Pub Date : 2023-11-03DOI: 10.1080/00207543.2023.2244844
Bin Liu, Kerem Akartunali, Stéphane Dauzère-pérès, Shaomin Wu
{"title":"Special issue on collaborative production and maintenance in the environment of big data and industry 4.0","authors":"Bin Liu, Kerem Akartunali, Stéphane Dauzère-pérès, Shaomin Wu","doi":"10.1080/00207543.2023.2244844","DOIUrl":"https://doi.org/10.1080/00207543.2023.2244844","url":null,"abstract":"","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135868351","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}
Pub Date : 2023-11-02DOI: 10.1080/00207543.2023.2275639
Yipei Zhang, Feng Chu, Ada Che, Yantong Li
AbstractUrged by the necessity to establish sustainable supply chains (SCs), this study focuses on exploring the closed-loop inventory routing problem (CIRP) for perishable food packed by multi-type returnable transport items (RTIs). The selling revenue of perishable food is dependent on food's remaining shelf life and the specific type of RTIs used for packaging. RTI selection decisions need to be jointly considered in the CIRP to weigh the potential benefits against associated costs. For this problem, we first develop an integer linear programme (ILP) to maximise the total profit of the holistic SC. Subsequently, we design a tailored kernel search (KS) matheuristic as an efficient solution. A real CIRP with multi-type RTIs for fresh strawberries is used to demonstrate the practicality of the ILP. For this case study, we perform extensive sensitivity analysis of the relevant parameters, extracting valuable managerial insights. Finally, experiments are conducted on 170 randomly generated instances. Computational results show that the proposed KS manages to achieve competitive solutions for instances with up to 10 retailers much more efficiently than CLPEX. For instances with up to 40 retailers, the KS algorithm significantly outperforms CPLEX in terms of solution quality, improving the obtained profit by 80.03% on average under the same computational time.KEYWORDS: Closed-loop inventory routingperishable foodinteger linear programmemulti-type RTIskernel search matheuristic AcknowledgementsThis work was partially derived from the first author’s Doctoral dissertation ‘Zhang, Y. (2019). Optimisation of closed-loop food supply chain with returnable transport items (Doctoral dissertation, University of Paris-Saclay; Northwestern Polytechnical University, Xi'an (China))’.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available from the corresponding author Ada Che, Email: ache@nwpu.edu.cn, upon reasonable request.Additional informationFundingThis work was partially supported by the National Natural Science Foundation of China [grant numbers 72201040, 72310107003 and 72271201], the China Postdoctoral Science Foundation [grant number 2021M700527], the Youth Innovation Team of Shaanxi Universities [grant number 22JP003], and the Fundamental Research Funds for the Central Universities [grant number 300102232607]. The authors also wish to thank all anonymous reviewers whose constructive comments helped to improve this paper.Notes on contributorsYipei ZhangYipei Zhang received the M.S. degree in management science and engineering from Northwestern Polytechnical University, Xi’an, China, in 2015, and the Ph.D. degrees from the University of Paris Saclay, Paris, France, and Northwestern Polytechnical University, Xi’an, China, in 2019 and 2020.Dr. Zhang is currently a Lecturer with Chang’an University. She has authored or coauthored articles publ
他在Omega、Naval Research Logistics、Computers & Operations Research、IEEE Transactions on Robotics and Automation、international Journal of Production Research、European Journal of Operational Research、Operations Research Letters和international Journal of Production Economics等国际期刊上发表了近60篇文章。他的研究兴趣包括运输规划和优化、生产调度、系统建模和优化。他目前担任IEEE Transactions on Intelligent Transportation Systems和Journal of Industrial and Management Optimization的副主编。李彦彤于2011年获得北京交通大学交通与运输专业学士学位，2013年获得中国天津军事运输大学交通规划与管理专业硕士学位，2019年获得法国巴黎萨克雷大学自动化专业博士学位。他是中国大连海事大学的副教授。他的研究兴趣包括生产和物流系统的计划和调度、供应链的集成优化、智能物流和基于数学规划的方法。他的研究论文被INFORMS Journal on Computing、European Journal of Operational research、international Journal of Production research、Transportation research Part E、Omega、international Journal of Production Economics、IEEE Transactions on Automation Science and Engineering、IEEE Transactions on Intelligent Transportation Systems、IEEE Transactions on Engineering Management、Computers & Operations research等国际期刊发表或接受。
