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1
Darware, N. P., Prof Bhavesh Bohra, Prof Swapanil Chaudhary, Dr Bharat Chede, and Dr Somdatta Karanjekar. "Design, Optimization and Analysis of Milling Fixture." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 1385–87. http://dx.doi.org/10.22214/ijraset.2023.53858.
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Abstract: The manufacturing industry of small scale provides wide range of products to fulfill the market needs. To face many challenges of market these industries should increase their production rate with good quality and accuracy. The manual production is of low production rate and long throughput time. Moreover, standardization of manual processes is difficult and also its difficulty in maintenance, thus fixtures is used on the machines. Therefore, this study aims to design a Fixture. Basically, Fixture is a work holding device to guide the tool. The main purpose of making this fixture is to perform the milling operation without any need of shifting the job regularly. This results in reduction of production time and increase in production rate. This will lead to decreasing manufacturing time and also the machining cost. In this work the Milling fixture is designed and analyzed for the stresses and deformation that occur during the machining process. The CAD modeling Software is used to model the Milling fixture and analysis work will be carried out by using ANSYS Workbench Software.
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Pol, G. J., A. R. Jadhav, S. J. Kadam, S. V. Kumbhar, and J. G. Shinde. "Design Optimization of Frame of Mechanical Press Machine." Asian Review of Mechanical Engineering 10, no. 1 (2021): 1–7. http://dx.doi.org/10.51983/arme-2021.10.1.2795.
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Power presses are used for the simple, accurate, and economical production of large quantities of articles quickly, accurately, and economically from the cold working of mild steel and other ductile materials. The components produced range over an extremely wide field and are used throughout the industry. Sometimes the pressings may be complicated and more than one pressing operation may be required. The press purpose is to shift one or more sources and movements to a tool or to die to shape or blanch a piece of work. Press design calls for the application of special knowledge about the production process. The press is designed either to perform a specific process or for primarily universal use. The manufacturing process for the metal formation is almost chip less. To perform these tasks Press tools are used. Job component deformation to the desired size is achieved by applying pressure. Presses are regarded as the best and most efficient way of shaping sheet metal into finished products. Pneumatic presses are widely used for operations such as punching, grinding, molding, clinching, blanking, deep drawing, and metal shaping.
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Wen, Shilin, Rui Han, Ke Qiu, et al. "K8sSim: A Simulation Tool for Kubernetes Schedulers and Its Applications in Scheduling Algorithm Optimization." Micromachines 14, no. 3 (2023): 651. http://dx.doi.org/10.3390/mi14030651.
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In recent years, Kubernetes (K8s) has become a dominant resource management and scheduling system in the cloud. In practical scenarios, short-running cloud workloads are usually scheduled through different scheduling algorithms provided by Kubernetes. For example, artificial intelligence (AI) workloads are scheduled through different Volcano scheduling algorithms, such as GANG_MRP, GANG_LRP, and GANG_BRA. One key challenge is that the selection of scheduling algorithms has considerable impacts on job performance results. However, it takes a prohibitively long time to select the optimal algorithm because applying one algorithm in one single job may take a few minutes to complete. This poses the urgent requirement of a simulator that can quickly evaluate the performance impacts of different algorithms, while also considering scheduling-related factors, such as cluster resources, job structures and scheduler configurations. In this paper, we design and implement a Kubernetes simulator called K8sSim, which incorporates typical Kubernetes and Volcano scheduling algorithms for both generic and AI workloads, and provides an accurate simulation of their scheduling process in real clusters. We use real cluster traces from Alibaba to evaluate the effectiveness of K8sSim, and the evaluation results show that (i) compared to the real cluster, K8sSim can accurately evaluate the performance of different scheduling algorithms with similar CloseRate (a novel metric we define to intuitively show the simulation accuracy), and (ii) it can also quickly obtain the scheduling results of different scheduling algorithms by accelerating the scheduling time by an average of 38.56×.
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Li, Xin Hua, Jian Zhou, Yi Zhang, and Ling Dai. "Fuzzy Optimal Design of Tower Crane Jib." Advanced Materials Research 199-200 (February 2011): 530–33. http://dx.doi.org/10.4028/www.scientific.net/amr.199-200.530.
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This article takes the tower crane jib as the optimized object, in view of the jib unique feature, and has established the jib quality optimization objective function, then uses amplification coefficient to obtain its membership function to the fuzzy constraint condition,thus has transformed constraint condition very well. Direct searching tool-box of MATLAB software is adopted to get the optimization model,not only is simplified the optimization process,but also global optimal solution is found reliably.
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Karthik, Hosavaranchi Puttaraju. "OPTIMIZING JOB DESIGN FOR BUSINESS STRATEGIC EXECUTION: A JDOT-BASED FRAMEWORK FOR AI-DRIVEN ORGANIZATIONAL TRANSFORMATION." International Journal of Business Quantitative Economics and Applied Management Research 7, no. 8 (2023): 33–45. https://doi.org/10.5281/zenodo.14679607.
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Strategic execution is critical to achieving business objectives, but organizations often fail to align job roles effectively to drive execution. The Job Design Optimization Tool (JDOT) offers a structured framework for aligning roles to strategic goals and understanding the probability of a person succeeding in that role by focusing on job spans—accountability, control, influence, and support. This paper applies JDOT to optimize roles for strategic execution, using the Go Mobile regional manager case study and how it impacted the traditional role. Additionally, it also applies the framework to design the Transformation manager role and Innovation manager role to support and accelerate the impact of emerging technologies on the organizational evolution and to lead continuous innovation and transformation to drive strategy execution by evaluating the impact of these roles and their success on the business strategy. It also highlights the traditional project diagnostic parameters such as project success rates, execution timelines, and collaboration scores introduced to assess effectiveness and how they are impacted by well-designed roles with the help of JDOT. Theoretical Comparative analysis shows how JDOT-optimized roles outperform poorly structured ones in driving strategy execution.
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Tang, Changjiang, Sukanya Chaemchoy, and Pruet Siribanpitak. "Identifying professional development needs to enhance inclusive leadership among university faculty in Chongqing, China." International Journal of Innovative Research and Scientific Studies 8, no. 3 (2025): 366–78. https://doi.org/10.53894/ijirss.v8i3.6510.
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This study aims to identify the professional development needs required to enhance inclusive leadership among university faculty members in Chongqing, China. As part of a broader initiative to design a competency-based training model for higher education, the research adopts a quantitative, descriptive design involving 379 academic professionals. A structured needs assessment tool, utilizing a modified Priority Needs Index (PNImodified), was employed. The findings revealed significant gaps between the current and desired states of inclusive leadership, particularly in talent optimization, adaptive mindset, and interpersonal trust. Training was the most frequently cited off-the-job method, while mentoring and coaching dominated among on-the-job strategies. The study concludes that inclusive leadership development remains insufficiently addressed in current faculty development efforts. Practically, the results inform the design of targeted training models for higher education, offering strategic direction for future human resource development (HRD) initiatives in Chinese universities.
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Penazzi, Stefano, Riccardo Accorsi, Emilio Ferrari, Riccardo Manzini, and Simon Dunstall. "Design and control of food job-shop processing systems." International Journal of Logistics Management 28, no. 3 (2017): 782–97. http://dx.doi.org/10.1108/ijlm-11-2015-0204.
