensitivity analysis •Exploring how data inputs and outputs can be elicited and presented using visual tools • Creating and using hybrid MCDA models The results of this testing are a key output of this thesis and provide insight into how such models might be used in food safety decision-making. The conclusions reached throughout this research phase can be classified into one of two broad groups: •Those relating to MCDA as a holistic process/methodology for decision-making •Those relating to the specific models and mathematical procedures for generating numerical inputs and outputs This thesis demonstrates that food-safety decision-making is a true multi-criteria, multi-stakeholder problem. The different stakeholders in food-safety decision-making do not always agree on the value and importance of the attributes used to evaluate competing intervention schemes. MCDA is well suited to cope with such complexity as it provides a structured methodology for the systematic and explicit identification, recording and aggregation of qualitative and quantitative information, gathered from a number of different sources, with the output able to serve as a basis for decision-making. The MCDA models studied in this thesis range from models that are simple and quick to construct and use, to more time consuming models with sophisticated algorithms. The type of model used for MCDA, the way these models are configured and the way inputs are generated or elicited can have a significant impact on the results of an analysis. This thesis has identified a number of key methodological considerations for those looking to employ one of the many available MCDA models. These considerations include: •Whether a model can accommodate the type and format of input data •The desired degree of compensation between criteria (i.e. full, partial or no compensation) •Whether the goal of an analysis is the identification of a ‘best’ option(s), or the facilitation of discussion, and communication of data •The degree of transparency required from a model and whether an easily understood audit trail is desired/required •The desired output of a model (e.g. complete or partial ranking). This thesis has also identified a number of practical considerations when selecting which model to use in food safety decision-making. These include: •The amount of time and energy required of stakeholders in the generation of data inputs (elicitation burden) •The degree of training required for participants •How data inputs are to be elicited and aggregated in different group decision-making environments •The availability of MCDA software for assisting an analysis Considering the above points will assist users in selecting a suitable MCDA model that meets their requirements and constraints. This thesis provides original and practical knowledge to assist groups or individuals looking to employ MCDA in the context of food-safety intervention decision-making. This research could also serve as a guide for those looking to evaluate a different selection of MCDA models.

The real estate selection process might be regarded as a typical Multi-Criteria Decision Making (MCDM) problem. With current literature concentrating predominantly on institutional investment decision making, additional effort should be directed towards studying inexperienced homebuyers who want to buy a property in which to live. In this context, authors have described the decision environment as a complex decision process with restricted access to property data, high financial burdens partially due to the illiquid nature of the investment, the unfamiliarity with the decision task and low transparency in information aggregation. Consequently, this situation could benefit from a more structured approach that assists homebuyers in their actions. In order to guide the decision making process and provide a suitable support mechanism, it is necessary to first structure the problem and extract the required information. A thorough literature review shows that little guidance is available for MCDM problem structuring. Consequently, this research first proposes an MCDM problem-structuring framework to decompose complex problems into smaller parts. Foremost, the application is intended for high-involvement consumer products and services. This framework is derived from MCDM and methodology literature, where the former provides the elements that need to be defined in any MCDM problem situation, and the latter suggests suitable data collection and analysis methods to obtain the information. As a result, the first contribution to existing literature is the introduction of an MCDM problem-structuring framework, which consists of a carefully designed sequential exploratory mixed method procedure. Next, following the proposed structure, the real estate selection problem in Majorca (Spain) is defined. Whilst providing the inherent problem elements and establishing a comprehensive list of evaluation criteria to assess luxury properties, the fieldwork also offers behavioural insights, contributing and supplementing existing real estate research. In particular, major misunderstandings and false assumptions during real estate agent and client interactions are observed, stressing the need to optimise communication and targeting strategies. On the basis of the relevant real estate evaluation criteria, a dataset of alternative houses is created and subsequently rated by prospective luxury-homebuyers. This provides the basis for the third research focus, the construction of a decision support model for real estate selection. In accordance to the problem features and model requirements, the Evidential Reasoning (ER) rule is identified to offer a powerful and transparent evidence aggregation process, with the potential to have a superior performance than other methods in addressing the selection decision. Due to the ER rule’s short history (2013), application studies in general are practically non-existing and unprecedented in the real estate domain. Therefore, the use of a modified ER model can provide the real estate literature with a prescriptive multi-criteria decision support mechanism, whilst simultaneously offering an application study for the MCDM community and other relevant decision analysis domains. In closing, modelling a real problem using the ER rule highlights the method’s advantages and might in turn increase awareness, leading to more applications.

The purpose of this thesis is to investigate to what extent the environmental agenda should be taken into consideration during the project phase of logistics at Northvolt. The objective is to build an optimization model for route planning with a trade-off solution for three criteria; cost, emission and time. An abductive research approach is used. The empirical data was collected quantitatively in accordance with cross-industry standard process for data mining. The results obtained from the optimization model were based on an emission calculation framework and weights assigned to the three selected criteria by five decision makers. These results are compared to an as-is analysis of current and of historical transportations as well as to expected future transportations. These results provide insights and emphasize the importance of agility in decision making aiming at the reduction of emissions. The analysis shows that increased costs do not have a clear correlation with reduced emissions or transportation time. It also demonstrates that transporting goods on sea would increase the transportation time but decrease the emissions. The conclusions of the study are that costs can be reduced by 8%, emissions by 6% and transportation time by 14%, compared to their current operations. Short-term recommendations address the need of being agile in the route planning and analyse each shipment individually where the weights of the criteria should be alternated depending on the status of the project. This requires increased communication with the installation team on site and negotiation of contractual rates from additional ports in Sweden. Long-term recommendations emphasise the importance of developing and using emission key performance indicators to set targets in combination with a strategy in order to steer daily operations.

