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Current Issues in Flexible Manufacturing Using Multicriteria Decision Analysis and Ontology Based Interoperability in an Advanced Manufacturing Environment 10th

Current Issues in Flexible Manufacturing Using Multicriteria...

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10th International Conference of Production Research - Americas, ICPR-Americas 2020

作者： Canciglieri, M.B. Leite, A. F. C. S. de M. Rocha Loures, E. de F. Canciglieri, O. Monfared, R.P. Goh, Y.M. Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná Curitiba Paraná Brazil School of Mechanical Electrical and Manufacturing Engineering Loughborough University Loughborough Leicestershire United Kingdom

ISBN: (纸本)9783030763060

The manufacturing industry is undergoing a major transformation based on the emerging industry 4.0 technologies, such as cloud computing, big data, internet of things and cyber-physical systems. These novelty technologies aim at providing central management for the user’s flexible manufacturing requirements and information. Also, the advent of these technologies has transformed the process planning and became crucial for the building of knowledge-based process planning environments. However, current praxis cannot deal with all semantic issues within this new paradigm, as requirements must be clear, consistent, measurable, stand-alone, testable, unambiguous, unique and verifiable. In this context, multicriteria decision analysis models have gained focus of the scientific and industrial communities as a support tool for the decision-making process in the product development and advanced manufacturing as these processes excel in environments with numerous and conflicting alternatives, providing the optimal alternative. Therefore, the main objective of this research is to highlight the current issues and research tendencies regarding ontology-based interoperability systems, multicriteria decision analysis and their integration. To achieve this goal, it will be applied a literature review on the targeted technologies, discussing the current tendencies of the field and the main issues regarding their implementation and integration. Finally, the paper points themes for further research and indicates viable concepts that can compose a solution for the gaps in a systematic manner. © 2021, Springer Nature Switzerland AG.

关键词： Flexible manufacturing systems

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Blockchain: A Review from the Perspective of Operations Researchers 22

Blockchain: A Review from the Perspective of Operations Rese...

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Proceedings of the Winter Simulation Conference

作者： Hong Wan Kejun Li Yining Huang Department of Industrial and Systems Engineering North Carolina State University Raleigh NC USA Operations Research Graduate Program North Carolina State University Raleigh NC USA

Blockchain is a distributed append-only digital ledger. The technology has caught much attention since the emergence of cryptocurrency, and there is an increasing number of blockchain applications in various businesses. The concept, however, is still novel to many members of the simulation and operations research community. In this tutorial, we introduce the blockchain technology and review its frontier related research. There are exciting opportunities for researchers in simulation, system analysis, and data science.

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Graph Neural Networks for the Offline Nanosatellite Task Scheduling Problem

arXiv

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arXiv 2023年

作者： Pacheco, Bruno Machado Seman, Laio Oriel Rigo, Cezar Antônio Camponogara, Eduardo Bezerra, Eduardo Augusto dos Santos Coelho, Leandro Florianópolis Brazil Florianópolis Brazil Department of Electrical Engineering Federal University of Parana Curitiba Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Brazil

This study investigates how to schedule nanosatellite tasks more efficiently using Graph Neural Networks (GNNs). In the Offline Nanosatellite Task Scheduling (ONTS) problem, the goal is to find the optimal schedule for tasks to be carried out in orbit while taking into account Quality-of-Service (QoS) considerations such as priority, minimum and maximum activation events, execution time-frames, periods, and execution windows, as well as constraints on the satellite’s power resources and the complexity of energy harvesting and management. The ONTS problem has been approached using conventional mathematical formulations and exact methods, but their applicability to challenging cases of the problem is limited. This study examines the use of GNNs in this context, which has been effectively applied to optimization problems such as the traveling salesman, scheduling, and facility placement problems. More specifically, we investigate whether GNNs can learn the complex structure of the ONTS problem with respect to feasibility and optimality of candidate solutions. Furthermore, we evaluate using GNN-based heuristic solutions to provide better solutions (w.r.t. the objective value) to the ONTS problem and reduce the optimization cost. Our experiments show that GNNs are not only able to learn feasibility and optimality for instances of the ONTS problem, but they can generalize to harder instances than those seen during training. Furthermore, the GNN-based heuristics improved the expected objective value of the best solution found under the time limit in 45%, and reduced the expected time to find a feasible solution in 35%, when compared to the SCIP (Solving Constraint Integer programs) solver in its off-the-shelf configuration. © 2023, CC BY-NC-ND.