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Purpose The food processing industry is growing with retail and catering supply chains. With the rising complexity of food products and the need to address food customization expectations, food processing systems are progressively shifting from production line to job-shops that are characterized by high flexibility and high complexity. A food job-shop system processes multiple items (i.e. raw ingredients, toppings, dressings) according to their working cycles in a typical resource and capacity constrained environment. Given the complexity of such systems, there are divergent goals of process cost optimization and of food quality and safety preservation. These goals deserve integration at both an operational and a strategic decisional perspective. The twofold purpose of this paper is to design a simulation model for food job-shop processing and to build understanding of the extant relationships between food flows and processing equipment through a real case study from the catering industry. Design/methodology/approach The authors designed a simulation tool enabling the analysis of food job-shop processing systems. A methodology based on discrete event simulation is developed to study the dynamics and behaviour of the processing systems according to an event-driven approach. The proposed conceptual model builds upon a comprehensive set of variables and key performance indicators (KPIs) that describe and measure the dynamics of the food job-shop according to a multi-disciplinary perspective. Findings This simulation identifies the job-shop bottlenecks and investigates the utilization of the working centres and product queuing through the system. This approach helps to characterize how costs are allocated in a flow-driven approach and identifies the trade-off between investments in equipment and operative costs. Originality/value The primary purpose of the proposed model relies on the definition of standard resources and operating patterns that can meet the behaviour of a wide variety of food processing equipment and tasks, thereby addressing the complexity of a food job-shop. The proposed methodology enables the integration of strategic and operative decisions between several company departments. The KPIs enable identification of the benchmark system, tracking the system performance via multi-scenario what-if simulations, and suggesting improvements through short-term (e.g. tasks scheduling, dispatching rules), mid-term (e.g. recipes review), or long-term (e.g. re-layout, working centres number) levers.
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Salonvaara, Mikael, Andre Desjarlais, Antonio J. Aldykiewicz, et al. "Application of Machine Learning to Assist a Moisture Durability Tool." Energies 16, no. 4 (2023): 2033. http://dx.doi.org/10.3390/en16042033.
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The design of moisture-durable building enclosures is complicated by the number of materials, exposure conditions, and performance requirements. Hygrothermal simulations are used to assess moisture durability, but these require in-depth knowledge to be properly implemented. Machine learning (ML) offers the opportunity to simplify the design process by eliminating the need to carry out hygrothermal simulations. ML was used to assess the moisture durability of a building enclosure design and simplify the design process. This work used ML to predict the mold index and maximum moisture content of layers in typical residential wall constructions. Results show that ML, within the constraints of the construction, including exposure conditions, does an excellent job in predicting performance compared to hygrothermal simulations with a coefficient of determination, R2, over 0.90. Furthermore, the results indicate that the material properties of the vapor barrier and continuous insulation layer are strongly correlated to performance.
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Xu, Shu Bo, K. K. Sun, Cai Nian Jing, and Guo Cheng Ren. "Finite Element Analysis and Optimization of Structure for Large Blanking Machine Equipment during High-Speed Blanking." Materials Science Forum 697-698 (September 2011): 176–81. http://dx.doi.org/10.4028/www.scientific.net/msf.697-698.176.
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This paper reviews the background and significance to investigate the high-speed speed blanking process modeling simulation and optimization of large blanking machine equipment for CNC uncoiling, leveling and shearing line. And a powerful tool has been providing to of heavy machinery optimal design specifications. The finite element model of beam structure has been established by using a three-dimensional modeling software UG NX4.0 CAD and finite element analysis software ANSYS. Then the static and dynamic characteristics results of the whole beam structure have been simulated. On the basis of analysis results, the optimal static and dynamic performance of square cross-section of the beam structure has been obtained. Drawings at the design stage of large blanking machine equipment for NC uncoiling, leveling and shearing line, the use of finite element theory and modal analysis theory, the structure of the blanking machine static and dynamic performance analysis and prediction using optimal design method for optimization, the new machine to improve job performance, improve processing accuracy, shorten the development cycle and enhance the competitiveness of products is very important.
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Bao, Bizhen, Zhao Duan, Ningbo Xu, et al. "A new algorithm of the scheduling of a flexible manufacturing system based on genetic algorithm." Manufacturing Review 10 (2023): 11. http://dx.doi.org/10.1051/mfreview/2023010.
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In the flexible manufacturing system, a reasonable production scheduling is crucial in shortening the processing completion time and improving the equipment utilization. Traditional manual scheduling cannot effectively solve the complex workshop scheduling problems and cannot provide a scheduling solution that meets the requirements in a short period of time, which can lead to a decrease in processing efficiency. Aiming at the complex job shop scheduling problem, the genetic algorithm is used to find the optimal scheduling solution in this study, taking the number of overdue jobs, the total overdue time, the job completion time, the comprehensive load rate and the maximum load rate of the machine tool as the performance indicators of the scheduling algorithm. The chromosomes are designed as process gene chain and equipment gene chain to improve the diversity and the robustness to scheduling problems of chromosome through crossover, variation, selection and other processes. The impact of different parameter settings on the performance indicators of each scheduling algorithm is researched by adjusting the four algorithm-related parameters, and there has been a certain improvement in the results of the scheduling problems. This study provides a reference for the design and optimization of production scheduling algorithm based on genetic algorithm.
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Viazlo, Oleksandr, and André Sailer. "Efficient Iterative Calibration on the Grid using iLCDirac." EPJ Web of Conferences 245 (2020): 03003. http://dx.doi.org/10.1051/epjconf/202024503003.
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Software tools for detector optimization studies for future experiments need to be efficient and reliable. One important ingredient of the detector design optimization concerns the calorimeter system. Every change of the calorimeter configuration requires a new set of overall calibration parameters which in turn requires a new calorimeter calibration to be done. An efficient way to perform calorimeter calibration is therefore essential in any detector optimization tool set. In this contribution, we present the implementation of a calibration system in iLCDirac, which is an extension of the DIRAC grid interware. Our approach provides more direct control over the grid resources to reduce overhead of file download and job initialisation, and provides more flexibility during the calibration process. The service controls the whole chain of a calibration procedure, collects results from finished iterations and redistributes new input parameters among worker nodes. A dedicated agent monitors the health of running jobs and resubmits them if needed. Each calibration has an up-to-date backup which can be used for recovery in case of any disruption in the operation of the service. As a use case, we will present a study of optimization of the calorimetry system of the CLD detector concept for FCC-ee, which has been adopted from the CLICdet detector model. The detector has been simulated with the DD4hep package and calorimetry performance has been studied with the particle flow package PandoraPFA.
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Wu, Jingjing. "Research on Precise Recruitment Strategy of Hospital Talents Based on Post Competency Model." Scientific and Social Research 6, no. 12 (2025): 338–43. https://doi.org/10.26689/ssr.v6i12.9240.
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This study focuses on the precise recruitment strategy of hospital talents based on the post competency model. Through the analysis of the current situation and problems of hospital talent recruitment, this paper expounds the method and significance of the construction of post competency model. This paper discusses in detail how to design accurate recruitment process based on this model, including job analysis, competency assessment tool development, recruitment channel optimization, and improvement of interview and selection links. The aim is to provide scientific and effective talent recruitment theoretical basis and practical guidance for hospital human resource management, to improve the quality of hospital talent team, enhance the core competitiveness of hospitals, better meet the needs of medical services and promote the sustainable development of hospitals.
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Mohanan, Martin, Poorva Belwal, Samit Kapoor, Satya Sharma, and Mrs Kirti Kushwah. "Revolutionizing Career Advancement: A Comprehensive Research on a Resume Builder Application." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 2763–68. http://dx.doi.org/10.22214/ijraset.2024.60511.