Nell'elaborato si analizzano aspetti della teoria dei giochi e della multi-criteria decision-making. La riflessione serve a proporre le basi per un nuovo modello di protocollo di routing in ambito Mobile Ad-hoc Networks. Questo prototipo mira a generare una rete che riesca a gestirsi in maniera ottimale grazie ad un'acuta tecnica di clusterizzazione. Allo stesso tempo si propone come obiettivo il risparmio energetico e la partecipazione collaborativa di tutti i componenti.

Today, population census results are not only fundamental for population counting but also for providing various information to many organizations and people who make research and take decisions about human-related issues. However, statistics produced on the basis of administrative divisions in Turkey are inadequate to meet the needs of most In this study, a new census geography is aimed for Turkey using Geographic Information Systems and Multi Criteria Decision Making methodologies. This new census geography is for statistical purposes only, independent from legal boundaries and generated by the concept of small area statistics. New rules and methodologies are created by taking the United States and the United Kingdom systems as models to reach to the main aim, and then they are applied on a case study area, Ç
ankaya District in Ankara. Through this application process, firstly all the collected graphical and nongraphical raw data are geo-referenced and combined in a common geodatabase. Secondly, this geodatabase is used to understand the differentiation of quality of life indicators across the case study area. This pattern is then used to draw the boundaries of small statistical units of the new census geography in terms of the previously defined population sizes. In conclusion, a nationwide standard census geography hierarchy, which ranges between national level at the top and block level at the bottom, is proposed for use in 2010 Population Census and afterwards.

The increased demand for biofuels is expected to put additional strain on the available agricultural resources while at the same time causing environmental degradation. Hence, new energy policies need to be formulated and implemented in order to meet global energy needs while reducing the impact of biofuels farming and production. This research focuses on proving a decision support system which can aid the formulation of policies for the sustainable biofuel production. The system seeks to address policy formulation that requires reconciliation of the qualitative aspects of decision making (such as stakeholder’s viewpoints) with quantitative data, which often may be imprecise. To allow this, based on: Fuzzy logic and Multi Criteria Decision Making (MCDM) in the form of Analytical Hierarchy Process (AHP). Using these concepts, three software functionalities, “Options vs. Fuzzy Criteria Matrix”, “Analytical Hierarchy Process” and “Fuzzy AHP” were developed. These were added within the framework of pre-existing base software, Compendium (developed by the Open University, UK). A number of case study based models have been investigated using the software. These models made use of data from the Philippines and India in order to pinpoint suitable land and crop options for these countries. The models based on AHP and Fuzzy AHP were very successful in identifying suitable crop options for India by capturing both the stakeholder viewpoints and quantitative data. The software functionalities are very effective in scenario planning and selection of policies that would be beneficial in achieving a desired future scenario. The models further revealed that the newly developed software correctly identified many of the important issues in a consistent manner.

Because of the increasing demand for the provision of energy worldwide and the numerous damages caused by a major use of fossil sources, the contribution of renewable energies has been increasing significantly in the global energy mix with the aim at moving towards a more sustainable development. In this context, this work aims at the development of a general methodology for designing PV systems based on ecodesign principles and taking into account simultaneously both techno-economic and environmental considerations. In order to evaluate the environmental performance of PV systems, an environmental assessment technique was used based on Life Cycle Assessment (LCA). The environmental model was successfully coupled with the design stage model of a PV grid-connected system (PVGCS). The PVGCS design model was then developed involving the estimation of solar radiation received in a specific geographic location, the calculation of the annual energy generated from the solar radiation received, the characteristics of the different components and the evaluation of the techno-economic criteria through Energy PayBack Time (EPBT) and PayBack Time (PBT). The performance model was then embedded in an outer multi-objective genetic algorithm optimization loop based on a variant of NSGA-II. A set of Pareto solutions was generated representing the optimal trade-off between the objectives considered in the analysis. A multi-variable statistical method (i.e., Principal Componet Analysis, PCA) was then applied to detect and omit redundant objectives that could be left out of the analysis without disturbing the main features of the solution space. Finally, a decision-making tool based on M-TOPSIS was used to select the alternative that provided a better compromise among all the objective functions that have been investigated. The results showed that while the PV modules based on c-Si have a better performance in energy generation, the environmental aspect is what makes them fall to the last positions. TF PV modules present the best trade-off in all scenarios under consideration. A special attention was paid to recycling process of PV module even if there is not yet enough information currently available for all the technologies evaluated. The main cause of this lack of information is the lifetime of PV modules. The data relative to the recycling processes for m-Si and CdTe PV technologies were introduced in the optimization procedure for ecodesign. By considering energy production and EPBT as optimization criteria into a bi-objective optimization cases, the importance of the benefits of PV modules end-of-life management was confirmed. An economic study of the recycling strategy must be investigated in order to have a more comprehensive view for decision making.