{"title":"Closed-loop inventory routing problem for perishable food with returnable transport items selection","authors":"Yipei Zhang, Feng Chu, Ada Che, Yantong Li","doi":"10.1080/00207543.2023.2275639","DOIUrl":"https://doi.org/10.1080/00207543.2023.2275639","url":null,"abstract":"AbstractUrged by the necessity to establish sustainable supply chains (SCs), this study focuses on exploring the closed-loop inventory routing problem (CIRP) for perishable food packed by multi-type returnable transport items (RTIs). The selling revenue of perishable food is dependent on food's remaining shelf life and the specific type of RTIs used for packaging. RTI selection decisions need to be jointly considered in the CIRP to weigh the potential benefits against associated costs. For this problem, we first develop an integer linear programme (ILP) to maximise the total profit of the holistic SC. Subsequently, we design a tailored kernel search (KS) matheuristic as an efficient solution. A real CIRP with multi-type RTIs for fresh strawberries is used to demonstrate the practicality of the ILP. For this case study, we perform extensive sensitivity analysis of the relevant parameters, extracting valuable managerial insights. Finally, experiments are conducted on 170 randomly generated instances. Computational results show that the proposed KS manages to achieve competitive solutions for instances with up to 10 retailers much more efficiently than CLPEX. For instances with up to 40 retailers, the KS algorithm significantly outperforms CPLEX in terms of solution quality, improving the obtained profit by 80.03% on average under the same computational time.KEYWORDS: Closed-loop inventory routingperishable foodinteger linear programmemulti-type RTIskernel search matheuristic AcknowledgementsThis work was partially derived from the first author’s Doctoral dissertation ‘Zhang, Y. (2019). Optimisation of closed-loop food supply chain with returnable transport items (Doctoral dissertation, University of Paris-Saclay; Northwestern Polytechnical University, Xi'an (China))’.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available from the corresponding author Ada Che, Email: ache@nwpu.edu.cn, upon reasonable request.Additional informationFundingThis work was partially supported by the National Natural Science Foundation of China [grant numbers 72201040, 72310107003 and 72271201], the China Postdoctoral Science Foundation [grant number 2021M700527], the Youth Innovation Team of Shaanxi Universities [grant number 22JP003], and the Fundamental Research Funds for the Central Universities [grant number 300102232607]. The authors also wish to thank all anonymous reviewers whose constructive comments helped to improve this paper.Notes on contributorsYipei ZhangYipei Zhang received the M.S. degree in management science and engineering from Northwestern Polytechnical University, Xi’an, China, in 2015, and the Ph.D. degrees from the University of Paris Saclay, Paris, France, and Northwestern Polytechnical University, Xi’an, China, in 2019 and 2020.Dr. Zhang is currently a Lecturer with Chang’an University. She has authored or coauthored articles publ","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135973273","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}
Pub Date : 2023-11-02DOI: 10.1080/00207543.2023.2276808
Lei Wang, Haoxun Chen
AbstractA continuous-review, two-echelon distribution system with one central distribution centre (CDC) and multiple regional distribution centres (RDCs) is studied. The CDC jointly replenishes its inventories of multiple items from an external supplier, while each RDC replenishes its inventories of the items from the CDC. Each RDC faces a Poisson demand for each item, and the inventories of each stock in the system are controlled by a (Q, S) policy. Under this policy, an order is placed by a stock whenever its aggregate demand since the last order reaches a given quantity, and the inventory position of each item is raised up to its order-up-to level after the order placement. The objective is to optimise these (Q, S) policies so that the expected total cost of this system is minimised. We propose a decomposition and coordination method for this optimisation after deriving analytically the cost function of the system. Our extensive numerical experiments demonstrate the effectiveness of the proposed method. Furthermore, a parameter sensitivity analysis is conducted to analyse the impacts of some key system parameters on the performance of the method, and managerial insights are provided for optimising distribution systems with joint replenishment and real applications of the method.KEYWORDS: Inventory managementjoint replenishmentdistribution systemdecomposition and coordinationoptimisation Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.Additional informationFundingThis work was supported by China Scholarship Council with [grant number 201806950117].Notes on contributorsLei WangLei Wang received his B.Eng. and M.Sc. degrees in logistics engineering from Wuhan University of Technology, China, in 2017 and 2019, respectively. He received his Ph.D. degree in system security and optimisation from University of Technology of Troyes, France, in 2023. He has published an article in the Computers & Operations Research. His current research interests include inventory management, supply chain optimisation, and operations research.Haoxun ChenHaoxun Chen received his B. S. degree in applied mathematics from Fudan University, China, in 1984, his Master and Ph.D. degree in systems engineering in 1987 and 1990, respectively, from Xi’an Jiaotong University, China. He was with Xi’an Jiaotong University as a Lecturer from 1990 to 1992 and as an Associate Professor from 1993 to 1996. He visited INRIA-Lorraine, France, as a Visiting Professor, in 1994, University of Magdeburg, Germany, as a Research Fellow of Alexander von Humboldt Foundation, in 1997 and 1998, and University of Connecticut as an Assistant Professor in 1999 and 2000. He joined University of Technology of Troyes, France, in 2001 and has been a Full Professor since 2004. His research interests include sup