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Abstract: This abstract describes a modern Resume Builder Application that serves both job seekers and recruiters. It provides a comprehensive platform for producing, assessing, and matching resumes.The Resume Builder Application uses advanced natural language processing and machine learning to help users create personalized and visually appealing resumes. The userfriendly design provides a smooth experience by emphasizing significant abilities, experiences, and achievements. The application provides dynamic templates and content optimization features to guarantee resumes meet industry standards and attract recruiters' attention.The application offers a powerful candidate management solution to organizational users. Recruiters can search and filter a consolidated database of resumes to locate applicants who satisfy specific specifications. The program's powerful matching algorithm compares resumes to job descriptions for efficient candidate shortlisting and accurate alignment with company goals.The program's analytics platform helps employers track diversity data and enhance hiring practices.The resume builder application is a unique digital tool that can be used by individuals at any stage of their career. It becomes an easy-to-use platform for creating, editing, or updating resumes and portfolios by offering personalized career paths, skill evaluation, up-to-date job market information, and a valuable professional network. In order to protect sensitive data, the application also prioritizes privacy and data security and complies with industry requirements. Custom privacy settings allow individuals to maintain control over how their resumes are displayed
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V. Chandra Prakash, Dr, Subhash Tatale, Vrushali Kondhalkar, and Laxmi Bewoor. "A Critical Review on Automated Test Case Generation for Conducting Combinatorial Testing Using Particle Swarm Optimization." International Journal of Engineering & Technology 7, no. 3.8 (2018): 22. http://dx.doi.org/10.14419/ijet.v7i3.8.15212.
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In software development life cycle, testing plays the significant role to verify requirement specification, analysis, design, coding and to estimate the reliability of software system. A test manager can write a set of test cases manually for the smaller software systems. However, for the extensive software system, normally the size of test suite is large, and the test suite is prone to an error committed like omissions of important test cases, duplication of some test cases and contradicting test cases etc. When test cases are generated automatically by a tool in an intelligent way, test errors can be eliminated. In addition, it is even possible to reduce the size of test suite and thereby to decrease the cost & time of software testing.It is a challenging job to reduce test suite size. When there are interacting inputs of Software under Test (SUT), combinatorial testing is highly essential to ensure higher reliability from 72 % to 91 % or even more than that. A meta-heuristic algorithm like Particle Swarm Optimization (PSO) solves optimization problem of automated combinatorial test case generation. Many authors have contributed in the field of combinatorial test case generation using PSO algorithms.We have reviewed some important research papers on automated test case generation for combinatorial testing using PSO. This paper provides a critical review of use of PSO and its variants for solving the classical optimization problem of automatic test case generation for conducting combinatorial testing.
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Lowther, David, Vahid Ghorbanian, Mohammad Hossain Mohammadi, and Issah Ibrahim. "Design tools for electromagnetic-driven multi-physics systems using high performance computing." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 39, no. 1 (2019): 198–205. http://dx.doi.org/10.1108/compel-06-2019-0234.
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Purpose The design of electromagnetic systems for a variety of applications such as induction heating, electrical machines, actuators and transformers requires the solution of a multi-physics problem often involving thermal, structural and mechanical coupling to the electromagnetic system. This results in a complex analysis system embedded within an optimization process. The appearance of high-performance computing systems over the past few years has made coupled simulations feasible for the design engineer. When coupled with surrogate modelling techniques, it is possible to significantly reduce the wall clock time for generating a complete design while including the impact of the multi-physics performance on the device. Design/methodology/approach An architecture is proposed for linking multiple singe physics analysis tools through the material models and a controller which schedules the execution of the various software tools. The combination of tools is implemented on a series of computational nodes operating in parallel and creating a “super node” cluster within a collection of interconnected processors. Findings The proposed architecture and job scheduling system can allow a parallel exploration of the design space for a device. Originality/value The originality of the work derives from the organization of the parallel computing system into a series of “super nodes” and the creation of a materials database suitable for multi-physics interactions.
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Hutchins, Amber L. "Beyond resumes: LinkedIn for marketing educators." Journal of Research in Interactive Marketing 10, no. 2 (2016): 137–47. http://dx.doi.org/10.1108/jrim-12-2015-0099.
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Purpose This paper aims to explore expanded uses for LinkedIn.com as a teaching tool, beyond introductory assignments, for in-demand and emerging marketing techniques. Design/methodology/approach This paper reviews the current use of LinkedIn as a teaching tool for marketing educators and presents sample assignments that can be used to introduce students to content marketing, SEO, and online reputation management. Findings Marketing educators have found LinkedIn to be a valuable resource for teaching professional development in the business curriculum. But the site can also provide a platform for the exploration of emerging specializations including content marketing and search engine optimization (SEO). Research limitations/implications As a general review, this paper provides an overview of techniques. Research is needed to test and further explore the use of these techniques. Practical implications This paper provides rationale and recommendations for marketing educators who wish to better prepare students for a competitive job market, in which employers expect students to be well versed not only in social media but also in emerging techniques like content marketing and SEO. Originality/value Researchers have explored the use of various social media platforms for marketing education, including LinkedIn. This paper addresses the future potential of LinkedIn to teach various marketable skills and shows the versatility of social media platforms as pedagogical tools.
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Werthan, Audrey Kay, and Mojtaba Navvab. "Building Design Strategies and Their Contribution to Energy Performance for LEED Certification." Journal of Green Building 1, no. 4 (2006): 67–87. http://dx.doi.org/10.3992/jgb.1.4.67.
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Leadership in Energy and Environmental Design (LEED) is a national set of standards put forth by the U. S. Green Building Council in 1994 that was intended to inspire building designers to plan greener, more sustainable buildings. LEED offers up to ten points for improved energy optimization performance. It should be noted that achieving these ten points is time consuming, complex, and expensive. This research is a case study that details the process of using a computer simulation study as a building energy optimizing tool in order to achieve these optional points. Determination is made as to how many LEED points can be obtained when basic strategies such as window performance and day-lighting are integrated into one energy optimized building design. The results show that well-established energy conservation methods achieve as few as two or three LEED energy points, thereby possibly offering a disincentive for designers to attempt this difficult challenge. These fundamental efforts to achieve energy optimized building design are the first steps toward high performance building design and offer a fundamental solution to the substantial, negative environmental impacts caused by buildings today.
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Halabi, Mohammad, Murad Alshehri, Hassan Alzhrani, Mohammed K. Fageha, and Alaa Alaidroos. "DESIGN AND OPTIMIZATION OF A COST-EFFECTIVE NET-ZERO ENERGY MOSQUE LOCATED IN A HOT AND DRY CLIMATE." Journal of Green Building 19, no. 3 (2024): 91–115. http://dx.doi.org/10.3992/jgb.19.3.91.
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ABSTRACT This research paper presents a comprehensive analysis to develop an energy-efficient and net-zero-energy (NZE) model for a proposed hypothetical mosque model located in Riyadh, Saudi Arabia. The study employs a sequential optimization analysis using BEopt software (Building Energy Optimization Tool) to explore various energy conservation measures (ECMs) from three main categories: HVAC, lighting, and building envelope. The ECMs are evaluated based on energy savings and life-cycle cost, with the most cost-effective options selected for the proposed energy-efficient design package. Furthermore, a passive downdraught evaporative cooling (PDEC) system is introduced as a passive cooling strategy to further reduce the cooling energy consumption. Computational fluid dynamics (CFD) analysis is utilized to optimize the airflow around the building and PDEC tower, determining the optimal PDEC dimensions for enhanced ventilation performance. The PDEC system is found to contribute 12% to cooling energy savings, which further enhances the energy efficiency of the proposed mosque. A rooftop solar PV system is incorporated into the optimal energy-efficient design to achieve a NZE mosque. The results demonstrate that the NZE model achieves 80% energy savings, with the remaining 20% offset by the solar PV system. Moreover, life-cycle cost analysis reveals that the NZE model offers the lowest cost, making it the most cost-effective design option. This research provides valuable insights into designing sustainable mosques in hot and dry climates, such as Riyadh, offering a comprehensive solution to significantly reduce energy end-use and promote sustainable building practices.