Human hair profiling is an integral part of a forensic investigation but it is one of the most technically difficult subjects in forensic science. This thesis describes the research and development of a novel approach for the rapid identification of unknown human and other related keratin fibres found at a crime scene. The work presented here is developed systematically and considers sample collection, sample preparation, analysis and interpretation of spectral data for the profiling of hair fibres encountered in criminal cases. Spectral comparison of fibres was facilitated with the use of chemometrics methods such as PCA, SIMCA and Fuzzy Clustering, and the less common approach of multi-criteria decision making methodology (MCDM). The aim of the thesis was to investigate the potential of some vibrational spectroscopy techniques for matching and discrimination of single keratin hair fibres in the context of forensic evidence. The first objective (chapter 3) of the thesis was to evaluate the use of Raman and FT-IR micro-spectroscopy techniques for the forensic sampling of hair fibres and to propose the preferred technique for future forensic hair comparisons. The selection of the preferred technique was based on criteria such as spectral quality, ease of use, rapid analysis and universal application to different hair samples. FT-IR micro-spectroscopy was found to be the most appropriate technique for hair analysis because it enabled the rapid collection of spectra from a wide variety of hair fibres. Raman micro-spectroscopy, on the other hand, was hindered with fluorescence problems and did not allow the collection of spectra from pigmented fibres. This objective has therefore shown that FT-IR micro-spectroscopy is the preferable spectroscopic technique for forensic analysis of hair fibres, whilst Raman spectroscopy is the least preferred. The second objective (chapter 3) was to investigate, through a series of experiments, the effect of chemical treatment on the micro-environment of human hair fibres. The effect of bleaching agents on the hair fibres was studied with some detail at different treatment times and the results indicate a significant change in the chemical environment of the secondary structure of the hair fibre along with changes in the C-C backbone structure. One of the most important outcomes of this research was the behaviour of the fÑ-helix during chemical treatment. The hydrogen bonding in the fÑ-helix provides for the stable structure of the fibre and therefore any disruption to the fÑ-helix will inevitably damage the molecular structure of the fibre. The results highlighted the behaviour of the fÑ-helix, which undergoes a significant decrease in content during oxidation, and is partly converted to a random-coil structure, whilst the fÒ-sheet component of the secondary structure remains unaffected. The reported investigations show that the combination of FT-IR and Raman micro-spectroscopy can provide an insight and understanding into the complex chemical properties and reactions within a treated hair fibre. Importantly, this work demonstrates that with the aid of chemometrics, it is possible to investigate simultaneously FT-IR and Raman micro-spectroscopic information from oxidised hair fibres collected from one subject and treated at different times. The discrimination and matching of hair fibres on the basis of treatment has potential forensic applications. The third objective (chapter 4) attempted to expand the forensic application of FT-IR micro-spectroscopy to other keratin fibres. Animal fibres are commonly encountered in crime scenes and it thus becomes important to establish the origin of those fibres. The aim of this work was to establish the forensic applications of FT-IR micro-spectroscopy to animal fibres and to investigate any fundamental molecular differences between these fibres. The results established a discrimination between fibres consisting predominantly of fÑ-helix and those containing mainly a fÒ-sheet structure. More importantly, it was demonstrated through curve-fitting and chemometrics, that each keratin fibre contains a characteristic secondary structure arrangement. The work presented here is the first detailed FT-IR micro-spectroscopic study, utilising chemometrics as well as MCDM methods, for a wide range of keratin fibres, which are commonly, found as forensic evidence. Furthermore, it was demonstrated with the aid of the rank ordering MCDM methods PROMETHEE and GAIA, that it is possible to rank and discriminate keratin fibres according to their molecular characteristics obtained from direct measurements together with information sourced from the literature. The final objective (chapter 5) of the thesis was to propose an alternative method for the discrimination and matching of single scalp human hair fibres through the use of FT-IR micro-spectroscopy and chemometrics. The work successfully demonstrated, through a number of case scenarios, the application of the technique for the identification of variables such as gender and race for an unknown single hair fibre. In addition, it was also illustrated that known hair fibres (from the suspect or victim) can be readily matched to the unknown hair fibres found at the crime scene. This is the first time that a substantial, systematic FT-IR study of forensic hair identification has been presented. The research has shown that it is possible to model and correlate individual¡¦s characteristics with hair properties at molecular level with the use of chemometrics methods. A number of different, important forensic variables of immediate use to police in a crime scene investigation such as gender, race, treatment, black and white hair fibres were investigated. Blind samples were successfully applied both to validate available experimental data and extend the current database of experimental determinations. Protocols were posed for the application of this methodology in the future. The proposed FT-IR methodology presented in this thesis has provided an alternative approach to the characterisation of single scalp human hair fibres. The technique enables the rapid collection of spectra, followed by the objective analytical capabilities of chemometrics to successfully discriminate animal fibres, human hair fibres from different sources, treated from untreated hair fibres, as well as black and white hair fibres, on the basis of their molecular structure. The results can be readily produced and explained in the courts of law. Although the proposed relatively fast FT-IR technique is not aimed at displacing the two slower existing methods of hair analysis, namely comparative optical microscopy and DNA analysis, it has given a new dimension to the characterisation of hair fibres at a molecular level, providing a powerful tool for forensic investigations.