关键词： Energy harvesting

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Multi-Objective Grouped Grey Wolf Optimization of PID Controllers Applied to a Water Treatment Plant Model

Multi-Objective Grouped Grey Wolf Optimization of PID Contro...

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Congress on Evolutionary Computation

作者： Yan Lieven Souza Lúcio Viviana Cocco Mariani Leandro Dos Santos Coelho Electrical Engineering Graduate Program (PPGEE) Federal University of Parana (UFPR) Curitiba Parana Brazil Department of Electrical Engineering Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) and Federal University of Parana (UFPR) Curitiba Parana Brazil Department of Electrical Engineering Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) and Federal University of Parana (UFPR) Curitiba Parana Brazil

A comparison of proportional-integral-derivative (PID) controller tuning for a 2x2 Multiple-Input Multiple-Output (MIMO) water treatment plant model is presented. The PID tuning procedures are based on Multi-Objective Grey Wolf Optimizer (MOGWO) and Multi-Objective Grouped Grey Wolf Optimizer (MOGGWO) are performed under two different initial conditions. The tuning procedure is structured as a multi-objective optimization problem with four objective functions, the integral time-squared error is minimized in each output response, and the integral of the control actions is minimized in each input. MOGWO is a metaheuristic based on the hunting behavior of grey wolves, following the hierarchy and leadership procedures that these animals have, while the proposed MOGGWO is a new version proposed in this study with two different groups with different functionalities. The performance was analyzed by 50 runs based on statistical metrics. MOGGWO can find more local minima since its Pareto front is more dispersed than that found by MOGWO.

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Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem 5

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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5th International Conference on Cybernetics and Intelligent systems, ICORIS 2023

作者： Anditta, Mietha Amartya, Natashya Warnars, Laurens Spits Warnars, Harco Leslie Hendric Spits Ramadhan, Arief Siswanto, Teddy Mantoro, Teddy Noordin, Nurulhuda Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11530 Indonesia Bina Nusantara University School of Information Systems Information Systems Departement Jakarta11530 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Departement Jakarta11530 Indonesia Telkom University Pjj Informatika Bandung40257 Indonesia Trisakti University Information Systems Department Jakarta11440 Indonesia Sampoerna University Faculty of Engineering and Technology Jakarta12760 Indonesia Universiti Teknologi Mara School of Computing Sciences College of Computing Informatics and Mathematics Selangor Malaysia

ISBN: (纸本)9798350369489

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset. © 2023 IEEE.

关键词： Dynamic programming

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Generalization Bounds for Contextual Stochastic Optimization using Kernel Regression

arXiv

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arXiv 2024年

作者： Wang, Yijie Hanasusanto, Grani A. Ho, Chin Pang Graduate Program in Operations Research and Industrial Engineering The University of Texas at Austin AustinTX78712-1591 United States Department of Industrial and Enterprise Systems Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States School of Data Science City University of Hong Kong Hong Kong

In this paper, we consider contextual stochastic optimization using Nadaraya-Watson kernel regression, which is one of the most common approaches in nonparametric regression. Recent studies have explored the asymptotic convergence behavior of using Nadaraya-Watson kernel regression in contextual stochastic optimization;however, the performance guarantee under finite samples remains an open question. This paper derives a finite-sample generalization bound of the Nadaraya-Watson estimator with a spherical kernel under a generic loss function. Based on the generalization bound, we further establish a suboptimality bound for the solution of the Nadaraya-Watson approximation problem relative to the optimal solution. Finally, we derive the optimal kernel bandwidth and provide a sample complexity analysis of the Nadaraya-Watson approximation problem. © 2024, CC BY.

