International Journal of Production Research最新文献

{"title":"Emergency medical supplies scheduling during public health emergencies: algorithm design based on AI techniques","authors":"Huosong Xia, Zelin Sun, Yuan Wang, Justin Zuopeng Zhang, Muhammad Mustafa Kamal, Sajjad M. Jasimuddin, Nazrul Islam","doi":"10.1080/00207543.2023.2267680","DOIUrl":"https://doi.org/10.1080/00207543.2023.2267680","url":null,"abstract":"Based on AI technology, this study proposes a novel large-scale emergency medical supplies scheduling (EMSS) algorithm to address the issues of low turnover efficiency of medical supplies and unbalanced supply and demand point scheduling in public health emergencies. We construct a fairness index using an improved Gini coefficient by considering the demand for emergency medical supplies (EMS), actual distribution, and the degree of emergency at disaster sites. We developed a bi-objective optimisation model with a minimum Gini index and scheduling time. We employ a heterogeneous ant colony algorithm to solve the Pareto boundary based on reinforcement learning. A reinforcement learning mechanism is introduced to update and exchange pheromones among populations, with reward factors set to adjust pheromones and improve algorithm convergence speed. The effectiveness of the algorithm for a large EMSS problem is verified by comparing its comprehensive performance against a super-large capacity evaluation index. Results demonstrate the algorithm's effectiveness in reducing convergence time and facilitating escape from local optima in EMSS problems. The algorithm addresses the issue of demand differences at each disaster point affecting fair distribution. This study optimises early-stage EMSS schemes for public health events to minimise losses and casualties while mitigating emotional distress among disaster victims.","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"84 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135271770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dynamic and reactive routing protocol for the physical internet network","authors":"Fatima Ezzahra Achamrah, Mariam Lafkihi, Eric Ballot","doi":"10.1080/00207543.2023.2274340","DOIUrl":"https://doi.org/10.1080/00207543.2023.2274340","url":null,"abstract":"","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"27 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Batch scheduling in a multi-purpose system with machine downtime and a multi-skilled workforce","authors":"Ai Zhao, Jonathan F. Bard","doi":"10.1080/00207543.2023.2265508","DOIUrl":"https://doi.org/10.1080/00207543.2023.2265508","url":null,"abstract":"","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"69 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data driven design optimisation: an empirical study of demand discovery combining theory of planned behaviour and Bayesian networks","authors":"Yitian Liu, Kang Hu, Ruifeng Zhou, Xianfeng Ai, Yunqing Chen","doi":"10.1080/00207543.2023.2271093","DOIUrl":"https://doi.org/10.1080/00207543.2023.2271093","url":null,"abstract":"AbstractMany theoretical methods have been applied to research user behaviour and requirements. However, the uncertainty associated with customer characteristics often biases the conclusions drawn from customer research and affects the effectiveness of product design. In this paper, Bayesian networks (BN) are introduced into the research on customer behaviour analysis based upon theory of planned behaviour (TPB), and an analysis model driven by customer research data is established from the perspective of user behaviour intention to guide design optimisation. Combining the User background Factor with the TPB Factor, the model analyses the uncertainty of the association between the two, and corrects the errors in the designer's prior knowledge through structural learning. By a case study the paper finds that the evaluations that enhance customers’ subjective norms and perceived behavioural control lead to a greater probability of purchase or use. In addition, customers with specific characteristics are more inclined to generate behaviour intention. The paper finally provides a design optimisation plan based upon the result of the research and discusses about the advantages of the research approaches and the directions of future researches.KEYWORDS: Product designdesign optimisationtheory of planned behaviourBayesian networkscustomer requirements Disclosure statementNo potential conflict of interest was reported by the author(s).AcknowledgmentsThe research also received support and assistance from College of Information Technology Shanghai Ocean University and Central China Normal University.Data availability statementBased on the protection of human subjects, the sequence files and note data for all samples used in this study have been desposited in Figshare (https://doi.org/10.6084/m9.figshare.21118321.v1). The data includes the statistical data table with the information of the participants removed, the Bayesian network graph collection generated by the R operation, the data table processed by the SPSSAU platform, and the Netica processing file.CRediT authorship contribution statementYitian Liu: Supervision, Conceptualisation, Research, Experiment, Design, Writing – original draft, Writing – review & editing, Writing – translate, Continuous modification. Kang Hu: Supervision, Conceptualisation, Research, Experiment, Design, Writing – original draft, Continuous modification. Ruifeng Zhou: