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Designing a cellular manufacturing system as a step toward creating sustainable cells under uncertain demand condition

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Journal of Remanufacturing 2025年第1期15卷 151-177页

作者： Almasarwah, Najat Abdelall, Esraa Bhutta, Mohammed Khurrum S. Saraireh, Mohammad Department of Industrial Systems Engineering Mutah University Alkarak 61710 Jordan Industrial Engineering Department Jordan University of Science and Technology P.O. Box 3030 Irbid 22110 Jordan Department of Management College of Business Ohio University Athens 45701 OH United States Department of Computer Engineering Mutah University Alkarak 61710 Jordan

This paper focuses on designing a cellular manufacturing system as a step toward creating sustainable cells. The proposed design aims to manufacture final products and remanufacture the returned products to diminish waste, where the return rate for each product is known. Firstly, the product families are formed using the uncapacitated mathematical model. Secondly, a nonlinear stochastic mathematical model is utilized for the propounded design as a step toward creating sustainable cells, where machine duplications are allowed. Besides, a simulation model is developed to handle the uncertainty in demand. Further, the performance of the proposed design with flexible cells is compared to regular cells. The results explain the ability of the proposed design to minimize the total number of machines. Furthermore, allowing machine duplication in the proposed design enhances the system flexibility;thus, machine breakdown will not impact system performance. The total number of machines required to cover the demand increases as the risk level of demand cover decreases. Therefore, the average waiting time and average work in process increase. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Designing a cellular manufacturing system as a step toward creating sustainable cells Flexible cells Nonlinear mathematical model Simulation Stochastic demand

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Extended warranty pricing in a competitive aftermarket under logit demand

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European Journal of Operational Research 2025年第3期325卷 541-552页

作者： Wang, Xiao-Lin Peng, Shizhe Zhang, Xiaoge Business School Sichuan University Chengdu610065 China School of Economics and Management Changsha University of Science and Technology Changsha410114 China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong

It is common for multiple firms—such as manufacturers, retailers, and third-party insurers—to coexist and compete in the aftermarket for durable products. In this paper, we study price competition in a partially concentrated aftermarket where one firm offers multiple extended warranty (EW) contracts while the others offer a single one. The demand for EWs is described by the multinomial logit model. We show that, at equilibrium, such an aftermarket behaves like a combination of monopoly and oligopoly. Building upon this base model, we further investigate sequential pricing games for a durable product and its EWs to accommodate the ancillary nature of after-sales services. We consider two scenarios: one where the manufacturer (as the market leader) sets product and EW prices simultaneously, and another where these decisions are made sequentially. Our analysis demonstrates that offering EWs incentivizes the manufacturer to lower the product price, thereby expanding the market potential for EWs. Simultaneous product–EW pricing leads to a price concession on EWs compared to sequential pricing, effectively reducing the intensity of competition in the aftermarket. Overall, the competitiveness of an EW hinges on its ability to deliver high value to consumers at low marginal cost to its provider. While our focus is on EWs, the proposed game-theoretical pricing models apply broadly to other ancillary after-sales services. © 2025 Elsevier B.V.

关键词： After-sales service Market-share redistribution Multinomial logit Pricing Simultaneous/sequential game

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Mining Quantitative Association Rules 17th

Mining Quantitative Association Rules

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17th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024

作者： Yen, Show-Jane Lee, Yue-Shi Lee, Wen-Hao Department of Computer Science and Information Engineering Ming Chuan University Taipei Taiwan Department of Management Information Systems National Chiayi University Chiayi City Taiwan

ISBN: (纸本)9789819606948

Mining quantitative association rules involves identifying the relationships between items based on their purchased quantities from a transaction database. In general, items with varying quantities are treated as distinct new items, making it challenging to meet the criteria for these new items. Previous methods have often relaxed these criteria to uncover quantitative association rules;however, excessive relaxation can lead to the discovery of many irrelevant rules, while insufficient relaxation may result in few or no rules being found. Additionally, the quantity ranges associated with items can be divided into intervals, and some of these intervals need to be combined, potentially leading to information loss during the process. In this article, we present new definitions and an approach for discovering quantitative association rules that address the issues of user-defined criteria, range segmentation, and interval integration, allowing us to identify the quantitative association rules that genuinely interest users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data mining