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Park, Arim, Mark Rodgers, and Soohyun Cho. "Designing Efficient and Equitable Freight Services Markets for Sustainable Economic Performance." American Business Review 27, no. 1 (2024): 167–81. http://dx.doi.org/10.37625/abr.27.1.167-181.
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Despite the indispensability of freight trucking services and truckers’ role as critical stakeholders in supply chains, relatively little attention has been paid to analyzing small independent truckers’ roles. Shippers often prefer working with larger trucking companies to the detriment of smaller independent truckers, who must grapple with an inherently disadvantageous job market. Furthermore, in the presence of uncertainty or peak demand periods, trucker shortages can pose significant economic challenges for shippers and downstream customers. In this paper, we propose an analytical framework to address these challenges in efforts to enhance the sustainability of the freight service industry. We formulate and solve a weighted bi-objective optimization model that simultaneously maximizes the total profits of both shippers and truckers to design a sustainable freight services market. Further, we leverage Monte Carlo simulation trials to examine how all players in this market can achieve a better solution under uncertainty. Ultimately, after evaluating multiple scenarios, we find that shippers and truckers yield the highest economic benefits under a balanced design that leverages principles of supply chain coordination, while satisfying all demand from shippers. This framework can serve as a decision support tool for policymakers who aim to ensure all stakeholders in the market can become and remain profitable. Based on our findings, this study suggests practical implications on how to consider humanitarian policies aimed at promoting equity for truckers and ensuring the timely shipment of essential products for both shippers and truckers.
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Singh, Kanwal Jeet, Inderpreet Singh Ahuja, and Jathinder Kapoor. "Optimization of material removal rate in ultrasonic machining of polycarbonate bulletproof glass and acrylic heat-resistant glass by Taguchi method." Multidiscipline Modeling in Materials and Structures 13, no. 4 (2017): 612–27. http://dx.doi.org/10.1108/mmms-04-2017-0019.
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Purpose The purpose of this paper, an original research paper, is to study the optimization of material removal rate (MRR) in ultrasonic machining of polycarbonate bulletproof glass and acrylic heat-resistant glass. The machining of these materials is a very tough job. There are so many constraints which need to be taken into account while machining, but without proper knowledge of material properties and machining parameters, machining is not possible. This paper gives basic knowledge about polycarbonate bulletproof and acrylic heat-resistant glass and provides ways as to how these types of materials are processed or machined. Design/methodology/approach The Taguchi method was utilized to optimize the ultrasonic machining parameters for drilling these advanced materials. The relationship between MRR and other controllable process parameters such as concentration of slurry, type of abrasive, abrasive grit size, power rating, concentration of HF acid and type of tool material has been analyzed by using the Taguchi approach. Findings Through the Taguchi analysis, it is concluded that types of abrasive and HF acid concentrations have a significant role to play in MRR for both materials; in which, type of abrasive have 72.91 and 72.96 percent contribution in MRR for polycarbonate bulletproof and acrylic heat-resistant glass, respectively. Similarly, HF acid concentration has 14.70 and 14.65 percent contribution in MRR for polycarbonate bulletproof and acrylic heat-resistant glass, respectively. The MRR was improved by 34.44 percent in polycarbonate bulletproof glass and 29.25 percent in acrylic heat-resistant glass. Originality/value After experimental investigation, the results of the Taguchi modal are validated.
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Lehr, Doug. "Technology Focus: Completions (April 2021)." Journal of Petroleum Technology 73, no. 04 (2021): 41. http://dx.doi.org/10.2118/0421-0041-jpt.
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In the 2020 Completions Technology Focus, I stated that digitization will forever change how the most complex problems in our industry are solved. And, despite another severe downturn in the upstream industry, data science continues to provide solutions for complex unconventional well problems. Casing Damage Casing collapse is an ongoing problem and almost always occurs in the heel of the well. It prevents passage of frac plugs and milling tools. Forcing a frac plug through the collapsed section damages the plug, predisposing it to failure, which leads to more casing damage and poor stimulation. One team has developed a machine-learning (ML) model showing a positive correlation between zones with high fracturing gradients and collapsed casing. The objective is a predictive tool that enables a completion design that avoids these zones. Fracture-Driven Interactions (FDIs) Can Be Avoided in Real Time Pressurized fracturing fluids from one well can communicate with fractures in a nearby well or can intersect that well-bore. Such FDIs can occur while fracturing a child well and can negatively affect production in the parent well. FDIs are caused by well spacing, depletion, or completion design but, until recently, were not quickly diagnosed. Analytics and machine learning now are being used to analyze streaming data sets during a frac job to detect FDIs. A recently piloted detection system alerts the operator in real time, which enables avoidance of FDIs on the fly. Data Science Provides the Tools Analyzing casing damage and FDIs is a complex task involving large amounts of data already available or easily acquired. Tools such as ML perform the data analysis and enable decision making. Data science is enabling the unconventional “onion” to be peeled many layers at a time. Recommended additional reading at OnePetro: www.onepetro.org. SPE 199967 - Artificial Intelligence for Real-Time Monitoring of Fracture-Driven Interactions and Simultaneous Completion Optimization by Hayley Stephenson, Baker Hughes, et al. SPE 201615 - Novel Completion Design To Bypass Damage and Increase Reservoir Contact: A Middle Magdalena, Central Colombian Case History by Rosana Polo, Oxy, et al. SPE 202966 - Well Completion Optimization in Canada Tight Gas Fields Using Ensemble Machine Learning by Lulu Liao, Sinopec, et al.
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S. Muthukumar. "Artificial Intelligence Adoption and Its Influence on Workforce Dynamics in the IT Sector." Journal of Information Systems Engineering and Management 10, no. 43s (2025): 258–68. https://doi.org/10.52783/jisem.v10i43s.8362.
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Introduction: As a response to evolving job roles and workforce dynamics-related issues, IT firms are turning to more advanced technologies, of which Artificial Intelligence (AI) is a force for change. AI has emerged as one of the most influential and quickest-to-be-adopted innovations, with far-reaching implications in terms of influencing the manner in which companies' operate and the manner in which employees engage with their work. By streamlining mundane and repetitive work, AI not only maximizes operations but also releases human labor to focus on higher-value, innovative, and strategic tasks. This technological revolution is changing the existing jobs along with creating new opportunities, more so in technologically oriented domains such as data science, machine learning, and AI system management. As AI penetrates more workforce processes, it also calls for a parallel evolution in workforce capacities. The workers will have to continuously upskill and gain hard skills like programming and data science, in addition to soft skills such as critical thinking, flexibility, and emotional quotient. Furthermore, the advent of AI encourages a more adaptive and dynamic work culture. It enables agile teams and more flexible work cultures, such as remote or hybrid work arrangements. Not only do these innovations drive greater organizational effectiveness but also a greater employee experience in terms of satisfaction and personalization. In essence, AI is not just an optimization tool—it is an accelerator of a broader cultural and structural shift in the modern workplace. Objectives: The primary goal of this research is to realize comprehensively the impact of using Artificial Intelligence (AI) on manpower dynamics within the Information Technology (IT) sector, with specific reference to the Coimbatore District of Tamil Nadu, India. Methods: convenience sampling was used to select 201 IT employees in Coimbatore District of Tamil Nadu, India. Questionnaire was prepared to obtain data from the samples. Mean and Chi-square test was conducted in this study. Results: Chi-Square test statistic is statistically significant at indicating association among age, gender, marital status, income, adoption, job positions and AI impact, and hence suggests that views about AI impact vary remarkably across IT firm job positions and employees' age, gender, marital status, income, AI adoption and job positions. Conclusions: Design and implement training that is age- and career-stage-differentiated based on the varying impact of AI across different groups of demographics. Offer mentorship, up-skilling, and inclusion initiatives focused on women working in tech for their equitable transformation to AI-enabled transformations. Develop support structures accounting for the specific needs of high- and low-income employees who might feel even more vulnerable or stressed by AI innovation. Identify how individual factors such as marital status and life stage impact AI planning for adoption—most notably in determining support for work-life balance and career progression. This emphasizes the need for organizations to embrace inclusive, human-focused AI policies that prioritize upskilling, emotional intelligence, flexible working patterns, and ethical AI leadership.