Human hair profiling is an integral part of a forensic investigation but it is one of the most technically difficult subjects in forensic science. This thesis describes the research and development of a novel approach for the rapid identification of unknown human and other related keratin fibres found at a crime scene. The work presented here is developed systematically and considers sample collection, sample preparation, analysis and interpretation of spectral data for the profiling of hair fibres encountered in criminal cases. Spectral comparison of fibres was facilitated with the use of chemometrics methods such as PCA, SIMCA and Fuzzy Clustering, and the less common approach of multi-criteria decision making methodology (MCDM). The aim of the thesis was to investigate the potential of some vibrational spectroscopy techniques for matching and discrimination of single keratin hair fibres in the context of forensic evidence. The first objective (chapter 3) of the thesis was to evaluate the use of Raman and FT-IR micro-spectroscopy techniques for the forensic sampling of hair fibres and to propose the preferred technique for future forensic hair comparisons. The selection of the preferred technique was based on criteria such as spectral quality, ease of use, rapid analysis and universal application to different hair samples. FT-IR micro-spectroscopy was found to be the most appropriate technique for hair analysis because it enabled the rapid collection of spectra from a wide variety of hair fibres. Raman micro-spectroscopy, on the other hand, was hindered with fluorescence problems and did not allow the collection of spectra from pigmented fibres. This objective has therefore shown that FT-IR micro-spectroscopy is the preferable spectroscopic technique for forensic analysis of hair fibres, whilst Raman spectroscopy is the least preferred. The second objective (chapter 3) was to investigate, through a series of experiments, the effect of chemical treatment on the micro-environment of human hair fibres. The effect of bleaching agents on the hair fibres was studied with some detail at different treatment times and the results indicate a significant change in the chemical environment of the secondary structure of the hair fibre along with changes in the C-C backbone structure. One of the most important outcomes of this research was the behaviour of the fÑ-helix during chemical treatment. The hydrogen bonding in the fÑ-helix provides for the stable structure of the fibre and therefore any disruption to the fÑ-helix will inevitably damage the molecular structure of the fibre. The results highlighted the behaviour of the fÑ-helix, which undergoes a significant decrease in content during oxidation, and is partly converted to a random-coil structure, whilst the fÒ-sheet component of the secondary structure remains unaffected. The reported investigations show that the combination of FT-IR and Raman micro-spectroscopy can provide an insight and understanding into the complex chemical properties and reactions within a treated hair fibre. Importantly, this work demonstrates that with the aid of chemometrics, it is possible to investigate simultaneously FT-IR and Raman micro-spectroscopic information from oxidised hair fibres collected from one subject and treated at different times. The discrimination and matching of hair fibres on the basis of treatment has potential forensic applications. The third objective (chapter 4) attempted to expand the forensic application of FT-IR micro-spectroscopy to other keratin fibres. Animal fibres are commonly encountered in crime scenes and it thus becomes important to establish the origin of those fibres. The aim of this work was to establish the forensic applications of FT-IR micro-spectroscopy to animal fibres and to investigate any fundamental molecular differences between these fibres. The results established a discrimination between fibres consisting predominantly of fÑ-helix and those containing mainly a fÒ-sheet structure. More importantly, it was demonstrated through curve-fitting and chemometrics, that each keratin fibre contains a characteristic secondary structure arrangement. The work presented here is the first detailed FT-IR micro-spectroscopic study, utilising chemometrics as well as MCDM methods, for a wide range of keratin fibres, which are commonly, found as forensic evidence. Furthermore, it was demonstrated with the aid of the rank ordering MCDM methods PROMETHEE and GAIA, that it is possible to rank and discriminate keratin fibres according to their molecular characteristics obtained from direct measurements together with information sourced from the literature. The final objective (chapter 5) of the thesis was to propose an alternative method for the discrimination and matching of single scalp human hair fibres through the use of FT-IR micro-spectroscopy and chemometrics. The work successfully demonstrated, through a number of case scenarios, the application of the technique for the identification of variables such as gender and race for an unknown single hair fibre. In addition, it was also illustrated that known hair fibres (from the suspect or victim) can be readily matched to the unknown hair fibres found at the crime scene. This is the first time that a substantial, systematic FT-IR study of forensic hair identification has been presented. The research has shown that it is possible to model and correlate individual¡¦s characteristics with hair properties at molecular level with the use of chemometrics methods. A number of different, important forensic variables of immediate use to police in a crime scene investigation such as gender, race, treatment, black and white hair fibres were investigated. Blind samples were successfully applied both to validate available experimental data and extend the current database of experimental determinations. Protocols were posed for the application of this methodology in the future. The proposed FT-IR methodology presented in this thesis has provided an alternative approach to the characterisation of single scalp human hair fibres. The technique enables the rapid collection of spectra, followed by the objective analytical capabilities of chemometrics to successfully discriminate animal fibres, human hair fibres from different sources, treated from untreated hair fibres, as well as black and white hair fibres, on the basis of their molecular structure. The results can be readily produced and explained in the courts of law. Although the proposed relatively fast FT-IR technique is not aimed at displacing the two slower existing methods of hair analysis, namely comparative optical microscopy and DNA analysis, it has given a new dimension to the characterisation of hair fibres at a molecular level, providing a powerful tool for forensic investigations.

Ventilation is a critical task in underground mining operation. Lack of a good ventilation system causes accumulation of harmful gases, explosions, and even fatalities. A proper ventilation system provides adequate fresh air to miners for a safe and comfortable working environment. Fans, which provide air flow to different faces of a mine, have great impact in ventilation systems. Thus, selection of appropriate fans for a mine is the acute task. Unsuitable selection of a fan decreases safety and production rate, which increases capital and operational costs. Moreover, pitch angle of fans&rsquo
blades plays an important role in fan&rsquo
s efficiency. Therefore, selection of a fan and its pitch angle, which yields the maximum efficiency, is an emerging issue for an efficient mine ventilation. The main objective of this research study is to provide a decision making methodology for the selection of a main fan and its appropriate pitch angle for efficient mine ventilation. Nowadays, analytical hierarchy process as multi criteria decision making is used, and it yields outputs based on pairwise comparison. On the other hand, Fuzzy Logic as a soft computing method was combined with analytical hierarchy process and combined model did not yield appropriate results
because Fuzzy AHP increased uncertainty ratio in this study. However, fuzzy analytical hierarchy process might be inapplicable when it faces with vague and complex data set. Soft computing methods can be utilized for complicated situations. One of the soft computing methods is a Neuro-Fuzzy algorithm which is used in classification and DM issues. This study has two phases: i) selection of an appropriate fan using Analytical Hierarchy Process (AHP) and Fuzzy Analytical Hierarchy Process (Fuzzy AHP) and ii) selection of an appropriate pitch angle using Neuro-Fuzzy algorithm and Fuzzy AHP method. This study showed that AHP can be effectively utilized for main fan selection. It performs better than Fuzzy AHP because FAHP contains more expertise and makes problems more complex for evaluating. When FAHP and Neuro-Fuzzy is compared for pitch angle selection, both methodologies yielded the same results. Therefore, utilization of Neuro-Fuzzy in situation with complicated and vague data will be applicable.