{"title":"A decomposition and coordination method for optimising ( <i>Q</i> , <b> <i>S</i> </b> ) policies in a two-echelon distribution system with joint replenishment","authors":"Lei Wang, Haoxun Chen","doi":"10.1080/00207543.2023.2276808","DOIUrl":"https://doi.org/10.1080/00207543.2023.2276808","url":null,"abstract":"AbstractA continuous-review, two-echelon distribution system with one central distribution centre (CDC) and multiple regional distribution centres (RDCs) is studied. The CDC jointly replenishes its inventories of multiple items from an external supplier, while each RDC replenishes its inventories of the items from the CDC. Each RDC faces a Poisson demand for each item, and the inventories of each stock in the system are controlled by a (Q, S) policy. Under this policy, an order is placed by a stock whenever its aggregate demand since the last order reaches a given quantity, and the inventory position of each item is raised up to its order-up-to level after the order placement. The objective is to optimise these (Q, S) policies so that the expected total cost of this system is minimised. We propose a decomposition and coordination method for this optimisation after deriving analytically the cost function of the system. Our extensive numerical experiments demonstrate the effectiveness of the proposed method. Furthermore, a parameter sensitivity analysis is conducted to analyse the impacts of some key system parameters on the performance of the method, and managerial insights are provided for optimising distribution systems with joint replenishment and real applications of the method.KEYWORDS: Inventory managementjoint replenishmentdistribution systemdecomposition and coordinationoptimisation Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.Additional informationFundingThis work was supported by China Scholarship Council with [grant number 201806950117].Notes on contributorsLei WangLei Wang received his B.Eng. and M.Sc. degrees in logistics engineering from Wuhan University of Technology, China, in 2017 and 2019, respectively. He received his Ph.D. degree in system security and optimisation from University of Technology of Troyes, France, in 2023. He has published an article in the Computers & Operations Research. His current research interests include inventory management, supply chain optimisation, and operations research.Haoxun ChenHaoxun Chen received his B. S. degree in applied mathematics from Fudan University, China, in 1984, his Master and Ph.D. degree in systems engineering in 1987 and 1990, respectively, from Xi’an Jiaotong University, China. He was with Xi’an Jiaotong University as a Lecturer from 1990 to 1992 and as an Associate Professor from 1993 to 1996. He visited INRIA-Lorraine, France, as a Visiting Professor, in 1994, University of Magdeburg, Germany, as a Research Fellow of Alexander von Humboldt Foundation, in 1997 and 1998, and University of Connecticut as an Assistant Professor in 1999 and 2000. He joined University of Technology of Troyes, France, in 2001 and has been a Full Professor since 2004. His research interests include sup","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135973405","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}
Pub Date : 2023-11-02DOI: 10.1080/00207543.2023.2276811
Ilya Jackson, Maria Jesus Saenz, Dmitry Ivanov
Our research strives to examine how simulation models of logistics systems can be produced automatically from verbal descriptions in natural language and how human experts and artificial intelligence (AI)-based systems can collaborate in the domain of simulation modelling. We demonstrate that a framework constructed upon the refined GPT-3 Codex is capable of generating functionally valid simulations for queuing and inventory management systems when provided with a verbal explanation. As a result, the language model could produce simulation models for inventory and process control. These results, along with the rapid improvement of language models, enable a significant simplification of simulation model development. Our study offers guidelines and a design of a natural language processing-based framework on how to build simulation models of logistics systems automatically, given the verbal description. In generalised terms, our work offers a technological underpinning of human-AI collaboration for the development of simulation models.