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Chakhmakhchev, Alexander. "Case Study: Optimization of Bakken Operations Using Data Science: Examples of the Workflows and Lessons Learned." Journal of Petroleum Technology 77, no. 06 (2025): 1–5. https://doi.org/10.2118/0625-0006-jpt.
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_ Since approximately 2012, large multistage completions in horizontal wells have significantly boosted oil production in the Bakken Formation. As of the time of writing, more than 18,000 stimulated wells have been completed in Bakken using various completion designs. Operators have experimented with well spacing, as well as the size and intensity of stimulation jobs by injecting increasingly large volumes of fluid and proppant to maximize oil production while minimizing costs. However, wells that received larger treatments did not always perform as expected. Furthermore, the aggregated impact of various interrelated completion design parameters and reservoir characteristics was not fully understood, leaving room for improvement in completion optimization evaluations. Publicly accessible completion and production data, meticulously collected by the North Dakota Industrial Commission (NDIC), combined with advancements in data science, have created an excellent opportunity to optimize drilling and completion strategies using statistical data analysis and predictive modeling. Observations and experiences from thousands of producing Bakken wells can now be analyzed and interpreted using data mining techniques. This article presents examples of data science applications in completion optimization calculations in Bakken production. Completion Design Optimization Using Predictive Modeling The initial optimization study analyzed drilling and completion results from more than 12,000 oil-producing wells across the Bakken in North Dakota using 2020 data (URTeC 3723843). Simple interpretations of the relationship between production performance and completion parameters, based on bivariate (two-dimensional) scatterplots, proved challenging due to the nonlinear nature of these dependencies. Data-mining techniques capable of accommodating nonlinear relationships and complex, incomplete information were applied to identify optimal completion practices. The dataset used in this analysis included eight publicly available completion design parameters: 1. Perforated interval 2. Injected fluid volume 3. Proppant amount 4. Stage count 5. Injection rate 6. Injection pressure 7. Proppant type 8. Completion type These parameters were used to predict well performance, measured by cumulative 6-month oil production. Predictive modeling was performed using the gradient boosting (GB) data-mining tool, in which multiple decision trees described the variability of the multidimensional dataset. The initial predictive modeling conducted for the entire Bakken play demonstrated acceptable performance for the training model (evaluated by R²), but the test model—using 20% of all data—showed weaker performance. This phenomenon, known as overfitting, occurred when a single statistical model struggled to accurately predict well production performance across various Bakken locations which are characterized by heterogeneous geology and reservoir properties.
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Wang, Jiehui, and Juanli Guo. "MULTIOBJECTIVE OPTIMIZATION OF THE INTERGENERATIONAL RESIDENTIAL SPACE WITH THE GOAL OF DAYLIGHTING AND THERMAL COMFORT." Journal of Green Building 18, no. 2 (2023): 225–44. http://dx.doi.org/10.3992/jgb.18.2.225.
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ABSTRACT (1) Background: An ageing population and two-child policy have led to the transformation of China’s family structure, and multigenerational residences account for an increasing proportion of mainstream family residences. Different generations of residents have great differences in behaviour patterns and health requirements, but existing residential buildings are not especially designed for health needs. (2) Methods: First, based on relevant codes and a questionnaire survey, the spatial needs and behaviour patterns of different generations of residents are obtained, and the benchmark model is established based on the Grasshopper (GH) parametric platform. Then, based on the GH platform and the building simulation plug-in, which are Ladybug Tools and a multiobjective optimization tool named Wallacei, daylight autonomy (DA), useful daylight illuminance (UDI), and the proportion of thermal discomfort hours (PDH) are taken as daylighting and thermal comfort indicators, and the bedroom location, orientation, bay size, depth to bay ratio, window form, window-to-wall ratio, and horizontal shading width are optimized. Finally, the Pareto front is analysed by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) comprehensive evaluation method, and the optimal solution is compared with existing multigenerational residences in Tianjin. (3) Results: First, the middle room, oriented south or south by east, large bay, small window-to-wall ratio, and bay window can effectively be improved for indoor thermal comfort, while rooms that are oriented south or south by east, small depth bay ratio, large window-to-wall ratio and balcony can effectively be improved for daylighting quality. Second, compared with existing buildings in Tianjin, the DA, UDI, and PDH of the adult bedroom with the ideal solution are increased by 33.7%, 3.5%, and 10.8%, respectively; the DA, UDI, and PDH of the child bedroom with the ideal solution are increased by 15.5%, 4.2%, and 4.9%, respectively; and the DA, UDI, and PDH of the elderly individual bedroom with the ideal solution are increased by 42.7%, 4.9%, and 1.7%, respectively. (4) Conclusions: The optimization scheme is substantially improved for the health of the indoor daylight and thermal environment of existing housing, and it provides a scientific and quantitative decision-making basis for the healthy design of multigenerational residences.
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Van Pham, Vuong, Ebrahim Fathi, and Fatemeh Belyadi. "New Hybrid Approach for Developing Automated Machine Learning Workflows: A Real Case Application in Evaluation of Marcellus Shale Gas Production." Fuels 2, no. 3 (2021): 286–303. http://dx.doi.org/10.3390/fuels2030017.
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The success of machine learning (ML) techniques implemented in different industries heavily rely on operator expertise and domain knowledge, which is used in manually choosing an algorithm and setting up the specific algorithm parameters for a problem. Due to the manual nature of model selection and parameter tuning, it is impossible to quantify or evaluate the quality of this manual process, which in turn limits the ability to perform comparison studies between different algorithms. In this study, we propose a new hybrid approach for developing machine learning workflows to help automated algorithm selection and hyperparameter optimization. The proposed approach provides a robust, reproducible, and unbiased workflow that can be quantified and validated using different scoring metrics. We have used the most common workflows implemented in the application of artificial intelligence (AI) and ML in engineering problems including grid/random search, Bayesian search and optimization, genetic programming, and compared that with our new hybrid approach that includes the integration of Tree-based Pipeline Optimization Tool (TPOT) and Bayesian optimization. The performance of each workflow is quantified using different scoring metrics such as Pearson correlation (i.e., R2 correlation) and Mean Square Error (i.e., MSE). For this purpose, actual field data obtained from 1567 gas wells in Marcellus Shale, with 121 features from reservoir, drilling, completion, stimulation, and operation is tested using different proposed workflows. A proposed new hybrid workflow is then used to evaluate the type well used for evaluation of Marcellus shale gas production. In conclusion, our automated hybrid approach showed significant improvement in comparison to other proposed workflows using both scoring matrices. The new hybrid approach provides a practical tool that supports the automated model and hyperparameter selection, which is tested using real field data that can be implemented in solving different engineering problems using artificial intelligence and machine learning. The new hybrid model is tested in a real field and compared with conventional type wells developed by field engineers. It is found that the type well of the field is very close to P50 predictions of the field, which shows great success in the completion design of the field performed by field engineers. It also shows that the field average production could have been improved by 8% if shorter cluster spacing and higher proppant loading per cluster were used during the frac jobs.