关键词： Optimization

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Analysis of Non-fatal Occupational Accidents in a Ready-mixed Concrete Company

Analysis of Non-fatal Occupational Accidents in a Ready-mixe...

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2022 IEEE International Conference on industrial engineering and engineering Management, IEEM 2022

作者： Prasetyo, Yogi Tri Sario, Maria Cristina Chuenyindee, Thanatorn Nadlifatin, Reny Persada, Satria Fadil Sittiwatethanasiri, Thaninrat Mapúa University School of Industrial Engineering and Engineering Management Manila Philippines Yuan Ze University Department of Industrial Engineering and Management Chung-Li Taiwan Mapúa University School of Graduate Studies Manila Philippines Navaminda Kasatriyadhiraj Royal Air Force Academy Department of Industrial Engineering and Aviation Management Bangkok Thailand Institut Teknologi Sepuluh Nopember Department of Information Systems Surabaya Indonesia Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta Indonesia

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

The production and delivery of ready-mixed concrete consist of several activities which expose employees to considerable safety hazards that lead to occupational accidents. The study aimed to determine the significant factors contributing to non-fatal occupational accidents in the ready-mixed concrete industry in Qatar. A total of 84 non-fatal occupational accidents were collected from a ready-mixed concrete company in Qatar. A coding scheme was developed based on available literature and the company's existing classification to analyze each accident in terms of employee, injury, and accident characteristics. Cross-tabulation analysis was used to determine the associations between categorical factors. Frequency distribution showed that regular employees aged between 25 and 44 years old were mostly involved in accidents, with hand/arm and leg/foot being mostly injured. The most common accident types were struck by an object (44%), slip, trip or fall on the same level (23.8%), and caught in between objects (17.9%). The majority of work-related accidents (71%) could be attributed to unsafe behavior, particularly due to improper use of PPE. Strong significant associations were observed between activity type and accident root cause, as well as injured body type and accident root cause. The significant factors contributing to non-fatal occupational accidents were determined through the study. The insights obtained from this study could be used by the company in designing a comprehensive safety management program with the goal of driving occupational injuries to a lower level. © 2022 IEEE.

关键词： Accidents

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The Impact of COVID-19 Pandemic on Airport: An Empirical Study of Service Quality, Customer Satisfaction, and Travel Intention for Sustainable Airport Operations

The Impact of COVID-19 Pandemic on Airport: An Empirical Stu...

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2022 IEEE International Conference on industrial engineering and engineering Management, IEEM 2022

作者： Prasetyo, Yogi Tri Fuente, Darlene Gayle D. Dela Chuenyindee, Thanatorn Nadlifatin, Reny Persada, Satria Fadil Mapúa University School of Industrial Engineering and Engineering Management Manila Philippines Yuan Ze University Department of Industrial Engineering and Management Chung-Li Taiwan Mapúa University School of Graduate Studies Manila Philippines Navaminda Kasatriyadhiraj Royal Air Force Academy Department of Industrial Engineering and Aviation Management Bangkok Thailand Institut Teknologi Sepuluh Nopember Department of Information Systems Surabaya Indonesia Bina Nusantara University Entrepreneurship Department Binus Business School Undergraduate Program Jakarta Indonesia

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Delivering a high service quality under the safety protocol of COVID-19 is very essential for the sustainable airport operations. The study was intended to determine the impact of COVID-19 on Airport Service Quality (ASQ), customer satisfaction, and travel intention by utilizing a structural equation modeling (SEM) approach. A total of 517 Filipinos voluntarily answered an online questionnaire that consists of 92 questions. SEM indicated that the security check, terminal facilities, and services had significant effects on perceived value which subsequently led to customer satisfaction. In addition, travel safety measures had direct effects on Filipinos' travel intention and customer satisfaction. Interestingly, service innovations had no significant impact on customer satisfaction but directly affected travel intention. By understanding the relationship between these factors, airport management could have better decision-making while efficiently and effectively utilizing the resources in these times of uncertainty. © 2022 IEEE.