Writing – review & editing, Writing – translate and Continuous modification. Xianfeng Ai: Supervision, Conceptualisation, Research, Experiment, Design, Writing – original draft. Yunqing Chen: Conceptualisation, Research, Writing – original draft, Writing – review & editing, Writing – translate.Additional informationFunding.Notes on contributorsYitian LiuYitian Liu is a graduate student. Graduated from the School of Art and Design, Wuhan University of Science and Technology in 2020, majoring in industrial Design, and is studying for a doctorate degree in industri","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136381887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A critical review of selective maintenance for mission-oriented systems: challenges and a roadmap for novel contributions","authors":"Hamzea Al-Jabouri, Ahmed Saif, Abdelhakim Khatab, Claver Diallo, Uday Venkatadri","doi":"10.1080/00207543.2023.2270689","DOIUrl":"https://doi.org/10.1080/00207543.2023.2270689","url":null,"abstract":"AbstractThe selective maintenance problem (SMP) arises in many mission-oriented multi-component systems that are operated for consecutive missions interspersed with finite breaks, during which only limited component repairs can be performed due to constrained resources. This NP-hard problem decides which components to maintain and to what levels of repair to guarantee a pre-specified performance level during the subsequent mission. Over the last two decades, a sizeable body of literature has been published on this topic. However, the contributions have stagnated in quality, and most articles deal with small to moderate problems. This paper provides a critical review of the SMP literature. A total of 136 research articles related to SMP are reviewed and a selection of key representative models is discussed in detail. This review is framed according to two feature categories: formulation characteristics, composed of three sub-groups of characteristics related to the system, maintenance and mathematical model characteristics; and solution approaches, grouped by exact methods and approximate algorithms. This critical review is aimed at identifying drawbacks, shortcomings, and blind spots of the SMP literature, and providing a roadmap for the challenges to be addressed and innovative future research topics to further advance the academic and industrial contributions of SMP.Keywords: Selective maintenancemaintenance planningreliability maximisationresource assignmentimperfect maintenance AcknowledgmentsWe also thank the anonymous reviewers for their suggestions and comments.Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article. The review data is freely available at www.smpreview.com (Al-Jabouri, Saif, Diallo, Khatab, and Venkatadri Citation2023), enabling custom sorting based on characteristics of interest.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Canadian Natural Science and Engineering Research Council (NSERC) grants awarded to the second, fourth and fifth authors through the Discovery Grant Programme.Notes on contributorsHamzea Al-JabouriHamzea Al-Jabouri Ph.D., is a Simulation Specialist at MAGNA International, located in Brampton, Ontario. He earned his Ph.D. in Industrial Engineering from Dalhousie University, Halifax, Nova Scotia, and attained a Master of Applied Science in Industrial Engineering from the University of Regina, Saskatchewan. Dr. Al-Jabouri is a member of the Association of Professional Engineers and Geoscientists of Saskatchewan (APEGS). Presently, his research endeavours focus on simulation-based optimisation, as well as large-scale and robust optimisation strategies for intelligent maintenance operations.Ahmed SaifAhmed Saif , P.Eng., Ph.D., is an Associate Professor in the Department of Industrial Engineering at Dalhousie University. He received his","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135316128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementing industry 4.0 for flexibility, quality, and productivity improvement: technology arrangements for different purposes","authors":"D. V. Enrique, G. Marodin, F. Charrua-Santos, A. G. Frank","doi":"10.1080/00207543.2022.2142689","DOIUrl":"https://doi.org/10.1080/00207543.2022.2142689","url":null,"abstract":"ABSTRACT Productivity, quality, and flexibility are key production targets pursued by companies that adopt Industry 4.0. However, it is unclear how Industry 4.0 technologies can help achieve these different and sometimes competing targets. This study investigates this relationship through a survey of 92 manufacturers. The study employs Exploratory Factor Analysis to define four main technology arrangements based on 18 Industry 4.0 technologies: Vertical Integration, Virtual Manufacturing, Advanced Manufacturing Processing Technologies, and Online Traceability. Then, independent samples tests were conducted to compare the implementation status of these arrangements when manufacturing flexibility, process quality, and productivity are (or are not) pursued as the main production targets. The results show that Vertical Integration is a general-purpose technology arrangement because it supports all targets. On the other hand, Virtual Manufacturing and Online Traceability are specific-purpose arrangements, adopted especially for flexibility and productivity targets, respectively. Advanced Manufacturing Processing Technologies, in turn, is an integrative-purpose technology arrangement since it is adopted when two competing