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Wang, Yanyan, Rongjun Man, Xiaofeng Zhao, and Hui Liu. "Modeling of Parallel Movement for Deep-Lane Unit Load Autonomous Shuttle and Stacker Crane Warehousing Systems." Processes 8, no. 1 (2020): 80. http://dx.doi.org/10.3390/pr8010080.
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The autonomous shuttle and stacker crane (AC/SC) warehousing system, as a new automated deep-lane unit load storage/retrieval system, has been becoming more popular, especially for batch order fulfilment because of its high flexibility, low operational cost and improved storage capacity. This system consists of a shuttle sub-system that controls motion along the x-axis and a stacker crane sub-system that controls motion along the y-axis and z-axis. The combination of shuttles and a stacker crane performs storage and retrieval tasks. Modelling the parallel motion is an important design tool that can be used to calculate the optimal number of shuttles for a given configuration of the warehousing system. In this study, shuttle movements from one lane to another are inserted into the stock-keeping unit (SKU) task queue, and convert such that they are consistent with the retrieval tasks. The tasks are then grouped according to their starting lane, and converted to an assembly-line parallel job problem by analysing the operating mode with the objectives of minimising the total working time of the stacker crane and the wasted shuttle time. A time sequence mathematical model based on the motion of the shuttles and stacker crane is proposed, and an improved Pareto-optimal elitist non-dominated sorting genetic algorithm is used to solve this multi-objective optimization problem. The model is validated via a simulation study, and via a real-world warehousing case study. We go on to describe guidelines for the layout and configuration of AS/SC warehousing systems, including the optimal number of shuttles and number of x-axis storage cells of lanes, which can improve efficiency and minimise both capital investment and operating costs.
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Korzachenko, Mykola. "Application of the lean-construction concept in energy management." PROBLEMS AND PROSPECTS OF ECONOMIC AND MANAGEMENT, no. 4 (40) (December 30, 2024): 111–18. https://doi.org/10.25140/2411-5215-2024-4(40)-111-118.
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Energy consumption constitutes a significant share of costs in industry, transportation, buildings, and households. Efficient energy use contributes to cost reduction. Increasing energy availability supports economic growth, the creation of new technologies, and job opportunities. Efficient energy utilization helps reduce dependence on limited natural resources. The design and construction of energy-efficient buildings are among the most critical issues in the modern world. The effective application of energy resources through innovative solutions is key to addressing this challenge. In Ukraine, a comprehensive approach to the thermal modernization of old facilities has not been developed. Most buildings are simply insulated externally, neglecting areas such as the basement, attic, entrance doors, windows, heating, and ventilation systems, among others. This article examines the implementation of the Lean-Construction concept through the example of an energy audit and thermal modernization works at a hospital in Chernihiv. Lean-Construction is an effective tool for introducing cost-efficient and energy-efficient solutions in construction and reconstruction. The optimization process during building modernization should focus on identifying «weak points» and subsequently implementing appropriate technologies according to specific needs. The work outlines the main principles of lean construction and their application in energy management. Combined with modern energy management technologies, the proposed approach enables significant cost reductions, increased productivity, and environmental sustainability. For the studied facility, procedures for operation and maintenance have been provided. These involve monitoring the integrity of insulation and finishing materials, cleaning systems, ensuring the integrity and airtightness of window and door structures, maintaining sealing materials, ensuring the functionality of automatic door-closing systems, and controlling lighting devices, heating systems, and ventilation systems. Stages for equipping a dispatching system for the facility have also been developed.
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Rajan, S. D., S. W. Chin, and L. Gani. "Toward a Practical Design Optimization Tool." Computer-Aided Civil and Infrastructure Engineering 11, no. 4 (1996): 259–74. http://dx.doi.org/10.1111/j.1467-8667.1996.tb00328.x.
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Mihelič, Aleš, and Boris Štok. "Tool design optimization in extrusion processes." Computers & Structures 68, no. 1-3 (1998): 283–93. http://dx.doi.org/10.1016/s0045-7949(98)00019-4.
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Esping, B. "Design optimization as an engineering tool." Structural Optimization 10, no. 3-4 (1995): 137–52. http://dx.doi.org/10.1007/bf01742585.
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Guo, Yin Sai, Yi Zhang, and Ming Ke Cheng. "Optimization Design of Excavator Working Device." Applied Mechanics and Materials 644-650 (September 2014): 610–13. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.610.
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It has heavy workload, imprecision, not intuition, and other shortcomings by utilizing the traditional methods to optimize excavator working device (bucket). Structural optimization of main working parts excavator (bucket) is performed by finite element analysis technology. Defect of bucket is found out on the basis of static analysis results. Optimization scheme is put forward to meet the job requirements.
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Yudha, Achmad, Roni Kambara, and Dibyantoro Dibyantoro. "Performance optimization through the dimension of job satisfaction." JPPI (Jurnal Penelitian Pendidikan Indonesia) 10, no. 1 (2024): 451. https://doi.org/10.29210/020242412.
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This study attempts to explore more deeply the correlation between the effect of job involvement, digital work design, and IPC organizational culture on the main context of employee performance using job satisfaction as the main mediator.This method uses a quantitative approach with data collection through questionnaires to measure employee perceptions with an interval scale of 1-100 to organic employees, the sampling technique uses simplerandom sampling and data processing using the SEM-PLS instrument.Research findings describe a positive correlation between job involvement on direct performance as well as engagement, and IPC organizational culture on satisfaction. While digital work design does not affect satisfaction and satisfaction is found to not affect the performance context.This study was only conducted on tinplate manufacturing employees in the short term, future studies need to explore further using a longitudinal design approach with a more complex research locus.
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Yudha, Achmad, Roni Kambara, and Dibyantoro Dibyantoro. "Performance Optimization Through The Dimension Of Job Satisfaction." Cakrawala Repositori IMWI 6, no. 4 (2023): 813–23. http://dx.doi.org/10.52851/cakrawala.v6i4.419.
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This study attempts to explore more deeply the correlation between the effect of job involvement, digital work design, and IPC organizational culture on the main context of employee performance using job satisfaction as the main mediator. This method uses a quantitative approach with data collection through questionnaires to measure employee perceptions with an interval scale of 1-100 to organic employees, the sampling technique uses simple random sampling and data processing using the SEM-PLS instrument. Research findings describe a positive correlation between job involvement on direct performance as well as engagement, and IPC organizational culture on satisfaction. While digital work design does not affect satisfaction and satisfaction is found to not affect the performance context. This study was only conducted on tinplate manufacturing employees in the short term, future studies need to explore further using a longitudinal design approach with a more complex research locus.
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Neeraja, Ch, and Ch Divya Bharathi. "Design And Optimization of Injection Mould Tool." IOSR Journal of Mechanical and Civil Engineering 16, no. 053 (2016): 36–43. http://dx.doi.org/10.9790/1684-16053043643.
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Economikos, L., and R. Sherif. "Design optimization of a laser bond tool." IEEE Transactions on Components, Packaging, and Manufacturing Technology: Part A 19, no. 2 (1996): 234–39. http://dx.doi.org/10.1109/95.506109.
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Mihailov, D. N., E. I. Knyazev, I. V. Borodin, V. A. Blinov, Y. E. Dzhalatyan, and U. S. Ramazanov. "DIGITAL SOLUTION FOR OPTIMIZATION OF FORMATION TESTING DESIGN." Petroleum Engineering 22, no. 4 (2024): 72–82. http://dx.doi.org/10.17122/ngdelo-2024-4-72-82.