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Industry 5.0 in Healthcare: An Integrated Framework for Human-Centered Prosthetics Design and Manufacturing

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Procedia Computer Science 2025年 253卷 3288-3297页

作者： Giuseppina Ambrogio Carmine Borgia Marco Bortolini Gerardo Catapano Domenico Conforti Luigi De Napoli Francesco Gagliardi Francesco Gabriele Galizia Rosita Guido Francesco Longo Federica Mirabelli Giovanni Mirabelli Domenico Mundo Alberto Regattieri Maria Rosaria Saffioti Gabriele Scordamaglia Vittorio Solina Domenico Umbrello Pierpaolo Veltri University of Calabria Department of Mechanical Engineering Energy Engineering and Management Engineering Italy University of Bologna Department of Industrial Engineering Italy University of Calabria Department of Computer Engineering Modeling Electronics and Systems Engineering Italy

Industry 5.0 emphasizes human-centric approaches in technological advancement, particularly crucial in healthcare applications such as prosthetics development. While individual technologies like additive manufacturing, machine learning, digital twins, and tissue engineering have shown promise in prosthetics innovation, their integration remains a significant challenge. This paper presents a novel multidisciplinary framework developed through the National Centre for High-Performance Computing (CN-HPC) project, which addresses the gap between individual technological capabilities and their coordinated application in personalized prosthetics development. The framework, implemented through collaboration between six research groups from two universities, employs the House of Quality methodology to align technological integration with patient needs. Our approach encompasses a comprehensive seven-step process, from initial patient requirements definition through virtual modeling, production process optimization, virtual sensing, machine learning enhancement, digital twin simulation, to advanced tissue engineering. A data model facilitates seamless information exchange between different technological domains and research groups. This research contributes to the field by providing a scalable, human-centered framework for technology integration in medical device development, potentially extending beyond prosthetics to other healthcare applications. The proposed framework represents a significant step toward realizing the full potential of Industry 5.0 in healthcare manufacturing through the systematic integration of advanced technologies for delivering personalized prosthetic solutions.

关键词： Industry 5.0 Human-Centricity Prosthesis Healthcare House of Quality

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Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation

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ACM Transactions on Privacy and Security 2025年第2期28卷 1-33页

作者： Hore, Soumyadeep Ghadermazi, Jalal Paudel, Diwas Shah, Ankit Das, Tapas Bastian, Nathaniel Department of Industrial and Management Systems Engineering University of South Florida Tampa United States Department of Operations and Decision Technologies Indiana University Bloomington United States Army Cyber Institute United States Military Academy West Point United States

Recent advancements in artificial intelligence (AI) and machine learning (ML) algorithms, coupled with the availability of faster computing infrastructure, have enhanced the security posture of cybersecurity operations centers (defenders) through the development of ML-aided network intrusion detection systems (NIDS). Concurrently, the abilities of adversaries to evade security have also increased with the support of AI/ML models. Therefore, defenders need to proactively prepare for evasion attacks that exploit the detection mechanisms of NIDS. Recent studies have found that the perturbation of flow-based and packet-based features can deceive ML models, but these approaches have limitations. Perturbations made to the flow-based features are difficult to reverse-engineer, while samples generated with perturbations to the packet-based features are not *** methodological framework, Deep PackGen, employs deep reinforcement learning to generate adversarial packets and aims to overcome the limitations of approaches in the literature. By taking raw malicious network packets as inputs and systematically making perturbations on them, Deep PackGen camouflages them as benign packets while still maintaining their functionality. In our experiments, using publicly available data, Deep PackGen achieved an average adversarial success rate of 66.4% against various ML models and across different attack types. Our investigation also revealed that more than 45% of the successful adversarial samples were out-of-distribution packets that evaded the decision boundaries of the classifiers. The knowledge gained from our study on the adversary's ability to make specific evasive perturbations to different types of malicious packets can help defenders enhance the robustness of their NIDS against evolving adversarial attacks. © 2025 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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A dynamic simulation model to improve the livability of transportation systems