{"title":"From natural language to simulations: applying AI to automate simulation modelling of logistics systems","authors":"Ilya Jackson, Maria Jesus Saenz, Dmitry Ivanov","doi":"10.1080/00207543.2023.2276811","DOIUrl":"https://doi.org/10.1080/00207543.2023.2276811","url":null,"abstract":"Our research strives to examine how simulation models of logistics systems can be produced automatically from verbal descriptions in natural language and how human experts and artificial intelligence (AI)-based systems can collaborate in the domain of simulation modelling. We demonstrate that a framework constructed upon the refined GPT-3 Codex is capable of generating functionally valid simulations for queuing and inventory management systems when provided with a verbal explanation. As a result, the language model could produce simulation models for inventory and process control. These results, along with the rapid improvement of language models, enable a significant simplification of simulation model development. Our study offers guidelines and a design of a natural language processing-based framework on how to build simulation models of logistics systems automatically, given the verbal description. In generalised terms, our work offers a technological underpinning of human-AI collaboration for the development of simulation models.","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135934995","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}
Pub Date : 2023-11-01DOI: 10.1080/00207543.2023.2275635
Victor Delpla, Kevin Chapron, Jean-Pierre Kenné, Lucas A. Hof
{"title":"A novel approach for predicting Lockout/Tagout safety procedures for smart maintenance strategies","authors":"Victor Delpla, Kevin Chapron, Jean-Pierre Kenné, Lucas A. Hof","doi":"10.1080/00207543.2023.2275635","DOIUrl":"https://doi.org/10.1080/00207543.2023.2275635","url":null,"abstract":"","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135272887","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}
Pub Date : 2023-11-01DOI: 10.1080/00207543.2023.2275634
Ioannis Avgerinos, Ioannis Mourtos, Stavros Vatikiotis, Georgios Zois
AbstractMotivated by the need of quick job (re-)scheduling, we examine an elaborate scheduling environment under the objective of total weighted tardiness minimisation. The examined problem variant moves well beyond existing literature, as it considers unrelated machines, sequence-dependent and machine-dependent setup times, and a renewable resource constraint on the number of simultaneous setups. For this variant, we provide a relaxed MILP to calculate lower bounds, thus estimating a worst-case optimality gap. As a fast exact approach appears not plausible for instances of practical importance, we extend known (meta-)heuristics to deal with the problem at hand, coupling them with a Constraint Programming (CP) component – vital to guarantee the non-violation of the problem's constraints – which optimally allocates resources with respect to tardiness minimisation. The validity and versatility of employing different (meta-)heuristics exploiting a relaxed MILP as a quality measure are revealed by our extensive experimental study, which shows that the methods deployed have complementary strengths depending on the instance parameters. Since the problem description has been obtained from a textile manufacturer where jobs of diverse size arrive continuously under tight due dates, we also discuss the practical impact of our approach in terms of both tardiness decrease and broader managerial insights.KEYWORDS: Parallel machine schedulingsequence-dependent setup timesweighted tardinessmetaheuristicstextile manufacturing Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementInstances, results