targets are pursued: productivity and manufacturing flexibility. The study ends with a decision model to implement Industry 4.0 based on the production targets a company may pursue. It shows the interconnection and trade-offs between these production targets and the Industry 4.0 technologies adopted.","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"61 1","pages":"7001 - 7026"},"PeriodicalIF":9.2,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46582805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation-based production control of manufacturing–remanufacturing systems with uncertain seasonal return and imprecise demand and inventory","authors":"Vladmir Polotski, Jean-Pierre Kenné, Ali Gharbi","doi":"10.1080/00207543.2023.2269275","DOIUrl":"https://doi.org/10.1080/00207543.2023.2269275","url":null,"abstract":"AbstractHybrid manufacturing systems utilising raw materials and returned end-of-life products for production are studied. The systems are failure-prone and subject to inventory, market demand and return uncertainties. Thanks to growing environmental and sustainability concerns, the manufacturing sector is currently experiencing a significant growth in the popularity of reverse logistics. However, the practical implementation of production control in such systems is challenging due to return flow uncertainty and variability. To address this challenge, an estimation-based control using the Kalman filter is proposed in this research. The demand and return models employed contain random and deterministic components, with the latter being time-invariant for demand and uncertain with seasonal variations for return. The processing steps used include the estimation of inventory levels and demand and return components, return forecasting allowing cost computation over a long horizon, and the determination of the production and disposal policies adapting to market variations and uncertainties. We classify the systems according to relationships between their production capacity, demand and return ranges. We then present an extensive numerical study of optimal policies for various system classes and show that adaptive policies outperform the conventional ones, thus proving the effectiveness of the proposed production control approach for complex industrially oriented systems.Keywords: Remanufacturingfailure-proneuncertaintyestimationforecastingKalman filter Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data supporting the findings of this study are available from the corresponding author, V. Polotski, upon reasonable request.Additional informationNotes on contributorsVladmir PolotskiVladimir Polotski is a researcher in the Mechanical Engineering Department at Ecole de Technologie Superieure (ETS). Hi graduated from Moscow State University in 1974 and obtained his Ph.D. in Mechanics and Control in 1978. After moving to Canada in 1993, he joined the Perception and Robotics Group at Ecole Polytechnique de Montreal where he worked as a researcher from 1994 to 2004. From 2005 to 2009 he was a Chief algorithm designer in Frontline Robotics Inc. working on autonomous robotic systems for security applications. From 2010 to 2012 he played a key role in the design and development of navigation systems for two planetary rover projects launched by CSA working for Cohort Systems Inc. and Neptec Design Group. Since 2012, he works as a researcher in the Department of Mechanical Engineering at ETS. His research interests focus o stochastic control of manufacturing systems and mathematical problems in product development. Dr. Polotski has more than 40 years of experience in automatic control, signal processing, sensor fusion, optimisation, mobile robotics and numerical modelling.Jean-Pierre KennéJean-Pierr","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135883540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A technology maturity assessment framework for Industry 5.0 machine vision systems based on systematic literature review in automotive manufacturing","authors":"Fotios K. Konstantinidis, Nikolaos Myrillas, Konstantinos A. Tsintotas, Spyridon G. Mouroutsos, Antonios Gasteratos","doi":"10.1080/00207543.2023.2270588","DOIUrl":"https://doi.org/10.1080/00207543.2023.2270588","url":null,"abstract":"AbstractWhen considering how an intelligent factory can ‘see,’ the answer lies in machine vision technology. To assess the current technological advancements of machine vision systems and propose a technology maturity assessment framework, a nine-phase Systematic Literature Review (SLR) strategy was implemented. As the automotive industry stands at the forefront of autonomous systems, we analysed 85 works across the entire automotive manufacturing life cycle. The findings revealed that machine vision is utilised in each technological pillar of Industry 4.0, encompassing autonomous robots, augmented reality, predictive maintenance, additive manufacturing, and more. In analysing 22 vision-based applications in 47 automotive components, we clustered machine vision systems' architectural components and processing techniques, ranging from threshold-based methods to advanced reinforcement learning techniques suitable for the I5.0 environment. Leveraging the insights gathered, we propose the I5.0 technology maturity assessment framework for machine vision systems, evaluating nine functional components across five scaling technology levels. This framework serves as a valuable tool to identify weaknesses and opportunities