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Public Transport 2025年第1期17卷 177-220页

作者： Suryani, Erma Hendrawan, Rully Agus Adipraja, Philip Faster Eka Widodo, Basuki Chou, Shuo-Yan Az-Zahra, Alifia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Department of Informatics Engineering Institut Teknologi Dan Bisnis Asia Malang Surabaya65113 Indonesia Department of Mathematics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Department of Civil Engineering Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia

Transportation users often face shortcomings, such as unreliable service causing uncertainty in transit, low quality of service, and loss of time due to road and highway congestion. This study aims to improve the livability of the transportation system by considering several factors. System dynamics is used as a model development method to analyze the relationship between complex and nonlinear variables affecting the livability of the transportation system. The system structure of a valid model can be modified by adding multiple feedback loops, new parameters, and changing the feedback loop structure to see how it affects other variables. The scenario model is built from a valid model by changing the model structure to improve the livability of the transportation system through improvements in the perception, safety, security, and health level of public transport, land use accessibility, and mitigation of air pollution. These strategies are expected to increase the livability of the transportation system by 71%, driven by the improvement in perception, safety, security level, and health level of public transport, land use accessibility, and decreased air pollution. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Land use

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Single-Loop Variance-Reduced Stochastic Algorithm for Nonconvex-Concave Minimax Optimization

Single-Loop Variance-Reduced Stochastic Algorithm for Noncon...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xia Jiang Linglingzhi Zhu Taoli Zheng Anthony Man-Cho So Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Nonconvex-concave (NC-C) finite-sum minimax problems have broad applications in decentralized optimization and various machine learning tasks. However, the nonsmooth nature of NC-C problems makes it challenging to design effective variance reduction techniques. Existing vanilla stochastic algorithms using uniform samples for gradient estimation often exhibit slow convergence rates and require bounded variance assumptions. In this paper, we develop a novel probabilistic variance reduction updating scheme and propose a single-loop algorithm called the probabilistic variance-reduced smoothed gradient descent-ascent (PVR-SGDA) algorithm. The proposed algorithm achieves an iteration complexity of ${\mathcal{O}}\left({{\varepsilon ^{ - 4}}}\right)$, surpassing the best-known rates of stochastic algorithms for NC-C minimax problems and matching the performance of the best deterministic algorithms in this context. Finally, we demonstrate the effectiveness of the proposed algorithm through numerical simulations.

关键词： Smoothing methods Machine learning algorithms Signal processing algorithms Signal processing Probabilistic logic Numerical simulation Complexity theory Speech processing Optimization Convergence

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Combination of SKU in POD Assignment in Robotic Mobile Fulfillment systems 11th

Combination of SKU in POD Assignment in Robotic Mobile Fulfi...

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11th International Conference on Industrial engineering and Applications, ICIEA 2024

作者： Pratiwi, Dinda Tria Chou, Shuo-Yan Suparno Kurniati, Nani Department of Industrial Management National Taiwan University of Science and Technology Keelung Taipei Taiwan Department of Industrial and Systems Engineering Sepuluh Nopember Institute of Technology Surabaya Indonesia