Open pit mine operations are complex businesses that demand a constant assessment of risk. This is because the value of a mine project is typically influenced by many underlying economic and physical uncertainties, such as metal prices, metal grades, costs, schedules, quantities, and environmental issues, among others, which are not known with much certainty at the beginning of the project. Hence, mining projects present a considerable challenge to those involved in associated investment decisions, such as the owners of the mine and other stakeholders. In general terms, when an option exists to acquire a new or operating mining project, , the owners and stock holders of the mine project need to know the value of the mining project, which is the fundamental criterion for making final decisions about going ahead with the venture capital. However, obtaining the mine project’s value is not an easy task. The reason for this is that sophisticated valuation and mine optimisation techniques, which combine advanced theories in geostatistics, statistics, engineering, economics and finance, among others, need to be used by the mine analyst or mine planner in order to assess and quantify the existing uncertainty and, consequently, the risk involved in the project investment. Furthermore, current valuation and mine optimisation techniques do not complement each other. That is valuation techniques based on real options (RO) analysis assume an expected (constant) metal grade and ore tonnage during a specified period, while mine optimisation (MO) techniques assume expected (constant) metal prices and mining costs. These assumptions are not totally correct since both sources of uncertainty—that of the orebody (metal grade and reserves of mineral), and that about the future behaviour of metal prices and mining costs—are the ones that have great impact on the value of any mining project. Consequently, the key objective of this thesis is twofold. The first objective consists of analysing and understanding the main sources of uncertainty in an open pit mining project, such as the orebody (in situ metal grade), mining costs and metal price uncertainties, and their effect on the final project value. The second objective consists of breaking down the wall of isolation between economic valuation and mine optimisation techniques in order to generate a novel open pit mine evaluation framework called the ―Integrated Valuation / Optimisation Framework (IVOF)‖. One important characteristic of this new framework is that it incorporates the RO and MO valuation techniques into a single integrated process that quantifies and describes uncertainty and risk in a mine project evaluation process, giving a more realistic estimate of the project’s value. To achieve this, novel and advanced engineering and econometric methods are used to integrate financial and geological uncertainty into dynamic risk forecasting measures. The proposed mine valuation/optimisation technique is then applied to a real gold disseminated open pit mine deposit to estimate its value in the face of orebody, mining costs and metal price uncertainties.

L’objectif de ce travail est de développer un cadre méthodologique et générique d’éco-conception de procédés chimiques couplant des outils de modélisation et de simulation traditionnels de procédés (HYSYS, COCO, ProSimPlus et Ariane), d’Analyse du Cycle de Vie (ACV), d’optimisation multiobjectif basée sur des Algorithmes Génétiques et enfin des outils d’aide à la décision multicritère (ELECTRE, PROMETHEE, M-TOPSIS). Il s’agit de généraliser, d’automatiser et d’optimiser l’évaluation des impacts environnementaux au stade préliminaire de la conception d’un procédé chimique. L’approche comprend trois étapes principales. Les deux premières correspondent d’une part aux phases d’analyse de l’inventaire par calcul des bilans de matière et d’énergie et d’autre part à l’évaluation environnementale par ACV. Le problème du manque d’information ou de l’imprécision dans les bases de données classiques en ACV pour la production d’énergie notamment sous forme de vapeur largement utilisée dans les procédés a reçu une attention particulière. Une solution proposée consiste à utiliser un simulateur de procédés de production d’utilités (Ariane, ProSim SA) pour contribuer à alimenter la base de données environnementale en tenant compte de variations sur les conditions opératoires ou sur les technologies utilisées. Des sous-modules « énergie » sont ainsi proposés pour calculer les émissions relatives aux impacts liés à l’utilisation de l’énergie dans les procédés. La troisième étape réalise l’interaction entre les deux premières phases et l’optimisation multi-objectif qui met en jeu des critères économiques et environnementaux. Elle conduit à des solutions de compromis le long du front de Pareto à partir desquelles les meilleures sont choisies à l’aide de méthodes d’aide à la décision. L’approche est appliquée à des procédés de production continus : production de benzène par hydrodéalkylation du toluène HDA et production de biodiesel à partir d’huiles végétales. Une stratégie à plusieurs niveaux est mise en oeuvre pour l'analyse de l'optimisation multi-objectif. Elle est utilisée dans les deux cas d'étude afin d'analyser les comportements antagonistes des critères
The objective of this work is to propose an integrated and generic framework for eco-design coupling traditional modelling and flowsheeting simulation tools (HYSYS, COCO, ProSimPlus and Ariane), Life Cycle Assessment, multi-objective optimization based on Genetic Algorithms and multiple criteria decision-making methods MCDM (Multiple Choice Decision Making, such as ELECTRE, PROMETHEE, M-TOPSIS) that generalizes, automates and optimizes the evaluation of the environmental criteria at earlier design stage. The approach consists of three main stages. The first two steps correspond respectively to process inventory analysis based on mass and energy balances and impact assessment phases of LCA methodology. Specific attention is paid to the main issues that can be encountered with database and impact assessment i.e. incomplete or missing information, or approximate information that does not match exactly the real situation that may introduce a bias in the environmental impact estimation. A process simulation tool dedicated to production utilities, Ariane, ProSim SA is used to fill environmental database gap, by the design of specific energy sub modules, so that the life cycle energy related emissions for any given process can be computed. The third stage of the methodology is based on the interaction of the previous steps with process simulation for environmental impact assessment and cost estimation through a computational framework. The use of multi-objective optimization methods generally leads to a set of efficient solutions, the so-called Pareto front. The next step consists in identifying the best ones through MCDM methods. The approach is applied to two processes operating in continuous mode. The capabilities of the methodology are highlighted through these case studies (benzene production by HDA process and biodiesel production from vegetable oils). A multi-level assessment for multi-objective optimization is implemented for both cases, the explored pathways depending on the analysis and antagonist behaviour of the criteria