and codes that support the findings of this work are available at https://github.com/svatikiot/Weighted_Tardiness_Experiments.Additional informationFundingThis research has been supported by the EU through the FACTLOG Horizon 2020 project, grant number 869951.Notes on contributorsIoannis AvgerinosIoannis Avgerinos is a Ph.D. candidate in combinatorial optimization at the Department of Management Science and Technology, Athens University of Economics and Business, Greece. He received the Bachelor's degree in geomatics engineering from National Technical University of Athens and the Master's degree in management science and technology from Athens University of Economics and Business. His research interests include combinatorial optimisation, decomposition methods and their implementation on transport and production scheduling problems.Ioannis MourtosIoannis Mourtos is a professor at the Department of Management Science and Technology, Athens University of Economics and Business. He studied computer engineering and informatics at University of Patras and operations research at London School of Economics and Political Science. His academic interests lie within combinatorial optimisation, polyhedral combinatorics and the integration of integer programming with constraint programming. His work has been applied to optim
摘要以快速作业(再)调度需求为出发点，研究了以总加权延迟最小化为目标的精细调度环境。所研究的问题变体远远超出了现有的文献，因为它考虑了不相关的机器、序列相关和机器相关的设置时间，以及对同时设置数量的可再生资源约束。对于这种变体，我们提供了一个宽松的MILP来计算下界，从而估计最坏情况下的最优性差距。由于快速精确的方法对于实际重要性的实例似乎不合理，我们扩展了已知的(元)启发式来处理手头的问题，将它们与约束规划(CP)组件相结合-这对于保证不违反问题的约束至关重要-这对于延迟最小化而言是最优分配资源的。我们广泛的实验研究揭示了采用不同(元)启发式方法利用宽松的MILP作为质量度量的有效性和通用性，这表明所部署的方法根据实例参数具有互补的优势。由于问题描述是从纺织制造商那里获得的，其中不同规模的工作在紧迫的截止日期下不断到达，我们还讨论了我们的方法在延迟减少和更广泛的管理见解方面的实际影响。关键词:并行机调度顺序相关设置时间加权延迟元启发式纺织品制造披露声明作者未报告潜在的利益冲突。数据可用性声明支持这项工作发现的实例、结果和代码可在https://github.com/svatikiot/Weighted_Tardiness_Experiments.Additional上获得。资助本研究由欧盟通过FACTLOG Horizon 2020项目提供支持，资助号为869951。作者简介sioannis Avgerinos是希腊雅典经济与商业大学管理科学与技术系组合优化专业的博士生。他持有雅典国立技术大学(National Technical University of Athens)的信息工程学士学位和雅典经济与商业大学(Athens University of Economics and Business)的管理科学与技术硕士学位。他的研究兴趣包括组合优化、分解方法及其在运输和生产调度问题上的应用。本文作者是雅典经济与商业大学管理科学与技术系教授。他在帕特雷大学学习计算机工程和信息学，在伦敦政治经济学院学习运筹学。他的学术兴趣集中在组合优化、多面体组合和整数规划与约束规划的集成。他的工作已被应用于制造业和物流的优化问题，并得到了几个欧盟资助项目的支持。Stavros Vatikiotis，雅典经济与商业大学管理科学与技术系博士候选人。他在雅典国立技术大学学习电气和计算机工程，并拥有雅典国立技术大学应用数学和物理科学学院的数学建模硕士学位。主要研究方向为组合优化方法的设计及其在工业生产环境中的应用。Georgios Zois是雅典经济与商业大学管理科学与技术系的研究助理。他在约阿尼纳大学学习计算机科学，在雅典大学学习逻辑、算法和计算，并在皮埃尔和玛丽居里大学(UPMC)获得博士学位，研究能源和温度效率计算的算法问题。他的研究兴趣在于为物流和制造业领域的实际应用设计和分析高效精确和接近最优的优化算法，包括位置优化和多式联运问题，以及资源意识生产调度和计划。
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Pub Date : 2023-11-01DOI: 10.1080/00207543.2023.2267680
Huosong Xia, Zelin Sun, Yuan Wang, Justin Zuopeng Zhang, Muhammad Mustafa Kamal, Sajjad M. Jasimuddin, Nazrul Islam
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-10-30DOI: 10.1080/00207543.2023.2274340
Fatima Ezzahra Achamrah, Mariam Lafkihi, Eric Ballot
{"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":null,"pages":null},"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}
Pub Date : 2023-10-30DOI: 10.1080/00207543.2023.2265508
Ai Zhao, Jonathan F. Bard
{"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":null,"pages":null},"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}
Pub Date : 2023-10-26DOI: 10.1080/00207543.2023.2271093
Yitian Liu, Kang Hu, Ruifeng Zhou, Xianfeng Ai, Yunqing Chen
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
{"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":null,"pages":null},"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}
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