ISBN: (纸本)9789819764914

The Robotic Mobile Fulfillment System (RMFS) is widely used in e-commerce warehouses and includes pods, storage locations, workstations for order picking or replenishment, and mobile robots. Decision-making in warehouse systems can be strategic, tactical, or operational. Product assignment, a tactical decision, significantly impacts picking efficiency. This research concentrates on SKU-to-pod allocation, the preliminary step before simulation, involving two phases: product grouping and product combination. Effective product grouping can enhance picking efficiency, while product combination in pod allocation aims to optimize the units picked per pod, termed pile-on, thereby reducing the reliance on Automated Mobile Vehicles (AMVs). Three scenarios were examined: Random Baseline, Class Combination, and Cluster Combination. Class Combination employs ABC classification to sort SKUs into classes using Pareto’s principle, correlating SKU percentages with order frequency percentages. In contrast, Cluster Combination considers product dimensions for pod placement. Simulations determine the optimal pile-on by evaluating the units picked per pod visit to the pick station, with a higher unit count per visit indicating reduced mobile robot transport and increased efficiency. The simulations revealed that Cluster Combination, the final scenario, achieved the best pile-on, with improvements of 30.82% and 8.19% over the first two scenarios, respectively. These results were validated using one-way ANOVA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mobile robots

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An improved skip-lot sampling scheme with resampling mechanism using an advanced capability index

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International Journal of Production Research 2025年第1期63卷 104-118页

作者： Wang, Zih-Huei Wu, Chien-Wei Li, Pei-Yi Department of Industrial Engineering and Systems Management Feng Chia University Taichung Taiwan Department of Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu Taiwan

In today’s competitive market, product quality holds significant importance for consumers, motivating companies to prioritise high-quality product development and enhance their competitiveness. Acceptance sampling plans have proven to be indispensable tools in quality control practices, enabling producers and consumers to protect their interests by establishing evaluation rules for product batches. As defective rates decrease in modern manufacturing, skip-lot sampling plans (SkSP) have emerged as a suitable way for lots exhibiting stable and excellent product quality. Additionally, repetitive group sampling plan (RGSP) offers a cost-effective way to maintain desired protection for both parties, especially in costly or destructive inspection scenarios. Therefore, this study integrates the benefits of process capability indices, SkSP, and RGSP to develop a new variables SkSP with RGSP as its reference plan based on an advanced capability index Cpmk (called Cpmk-based SkSP-RGSP). The proposed plan achieves comparable discriminatory power to traditional sampling plans while significantly reducing the required sample size. Detailed tables of plan parameters are given to facilitate effective implementation for practitioners. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Quality assurance

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Research on Enterprise Risk Decision Support System Optimization based on Ensemble Machine Learning 24

Research on Enterprise Risk Decision Support System Optimiza...

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5th International Conference on Computer Science and management Technology, ICCSMT 2024

作者： Gong, Chenwei Lin, Yuzhen Cao, Jinming Wang, Jun Henry Samueli School of Engineering Department of Computer University of California Los Angeles United States School of Information Systems and Management Carnegie Mellon University Jersey City United States Department of Electrical Computer and Systems Engineering Case Western Reserve University Cleveland United States School of Economics and Management Wuhan University Hubei Wuhan China

ISBN: (纸本)9798400709999

Since the advent of artificial intelligence, it has not only transformed the way we live, but is also accelerating the transformation of production methods. Machine learning is a pivotal technology that endows computers with intelligence. The primary driving force behind its advancement is the pursuit of rapid and precise knowledge acquisition. Nevertheless, the existing enterprise risk decision support system is inadequate in terms of both timeliness and effectiveness when confronted with the task of analysing vast quantities of data. Furthermore, it lacks the capacity to assimilate the expertise of managers and to facilitate interaction in an intuitive manner. By combining a variety of machine learning models, the ensemble learning method effectively leverages the advantages of different algorithms, thereby improving the accuracy and stability of risk prediction. Each model provides a unique perspective and decision-making boundaries when dealing with complex enterprise risk data, but a single model may not be adequate when faced with a particular data set or a particular type of risk. The ensemble method overcomes the problem of bias or overfitting that may be caused by a single model by combining the prediction results of multiple models, so as to obtain a more robust prediction effect. These methods optimize the final prediction results based on how well they perform during training by giving different weights to each model. This convergence strategy significantly improves the accuracy of enterprise risk assessment and helps to provide decision-makers with more reliable data support to make more informed risk management decisions in complex business environments. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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