The business cycle in the automotive industry follows the general economic cycle closely and therefore, undergoes cyclical fluctuations over time. Companies in the sector are faced with challenges and need to deal with market demands efficiently and quickly to stay competitive. Lean approach is one of the strategies that can aid firms to improve their competitiveness by minimizing waste (Pullan et al., 2013). In order to benefit from a lean approach, the first step is to select a proper tool based on the available resources and requirements of the company. Due to the fact that numerous lean tools have been introduced over time, decision makers in company may encounter challenges in selecting the proper one with regard to their demands. To deal with such an issue, Multi-Criteria Decision-Making (MCDM) can greatly assist decision makers to compare available alternatives and consequently select the best possible solution among them. This study aims at improving the operational process in MCoutinho Group, a Portuguese well-known company in the automotive sector, by helping the management board in selecting lean tool due to the company preferences. In this study, the applicability (and results) of the application of some MCDM techniques (SAW, TOPSIS, and VIKOR) is examined to compare ten lean tools, determined based on the literature. The results reveal some gaps between company requirements and the demands which have been considered in previous surveys. The process applied can save the costs of trial and error of implementing different lean tools. And finally, adopting such a lean tool that has been selected totally based on the exclusive requirements of the company can improve efficiency in the company.
O ciclo de negócios na indústria automotiva segue de perto o ciclo econômico geral e, portanto, sofre flutuações cíclicas ao longo do tempo. As empresas do setor enfrentam desafios e precisam lidar com as demandas do mercado de forma eficiente e rápida para se manterem competitivas. A abordagem enxuta é uma das estratégias que pode ajudar as empresas a melhorar sua competitividade, minimizando o desperdício (Pullan et al., 2013). Para se beneficiar de uma abordagem enxuta, o primeiro passo é selecionar uma ferramenta adequada com base nos recursos disponíveis e requisitos da empresa. Devido ao fato de que várias ferramentas enxutas foram introduzidas ao longo do tempo, os tomadores de decisão na empresa podem encontrar desafios ao selecionar a ferramenta adequada com relação às suas demandas. Para lidar com essa questão, a Tomada de Decisão Multi-Critérios (MCDM) pode ajudar muito os tomadores de decisão a comparar as alternativas disponíveis e, conseqüentemente, selecionar a melhor solução possível entre elas. Este estudo tem como objetivo melhorar o processo operacional do Grupo MCoutinho, empresa portuguesa de renome no setor automóvel, auxiliando a administração na seleção da ferramenta enxuta em função das preferências da empresa. Neste estudo, a aplicabilidade (e resultados) da aplicação de algumas técnicas MCDM (SAW, TOPSIS e VIKOR) é examinada para comparar dez ferramentas enxutas, determinadas com base na literatura. Os resultados revelam algumas lacunas entre os requisitos da empresa e as demandas consideradas em pesquisas anteriores. O processo aplicado pode economizar os custos de tentativa e erro de implementação de diferentes ferramentas enxutas. E, por fim, a adoção de uma ferramenta tão enxuta que foi selecionada totalmente com base nos requisitos exclusivos da empresa pode melhorar a eficiência da empresa.

博士
國立高雄科技大學
工業工程與管理系
107
Nowadays, the development of renewable energy sources is an urgent activity. Access to this type of renewable energy not only contributes to providing energy for society but also saves energy and reduces environmental pollution. In Vietnam, given the geographic conditions of a locality with long coastline, high winds and fairly distributed all the year, wind power plants emerged as a viable option. Thus, there are many wind power plants are building. One of the most importance aim of wind power projects is wind power plant location selection, wind farm location selection is Multi-Criteria Decision Making (MCDM) that the decision maker has to face. Although some studies have applied the MCDM model in wind farm location selection, very few studies have put attention on this fuzzy problem. That is why the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and hybrid Fuzzy Analytic Hierarchy Process (FAHP) are built for location selection of wind power farm in Vietnam. In the beginning, this study collected data from seven locations, with good conditions to invest in a wind power plant. an FAHP model was developed to determine the weight of each potential site to build a wind power farm. The TOPSIS is applied to rank all potential alternatives in the last stage. The results showed that Binh Thuan was the best place to build wind power plants in Vietnam. The contributions of this study has proposed an MCDM approach in a fuzzy environment for location selection of wind power farm in Vietnam. This article is also provide a practical approach and new flexible for a decision maker. This study can also provides a useful guide for wind farm locations selection in other countries.

碩士
國立交通大學
科技管理學程碩士班
91
Location selection is the key success factor for the Gallium Arsenic (GaAs) IC foundry. Questionnaire interview with experts generates 4 aspects and 12 criteria for the Analytic Hierarchy Process (AHP) method to evaluate the best GaAs IC foundry location. The aspects include overall strategy, manufacturing resources, manufacturing efficiency and integrated customer service. According to the questionnaire investigation among 12 experts of the industry, the top two weight normalized criteria which influence the GaAs IC FAB location selection are manufacturing efficiency and IC FAB supporting efficiency. In this paper, five GaAs IC foundry alternative locations along the Pan-Pacific Rim are evaluated, which including America (A), China(C), Japan (J), Korea (K) and Taiwan (T). Those alternatives are ranked by multicriteria method of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The result developed by using AHP and TOPSIS indicates that the optimal alternative is Taiwan and the second alternative is Korea due to the outstanding performance in high weight normalized criteria of manufacturing efficiency and IC FAB supporting efficiency. China is ranked as the last in current stage due to the immaturity of overall IC manufacturing environments. However China was believed by those experts to be the next most suitable alternative for GaAs IC foundry location due to high potential customer growth rate, highly demand from downstream integrated system manufacturer and lower overall operation cost.