关键词： COVID-19

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The Impact of the COVID-19 Pandemic on the Organizational Commitment in Semiconductor Industry: The Mediator Effect of the Job Satisfaction

The Impact of the COVID-19 Pandemic on the Organizational Co...

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2022 IEEE International Conference on industrial engineering and engineering Management, IEEM 2022

作者： Prasetyo, Yogi Tri Ong, Ardvin Kester S. Cagubcob, Alaissa Marie A. Chuenyindee, Thanatorn Nadlifatin, Reny Persada, Satria Fadil Mapúa University School of Industrial Engineering and Engineering Management Manila Philippines Yuan Ze University Department of Industrial Engineering and Management Chung-Li Taiwan Mapúa University School of Graduate Studies Manila Philippines Navaminda Kasatriyadhiraj Royal Air Force Academy Department of Industrial Engineering and Aviation Management Bangkok Thailand Institut Teknologi Sepuluh Nopember Department of Information Systems Surabaya Indonesia Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta Indonesia

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Due to the COVID-19 pandemic, employees are required to respond to changes and conform to stringent safety laws and regulations. This study aimed to analyze the impact of the COVID-19 pandemic on the organizational commitment among employees in the semiconductor industry. A total of 272 employees working in the semiconductor industry answered a self-administered questionnaire that considered 51 questions, distributed online. Utilizing Structural equation modeling (SEM), the results showed that the COVID-19 was found to have a significant direct effect on employees' perceived job outcomes (PO) and a negative direct effect on job demands (JD). Moreover, PO had a positive effect on job motivation (JM) and job satisfaction (JS). Subsequently, JM presented a significant positive direct effect on JS, while JD showed a negative effect on JS. Lastly, JS showed a significant positive direct effect on organizational commitment (OC). Intriguingly, an indirect effect of PO on JS was seen. This study is one of the first studies that analyzed the organization commitment among semiconductor workers during the COVID-19 pandemic. This paper could be utilized as a foundation to enhance organizational commitment, particularly in the semiconductor industry worldwide. © 2022 IEEE.

关键词： COVID-19

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A TWO-STAGE FEDERATED LEARNING APPROACH FOR industrial PROGNOSTICS USING LARGE-SCALE HIGH-DIMENSIONAL SIGNALS

arXiv

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arXiv 2024年

作者： Su, Yuqi Fang, Xiaolei Operations Research Graduate Program North Carolina State University RaleighNC United States Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University RaleighNC United States

industrial prognostics aims to develop data-driven methods that leverage high-dimensional degradation signals from assets to predict their failure times. The success of these models largely depends on the availability of substantial historical data for training. However, in practice, individual organizations often lack sufficient data to independently train reliable prognostic models, and privacy concerns prevent data sharing between organizations for collaborative model training. To overcome these challenges, this article proposes a statistical learning-based federated model that enables multiple organizations to jointly train a prognostic model while keeping their data local and secure. The proposed approach involves two key stages: federated dimension reduction and federated (log)-location-scale regression. In the first stage, we develop a federated randomized singular value decomposition algorithm for multivariate functional principal component analysis, which efficiently reduces the dimensionality of degradation signals while maintaining data privacy. The second stage proposes a federated parameter estimation algorithm for (log)-location-scale regression, allowing organizations to collaboratively estimate failure time distributions without sharing raw data. The proposed approach addresses the limitations of existing federated prognostic methods by using statistical learning techniques that perform well with smaller datasets and provide comprehensive failure time distributions. The effectiveness and practicality of the proposed model are validated using simulated data and a dataset from the NASA repository. © 2024, CC BY.

关键词： Principal component analysis

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