for improvement, guiding machine vision integration into an intelligent factory.Keywords: Maturity assessmentmachine visionsystematic literatureautomotive manufacturingindustry 5.0zero defect manufacturing Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData sharing not applicable – no new data generatedNotes1 https://fortune.com/fortune500/2021/.2 https://fortune.com/fortune500/2021/.3 https://bit.ly/ReviewedPapersAndAnalytics.Additional informationNotes on contributorsFotios K. KonstantinidisFotios Konstantinidis is a Team leader in Industry 5.0 & Smart Manufacturing at the Institute of Communication and Computer Systems (ICCS) of the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA) and holds a Ph.D. in Smart Manufacturing from the department of Production & Management Engineering at the Democritus University of Thrace (DUTh). He is currently leading a team of researchers and professionals with the objective of developing advanced industrial waste sorting systems. These systems utilize cutting-edge technologies such as hyperspectral & visual imaging, delta robots, air nozzles, X-ray sensors, and pretreatment units. Their focus areas include the efficient sorting of (bio)plastic waste, construction and demolition waste, metal scraps, mining characterization, and wood waste. Before this, Fotios worked as an I4.0 Technology Analyst, analysing the plants' maturity level and proposing I4.0 strategies for Fortune 500 companies. In contrast, he worked in the telecom industry at the Next-Generation Access networks. He has also organised workshops, delivered presentations at conferences/workshops, and published peer-reviewed journal","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136033298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A matheuristic approach for the multi-level capacitated lot-sizing problem with substitution and backorder","authors":"Hu Qin, Haocheng Zhuang, Chunlong Yu, Jiliu Li","doi":"10.1080/00207543.2023.2270076","DOIUrl":"https://doi.org/10.1080/00207543.2023.2270076","url":null,"abstract":"AbstractThe lot-sizing problem aims at determining the products to be produced and their quantities for each time period, which is a difficult problem in production planning. This problem becomes even more complicated when practical aspects such as limited production capacity, bill of materials, and item substitution are considered. In this paper, we study a new variant of the lot-sizing problem, called the multi-level capacitated lot-sizing problem with substitution and backorder. Unlike previous studies, this variant considers substitutions at both the product and component levels, which is based on the real needs of manufacturers to increase planning flexibility. Backorders are allowed, but should be delivered within a certain time limitation. We formulate this problem using a mathematical programming model. A matheuristic approach is proposed to solve the problem. This first generates an initial feasible solution using a relax-and-fix algorithm, and then improves it using a hybrid fix-and-optimise algorithm. The proposed algorithm is calibrated with a full factorial design of experiments, and its efficiency is well validated. Finally, through extensive numerical experiments, we analyse the properties of this new lot-sizing problem, such as the effect of substitution options, and the influence of backorder time limitation, and provide several useful managerial insights for manufacturing companies to save costs in production planning.KEYWORDS: Lot-sizing problemsubstitutionbackordermatheuristicfix-and-optimiserelax-and-fix Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe instance data that used in this paper are openly available in https://github.com/ZhuangHaoCheng/MLCLSPSB_Instance.Additional informationFundingThis research was partially supported by the National Key R&D Program of China [grant number 2018YFB1700600], National Natural Science Foundation of China [grant number 71971090,71821001], Shanghai Pujiang Program [grant number 21PJ1413300], and the Tongji University Fundamental Research Funds for the Central Universities.Notes on contributorsHu QinHu Qin received the Ph.D. degree from the City University of Hong Kong, Hong Kong, in 2011. He is currently a Professor with the School of Management, Huazhong University of Science and Technology. His current research interests are in the fields of algorithms and artificial intelligence, including various topics in operations research, such as vehicle routeing problem, freight allocation problem, container loading problems, and transportation problems.Haocheng ZhuangHaocheng Zhuang received B.S. degree from School of Management, Huazhong University of Science and Technology, Wuhan, China, 2020. He is currently pursuing the Ph.D. degree with the School of Management, Huazhong University of Science and Technology. His work focuses on the combinatorial optimisation problems in the production and logistics.Chunlong YuChunlong Yu is","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135994554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-based forecasting for optimised solar energy management and smart grid efficiency","authors":"Pierre Bouquet, Ilya Jackson, Mostafa Nick, Amin Kaboli","doi":"10.1080/00207543.2023.2269565","DOIUrl":"https://doi.org/10.1080/00207543.2023.2269565","url":null,"abstract":"","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136116771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}