碩士
國立中央大學
資訊管理學系
102
We always face with various decision-making problems in our daily life. To solve the decision-making problem, more recently there are various approaches have been applied in decision-making problem, such as operation research, statistical analysis, mathematics, sensitivity analysis, and so on. One of the most important approaches is Multi-Criteria decision making (MCDM). Generally, typical forms of MCDM may include three parts, i.e., input, output and solution approach. In general, the input can be expressed as: m alternatives with n criteria. It usually assumes that the underlying input data can be represented as a decision table. Besides, output usually is a single optimal solution and a set of solution preferred by decision maker. In this research, we propose summarization techniques which can summarize decision table and represent it as a summarization table for decision maker to support their making decision. Furthermore, the summarization table can be represented as radar chart to display each alternative for the distribution of the criteria values. The objective of our research is to discover summarization table, and its Degree of Information Lost (DIL) is minimum. Where Degree of Information Lost (DIL) means the sum of distances between original decision table ra×b and table vi×j. Finding a minimum output result is an NP-hard problem. Therefore, we use generic algorithms to improve summarization result.

With the fast-growing of data-rich systems, dealing with complex decision problems is unavoidable. Normalization is a crucial step in most multi criteria decision making (MCDM) models, to produce comparable and dimensionless data from heterogeneous data. Further, MCDM requires data to be numerical and comparable to be aggregated into a single score per alternative, thus providing their ranking. Several normalization techniques are available, but their performance depends on a number of characteristics of the problem at hand i.e., different normalization techniques may provide different rankings for alternatives. Therefore, it is a challenge to select a suitable normalization technique to represent an appropriate mapping from source data to a common scale. There are some attempts in the literature to address the subject of normalization in MCDM, but there is still a lack of assessment frameworks for evaluating normalization techniques. Hence, the main contribution and objective of this study is to develop an assessment framework for analysing the effects of normalization techniques on ranking of alternatives in MCDM methods and recommend the most appropriate technique for specific decision problems. The proposed assessment framework consists of four steps: (i) determining data types; (ii) chose potential candidate normalization techniques; (iii) analysis and evaluation of techniques; and (iv) selection of the best normalization technique. To validate the efficiency and robustness of the proposed framework, six normalization techniques (Max, Max-Min, Sum, Vector, Logarithmic, and Fuzzification) are selected from linear, semi-linear, and non-linear categories, and tested with four well known MCDM methods (TOPSIS, SAW, AHP, and ELECTRE), from scoring, comparative, and ranking methods. Designing the proposed assessment framework led to a conceptual model allowing an automatic decision-making process, besides recommending the most appropriate normalization technique for MCDM problems. Furthermore, the role of normalization techniques for dynamic multi criteria decision making (DMCDM) in collaborative networks is explored, specifically related to problems of selection of suppliers, business partners, resources, etc. To validate and test the utility and applicability of the assessment framework, a number of case studies are discussed and benchmarking and testimonies from experts are used. Also, an evaluation by the research community of the work developed is presented. The validation process demonstrated that the proposed assessment framework increases the accuracy of results in MCDM decision problems.
Com o rápido crescimento dos sistemas ricos em dados, lidar com problemas de decisão complexos é inevitável. A normalização é uma etapa crucial na maioria dos modelos de tomada de decisão multicritério (MCDM), para produzir dados comparáveis e adimensionais a partir de dados heterogéneos, porque os dados precisam ser numéricos e comparáveis para serem agregados em uma única pontuação por alternativa. Como tal, várias técnicas de normalização estão disponíveis, mas o seu desempenho depende de uma série de características do problema em questão, ou seja, diferentes técnicas de normalização podem resultar em diferentes classificações para as alternativas. Portanto, é um desafio selecionar uma técnica de normalização adequada para representar o mapeamento dos dados de origem para uma escala comum. Existem algumas tentativas na literatura de abordar o assunto da normalização, mas ainda há uma falta de estrutura de avaliação para avaliar as técnicas de normalização sobre qual técnica é mais apropriada para os métodos MCDM.Assim, a principal contribuição e objetivo deste estudo são desenvolver uma ferramenta de avaliação para analisar os efeitos das técnicas de normalização na seriação de alternativas em métodos MCDM e recomendar a técnica mais adequada para problemas de decisão específicos. A estrutura de avaliação da ferramenta proposta consiste em quatro etapas: (i) determinar os tipos de dados, (ii) selecionar potenciais técnicas de normalização, (iii) análise e avaliação de técnicas em problemas de MCDM, e (iv) recomendação da melhor técnica para o problema de decisão. Para validar a eficácia e robustez da ferramenta proposta, seis técnicas de normalização (Max, Max-Min, Sum, Vector, Logarithmic e Fuzzification) foram selecionadas - das categorias lineares, semilineares e não lineares- e quatro conhecidos métodos de MCDM foram escolhidos (TOPSIS, SAW, AHP e ELECTRE). O desenho da ferramenta de avaliação proposta levou ao modelo conceptual que forneceu um processo automático de tomada de decisão, além de recomendar a técnica de normalização mais adequada para problemas de decisão. Além disso, é explorado o papel das técnicas de normalização para tomada de decisão multicritério dinâmica (DMCDM) em redes colaborativas, especificamente relacionadas com problemas de seleção de fornecedores, parceiros de negócios, recursos, etc. Para validar e testar a utilidade e aplicabilidade da ferramenta de avaliação, uma série de casos de estudo são discutidos e benchmarking e testemunhos de especialistas são usados. Além disso, uma avaliação do trabalho desenvolvido pela comunidade de investigação também é apresentada. Esta validação demonstrou que a ferramenta proposta aumenta a precisão dos resultados em problemas de decisão multicritério.