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Evaluation of pumping time to receive a clean sample during fluid sampling job with wireline formation tester as well as evaluation of pumping scenarios to minimize sampling duration under existing constrains (reservoir and sandface constrains, safety etc.) are actual problems especially for fluid sampling in low-permeability formation. The article describes the digital solution allowing to simulate mud filtrate invasion in near-wellbore zone during well drilling and mud filtrate displacement by reservoir fluid during fluid sampling job with taking into account a formation damage dynamics. In this way, the software is able to evaluate depth of mud filtrate invasion and simulate both the multiphase inflow dynamics and the skin-factor dynamics during sampling job and predict sampling duration. To take into account a complex, essentially non-1D geometry of multiphase fluid flow in near-wellbore zone during fluid sampling, the special upscaling technique was developed. This upscaling technique is based on modification of 1D model by introduction of «effective» mud filtrate invasion profile and «geometry factor» which depend on ratio of vertical to horizontal permeabilities. The correlations between empirical model parameters and ratio of vertical to horizontal permeabilities were obtained by matching results of «pseudo 2D» simulations of pumping dynamics with results of 2D / 3D simulations conducted using commercial simulators ECLIPSE® and COMSOL Multiphysics®. The developed «pseudo 2D» model is computationally fast ones that allows to effectively conduct sensitivity study as well as adjust the model parameters and sampling scenario in real time in accordance with the real-time wireline formation tester measurements.
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Bin, Zeng, and Jing Zhang. "Design of a Resource Scheduling Tool for Production Project Management." Applied Mechanics and Materials 220-223 (November 2012): 165–68. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.165.
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Production planning is clearly needed in all manufacturing systems, and that is also the case for job shops. When dealing with a small number of products or jobs repetitive scheduling becomes a major issue. In this situation, the same tasks are performed sequentially and repeated from job to job. To help with this problem, a resource scheduling tool is developed that would work alongside Microsoft Project. This tool should act as a front end to Microsoft Project and allow for the creation of a schedule with a minimal amount of work for the scheduling manager. Due to the repetitive nature of production processes, the tool should be able to eliminate most of the manual scheduling currently done exclusively in MS Project.
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Panjabrao Raut, Mr Pritesh. "Optimization of Tool steel." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47693.
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Abstract Electro Discharge Machining (EDM) is a widely adopted non-traditional machining process used for shaping hard and electrically conductive materials, particularly tool steels. The present study focuses on optimizing the EDM process parameters for tool steel (D3) using a Grey-based Taguchi method to achieve improved machining performance. The primary objectives are to maximize the Material Removal Rate (MRR) while minimizing the surface roughness (Ra). Experiments were conducted on an ELECTRAPULS PS 35 EDM machine, utilizing copper electrodes with varying process parameters: voltage, current, and pulse-on time. A Taguchi L9 orthogonal array was employed to design the experiments. Grey Relational Analysis (GRA) was then applied to convert multi-response performance characteristics into a single Grey Relational Grade (GRG), facilitating multi-objective optimization. The results revealed that the optimal combination of process parameters for multi-performance characteristics was 70 V, 18 A, and 150 μs pulse-on time. The study confirms the effectiveness of integrating Taguchi design and GRA for simultaneous optimization of MRR and surface roughness in EDM, providing valuable insights for industrial applications and future research in advanced machining processes. Keywords: Electro Discharge Machining (EDM), Tool Steel (D3), Material Removal Rate (MRR), Surface Roughness (Ra), Grey Relational Analysis (GRA)
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Xu, Xiu Fen. "Design of Machine Tool Setting and Parameter Optimization." Advanced Materials Research 706-708 (June 2013): 1132–35. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.1132.
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To solve the existing problems in the NC machining process, the optimization design of cutting parameters in NC milling machine with genetic algorithm. With the maximum production efficiency as the optimization objective, the spindle speed, feed speed, milling width, depth and other parameters as optimal variables, establishes the optimization mathematical model of machine tool. The optimization results show that: parameters optimization can significantly improve the processing efficiency, and bring economic benefits for enterprises
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Li, Zi Sheng, Cao Jian, and Jiu Yi Xiao. "Optimization Design for Production Logistics System of Corrugated Box." Advanced Materials Research 463-464 (February 2012): 962–66. http://dx.doi.org/10.4028/www.scientific.net/amr.463-464.962.
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The existing problems of production logistics system of corrugated box are pointed out. The theory of SLP (Systematic Layout Planning) within facility planning and logistics analysis is employed to optimize the facility layout, production logistics system and working procedures. After analyzes the processes and its parameters of corrugated box, logistics amount is computed, and from-to table is obtained and logistics intensity is counted. And then job units relationship, non logistics factors, and comprehensive relationship for job units etc. are studied. Finally the workshop layout of corrugated box is designed. Moreover, the optimal solution is evaluated by using weighted checklist approach. Application of the new solution shows that the working procedures of corrugated box become more reasonable, and the workshop attains the logistics smooth, so the production efficiency of corrugated box is enhanced, and its production cost is lowered.
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Zhang, Xu, Shilong Wang, Lili Yi, Hong Xue, Songsong Yang, and Xin Xiong. "An integrated ant colony optimization algorithm to solve job allocating and tool scheduling problem." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 232, no. 1 (2016): 172–82. http://dx.doi.org/10.1177/0954405416636038.
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In this article, max–min ant colony optimization algorithm is proposed to determine how to allocate jobs and schedule tools with the objective of minimizing the makespan of processing plans in flexible manufacturing system. To expand the application range of max–min ant colony optimization algorithm, tool movement policy is selected as the running mode of flexible manufacturing system, which assumes that tools are shared among work centers and each operation is allowed to be machined by different kinds of tools. In the process of converting this scheduling problem into traveling salesman problem, disjunctive graph is modified to possess more than one path between each neighbor node. Besides providing practical methods of initializing pheromone, selecting node and calculating pheromone increment, max–min ant colony optimization algorithm employs the pheromone updating rule in max–min ant system to limit pheromone amount in a range, of which the upper and lower boundaries are updated after each iteration by formulations involving the current optimal makespan, the average number of optional tools and parameters. Finally, different sizes of processing plans are randomly generated, through which max–min ant colony optimization algorithm is proved effectively to tackle early stagnation and local convergence and thus obtains better solution than ant colony optimization algorithm and bidirectional convergence ant colony optimization algorithm.
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Liu, Shihao, Yanbin Du, and Mao Lin. "Study on lightweight structural optimization design system for gantry machine tool." Concurrent Engineering 27, no. 2 (2019): 170–85. http://dx.doi.org/10.1177/1063293x19832940.
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In order to improve the efficiency and effectiveness of the lightweight design of the gantry machine tool, a lightweight structural optimization design system for the gantry machine tool was constructed. Serialized gantry machine tools were parametrically modeled, and a load model with multiple operating conditions was established. A twice optimization design method integrating zero-order optimization, parameter rounding, and structural re-optimization was proposed. Using the proposed method, a lightweight structural optimization design system for gantry machine tool with parametric design, lightweight design, and other functions was developed. The developed gantry machine tool lightweight structural optimization design system was applied to complete the lightweight structural optimization design of gantry frame of a certain gantry machine tool, so the structural parameters of the gantry frame were optimized. Although the maximum stress and the maximum deformation of the gantry frame increases within the allowable range, the experimental comparison before and after the optimization shows that the mass of the whole gantry frame is reduced by 9.24%, which is beneficial to save the manufacturing cost. The research results show that the constructed lightweight structural optimization design system of the gantry machine tool has high engineering practicality.
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Shimada, Takumi, and Haruhiko Suwa. "Optimization of Cutting Tool Allocation to Enhance Workload Balance and Total Completion Time in Parallel-Type FMS." International Journal of Automation Technology 15, no. 6 (2021): 804–12. http://dx.doi.org/10.20965/ijat.2021.p0804.