碩士
國立海洋大學
漁業科學學系
89
In order to know the suitable areas for setting artificial habitats in the coastal waters of southwestern Taiwan, I utilize Multi-Criteria Decision Making (MCDM) technology with Geographical Information System (GIS) to collect, digitize and analyze all data related to this tropics. From the results, the most potentiality of setting artificial habitats in six counties of the southwestern Taiwan belongs to the coastal zones of Ping-Tong County and Kao-Hsiung County, secondary potentiality belongs to the coastal zones of Tai-Nan County and Kao-Hsiung City. The Chia-Yi County owns the least potentiality because of its broader shallow-water (<5m) area. It also shows the site selection model of artificial habitats work by comparing to the past setting evaluations of artificial habitats. The site selection model could be further modified for additional GIS layers in order to provide a better tool for decision making of artificial-habitat setting, and to enhance the effects of the artificial habitats that suitably setted.

博士
國立高雄科技大學
工業工程與管理系
107
In a market economy, choosing a supplier is a strategic decision, directly affecting to the business performance of the businesses, choosing an effective suppliers to help businesses create high quality products, low cost, meeting the demand that customers’ expect, and being able to aim for sustainable development in the future. The benefits of choosing the best suppliers help businesses gain a competitive advantages in the market. However, the process of selecting suppliers is very complicated, requiring decision makers to analyze and evaluate many qualitative and quantitative criteria are conflict together to get accurate results. Thus, this dissertation presents a mathematical model to optimize suppliers selection process, with the aim of improving products quality, timely delivery, meeting the increasing demands of customers, and ensuring reduce production costs of products. The multi-criteria decision-making (MCDM) model is proposed to address supplier evaluation and selection issues. In case study, the author considered twenty five rice suppliers in Viet Nam, they are adjusted based on 4 main criteria including financial criteria, delivery and services factors, qualitative criteria and environmental management systems. In the first stage of this study, fuzzy analytic network process (FANP) is applied to determine the weight of 25 potential suppliers (Decision Making Unit – DMU). Subsequently, data envelopment analysis (DEA) model were used to rank suppliers. As a results, there are 7 optimal suppliers from 25 potential suppliers list that can bring high economic efficiency for businesses.

博士
國立中央大學
企業管理學系
107
Appropriate airport ground handling service (AGHS) equipment vendor selection (AGHSEVS) can prevent aircraft damage and delays in airlines schedules, and ensure reliable and high-quality ground handling service. Previous research has seldom integrated multi-criteria decision making techniques with goal programming to solve the AGHSEVS problem. This paper describes a new system evaluating on model for AGHSEVS by considering both qualitative and quantitative methods. We compare fuzzy TOPSIS method based on fuzzy weighted average left and right score methods with multi-choice and multi-aspiration goal programming approach of an AGHS company in Taiwan. To reduce many uncertain factors in qualitative research such as the subjective preferences of decision makers and the technological advances in trends, the proposed approach can help airport ground handling service company managers make optimal decisions for AGHSEVS problems through the approach of goal programming. The practicability of the proposed model can be further applied to other AGHS companies with various scenarios and environments.

A dynamic decision making framework implementing multi criteria routing of multimedia services at private-public network border with access technology convergence is presented. The ingredients of the framework include information model, semantics capturing via ontology, information sharing and dissemination mechanisms and rule/policy specifications methodology. The control and management over the infrastructure is carried out by revamping the sole signaling protocols (SIP, diameter and SNMP). DEN-ng is enhanced and tagged in accordance with the requirements over the underlying framework. A dedicated language for the platform is proposed that has its deep roots inside the framework to avoid conflicts and overlapping. A dynamic decision engine is developed for routing the requests/sessions at private-public network border over the underlying multi-homed environment. Multi Criteria Decision Making (MCDM) theory is used for decision computation/calculation and the adapted methods are exploited according to the scenario and decision computation mode while keeping in view the corresponding enforcement mode. A test bed is developed to validate the proposed framework. The proposed system offers higher throughput and lowers call-dropping probability with an add-on susceptible delay.

Mestrado Mediterranean Forestry and Natural Resources Management (MEDFOR) - Instituto Superior de Agronomia - UL
In recent years, an integration of water production values for forest ecosystems into forest management planning models has become increasingly important in sustainable forest management. The reason is that both for surface and groundwater resources quantity and quality – forests play a vital role. The main objective of this work is to produce an analysis of forest management planning methods that have been implemented the most frequently for assessing an optimal integrated forest and water management for forested catchments. The research was developed within the framework of Alternative Models and Robust Decision-Making for Future Forest Management (ALTERFOR) Project; being developed to assess Decision Support System (DSS) tools that support sustainable forest management by means of including a variety of ecosystem services. For that purpose, we use a taxonomy of water related ecosystem services based on the classification scheme estimated based on the findings of the project. The taxonomy is used to develop a query of relevant references from 2754 journals, encompassing 46 scientific categories, stored in the ISI Web of Science database over the last 30 years. Query results were analyzed, and decision-making methods were classified and assessed according to their supporting role to forest managers in addressing specific ecosystem services. As forest managers are often confronted with conflicting preferences by owners and stakeholders regarding ecosystem services target levels, the query will further address the potential of decision-making methods to facilitate negotiation and consensus building
N/A