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This study aims to build a machine scheduling method that involves the cutting tool management in parallel-type flexible manufacturing systems. These systems consist of multi-axis CNC machine tools and are equipped with an automated tool changer and a large-capacity tool magazine. The target scheduling problem could be described as a multi-objective parallel scheduling problem. We consider the availability of cutting tools stored in the magazine as so-called “machine eligibility,” and propose a two-phase scheduling method for tool allocation and job sequencing on machines to minimize the workload balance between machines and the total completion time. Two mathematical models for tool allocation are provided: a machine-eligibility-based model and an enhanced version of the model that considers each cutting tool. A series of computational experiments demonstrates the effectiveness of the proposed method. We also clarify the relationship between schedule performance measures and job routing flexibility in the system.
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Abdel-Malek, K., Z. Mi, J. Yang, and K. Nebel. "Optimization-Based Layout Design." Applied Bionics and Biomechanics 2, no. 3-4 (2005): 187–96. http://dx.doi.org/10.1155/2005/285756.
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The layout problem is of importance to ergonomists, vehicle/cockpit packaging engineers, designers of manufacturing assembly lines, designers concerned with the placement of levers, knobs, controls, etc. in the reachable workspace of a human, and also to users of digital human modeling code, where digital prototyping has become a valuable tool. This paper proposes a hybrid optimization method (gradient-based optimization and simulated annealing) to obtain the layout design. We implemented the proposed algorithm for a project at Oral-B Laboratories, where a manufacturing cell involves an operator who handles three objects, some with the left hand, others with the right hand.
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Hatti, Santosh Kumar. "Efficient Resume Screening Tool." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 483–88. https://doi.org/10.22214/ijraset.2025.66307.
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The "Efficient Resume Screening Tool" employs Natural Language Processing (NLP) and Machine Learning (ML) to transform the traditional resume screening process. By automating the analysis of resumes, it identifies candidates best suited for specific job roles, significantly reducing the time and effort associated with manual screening while improving accuracy and consistency. The tool incorporates features such as skill categorization, web-based visualization, and automated report generation, catering to diverse recruitment needs. Using advanced NLP techniques, it extracts, analyzes, and ranks candidate information against predefined job criteria. Machine Learning models ensure adaptability and continuous improvement based on evolving job requirements and industry trends. A user-friendly interface provides recruiters with real-time insights through visual dashboards, simplifying decision-making and ensuring unbiased candidate evaluation. This paper outlines the methodology, system design, implementation, and performance evaluation, demonstrating how the tool enhances screening efficiency and addresses modern recruitment challenges.
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Payne, T. A., J. A. Turner, K. Rijswijk, A. K. Mcdermott, and R. D. N. Wakelin. "Informing extension project design: the right tool for the job." NZGA: Research and Practice Series 16 (January 1, 2016): 33–38. http://dx.doi.org/10.33584/rps.16.2016.3257.
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Extension successfully creates change when projects are designed to fit the issue, opportunity, or technology to be implemented, the potential users, and the influences external to the farm team. To better understand when and why different approaches to extension succeed this study developed a typology of nine extension approaches. The nine approaches provide a framework to identify the right extension approaches that will drive sustainable productivity improvements for higher onfarm profitability. Factors that need to be understood when deciding which of the extension approaches is best suited to a particular circumstance are: farmers' awareness of the problem, problem complexity, benefits desired by farmers, compatibility of the new technology with existing farm management, and the ability of the farm team to implement the technology. This paper outlines the 9 extension approaches, and the criteria and process for choosing the appropriate approach for the circumstances. Keywords: extension approaches, technology transfer, co-development
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Lending, Diane, and Norman L. Chervany. "Case Tool Use and Job Design: A Restrictiveness/Flexibility Explanation." Journal of Computer Information Systems 43, no. 1 (2002): 81–90. http://dx.doi.org/10.1080/08874417.2002.11647073.
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Banerjee, Pallab, Sharmistha Roy, Umar Muhammad Modibbo, et al. "OptiDJS+: A Next-Generation Enhanced Dynamic Johnson Sequencing Algorithm for Efficient Resource Scheduling in Distributed Overloading within Cloud Computing Environment." Electronics 12, no. 19 (2023): 4123. http://dx.doi.org/10.3390/electronics12194123.
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The continuously evolving world of cloud computing presents new challenges in resource allocation as dispersed systems struggle with overloaded conditions. In this regard, we introduce OptiDJS+, a cutting-edge enhanced dynamic Johnson sequencing algorithm made to successfully handle resource scheduling challenges in cloud computing settings. With a solid foundation in the dynamic Johnson sequencing algorithm, OptiDJS+ builds upon it to suit the demands of modern cloud infrastructures. OptiDJS+ makes use of sophisticated optimization algorithms, heuristic approaches, and adaptive mechanisms to improve resource allocation, workload distribution, and task scheduling. To obtain the best performance, this strategy uses historical data, dynamic resource reconfiguration, and adaptation to changing workloads. It accomplishes this by utilizing real-time monitoring and machine learning. It takes factors like load balance and make-up into account. We outline the design philosophies, implementation specifics, and empirical assessments of OptiDJS+ in this work. Through rigorous testing and benchmarking against cutting-edge scheduling algorithms, we show the better performance and resilience of OptiDJS+ in terms of reaction times, resource utilization, and scalability. The outcomes underline its success in reducing resource contention and raising service quality generally in cloud computing environments. In contexts where there is distributed overloading, OptiDJS+ offers a significant advancement in the search for effective resource scheduling solutions. Its versatility, optimization skills, and improved decision-making procedures make it a viable tool for tackling the resource allocation issues that cloud service providers and consumers encounter daily. We think that OptiDJS+ opens the way for more dependable and effective cloud computing ecosystems, assisting in the full realization of cloud technologies’ promises across a range of application areas. In order to use the OptiDJS+ Johnson sequencing algorithm for cloud computing task scheduling, we provide a two-step procedure. After examining the links between the jobs, we generate a Gantt chart. The Gantt chart graph is then changed into a two-machine OptiDJS+ Johnson sequencing problem by assigning tasks to servers. The OptiDJS+ dynamic Johnson sequencing approach is then used to minimize the time span and find the best sequence of operations on each server. Through extensive simulations and testing, we evaluate the performance of our proposed OptiDJS+ dynamic Johnson sequencing approach with two servers to that of current scheduling techniques. The results demonstrate that our technique greatly improves performance in terms of makespan reduction and resource utilization. The recommended approach also demonstrates its ability to scale and is effective at resolving challenging work scheduling problems in cloud computing environments.
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Chibane, Hicham, Sébastien Dubois, and Roland De Guio. "Innovation beyond optimization: Application to cutting tool design." Computers & Industrial Engineering 154 (April 2021): 107139. http://dx.doi.org/10.1016/j.cie.2021.107139.
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Morrell, Benjamin J., David J. Munk, Gareth A. Vio, and Dries Verstraete. "Development of a Hypersonic Aircraft Design Optimization Tool." Applied Mechanics and Materials 553 (May 2014): 847–52. http://dx.doi.org/10.4028/www.scientific.net/amm.553.847.
Full textAbstract:
The design and optimization of hypersonic aircraft is severely impacted by the high temperatures encountered during flight as they can lead to high thermal stresses and a significant reduction in material strength and stiffness. This reduction in rigidity of the structure requires innovative structural concepts and a stronger focus on aeroelastic deformations in the early design and optimisation of the aircraft structure. This imposes the need for a closer coupling of the aerodynamic and structural design tools than is current practice. The paper presents the development of a multi-disciplinary, closely coupled optimisation suite for hypersonic aircraft. An overview of the setup and structure of the optimization suite is given and the integration between the Tranair solver, used to determine the aerodynamic loads and temperatures, and MSC/NASTRAN, used for the structural sizing and design, will be given.