Pub Date : 2023-11-09DOI: 10.1080/00207543.2023.2280186
Bi Fan, Fengjie Liao, Chao Yang, Quande Qin
AbstractWith increasing environmental concerns and energy crisis, a variety of renewable energy sources (RES) are being increasingly utilised worldwide. However, the integration of RES such as wind power and photovoltaics in large-scale can lead to increased load fluctuations, which can undermine the overall environmental benefits and pose risks to the secure and stable operation of the power system. To mitigate this challenge, a two-stage electricity production scheduling is developed incorporating energy storage system (ESS) and dynamic emission modelling (DEM). In the first stage, a multi-objective mixed integer programming model schedules the production of RES, increasing penetration rate and system stability. In the second stage, a data-driven dynamic emission model is developed to optimise the load allocation of thermal power unit to reduce the carbon emissions. Furthermore, a flexible operating reserve strategy is proposed to handle the uncertainty resulting from the intermittent character of RES. Experimental results demonstrate that the proposed method effectively schedules the production of RES thereby alleviating the contradiction between high RES utilisation and stable system operation. Compared to the benchmark model, the proposed method can reduce the carbon emissions and total cost of the system by 20.34% and 10.65%, respectively.KEYWORDS: Renewable integrationenergy storage systemdynamic emissiongeneration scheduleoperational flexibility Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data presented in this study are available as request.Additional informationFundingThis research was supported by the National Natural Science Foundation of China [grant numbers 72174124, 71871146, 71701136], the Natural Science Foundation of Guangdong Province [grant numbers 2022A1515011009, 2021A1515010987], Shenzhen Science and Technology Program [grant number JCYJ20210324093414039], and by NTUT-SZU Joint Research Program [grant number 2023005].Notes on contributorsBi FanBi Fan, is an Associate Professor in the College of Management, Shenzhen University, Shenzhen, China. He received his Ph.D. degree in System Engineering and Engineering Management from City University of Hong Kong, in 2014. His research interests include the optimisation problems related to energy system management, intelligent manufacturing, and data-driven decisions.Fengjie LiaoFengjie Liao, is currently a postgraduate at College of Management, Shenzhen University, Shenzhen, China. He received the B.S degree from Shanghai Maritime University, Shanghai, China. His main research interests include power system dispatch and renewable energy planning.Chao YangChao Yang, is currently an Assistant Professor in Shenzhen University. He received the Ph.D. degree from Shenzhen University, Shenzhen, China, in 2020. His research interests include urbanisation, sustainable development, and the social-ecological effects of huma
{"title":"Two-stage electricity production scheduling with energy storage and dynamic emission modelling","authors":"Bi Fan, Fengjie Liao, Chao Yang, Quande Qin","doi":"10.1080/00207543.2023.2280186","DOIUrl":"https://doi.org/10.1080/00207543.2023.2280186","url":null,"abstract":"AbstractWith increasing environmental concerns and energy crisis, a variety of renewable energy sources (RES) are being increasingly utilised worldwide. However, the integration of RES such as wind power and photovoltaics in large-scale can lead to increased load fluctuations, which can undermine the overall environmental benefits and pose risks to the secure and stable operation of the power system. To mitigate this challenge, a two-stage electricity production scheduling is developed incorporating energy storage system (ESS) and dynamic emission modelling (DEM). In the first stage, a multi-objective mixed integer programming model schedules the production of RES, increasing penetration rate and system stability. In the second stage, a data-driven dynamic emission model is developed to optimise the load allocation of thermal power unit to reduce the carbon emissions. Furthermore, a flexible operating reserve strategy is proposed to handle the uncertainty resulting from the intermittent character of RES. Experimental results demonstrate that the proposed method effectively schedules the production of RES thereby alleviating the contradiction between high RES utilisation and stable system operation. Compared to the benchmark model, the proposed method can reduce the carbon emissions and total cost of the system by 20.34% and 10.65%, respectively.KEYWORDS: Renewable integrationenergy storage systemdynamic emissiongeneration scheduleoperational flexibility Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data presented in this study are available as request.Additional informationFundingThis research was supported by the National Natural Science Foundation of China [grant numbers 72174124, 71871146, 71701136], the Natural Science Foundation of Guangdong Province [grant numbers 2022A1515011009, 2021A1515010987], Shenzhen Science and Technology Program [grant number JCYJ20210324093414039], and by NTUT-SZU Joint Research Program [grant number 2023005].Notes on contributorsBi FanBi Fan, is an Associate Professor in the College of Management, Shenzhen University, Shenzhen, China. He received his Ph.D. degree in System Engineering and Engineering Management from City University of Hong Kong, in 2014. His research interests include the optimisation problems related to energy system management, intelligent manufacturing, and data-driven decisions.Fengjie LiaoFengjie Liao, is currently a postgraduate at College of Management, Shenzhen University, Shenzhen, China. He received the B.S degree from Shanghai Maritime University, Shanghai, China. His main research interests include power system dispatch and renewable energy planning.Chao YangChao Yang, is currently an Assistant Professor in Shenzhen University. He received the Ph.D. degree from Shenzhen University, Shenzhen, China, in 2020. His research interests include urbanisation, sustainable development, and the social-ecological effects of huma","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":" 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241063","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 : 2023-11-08DOI: 10.1080/00207543.2023.2276825
Amina Haned, Abida Kerdali, Mourad Boudhar
AbstractIn this paper, we address the problem of scheduling jobs on identical machines for minimising the maximum completion time (makespan). Each job requires a sequence-independent setup time, which represents the time needed to prepare the machines for job execution. Then, we introduce a dynamic programme to solve the case with two machines, and show that this problem admits a fully polynomial time approximation scheme. For the case of m machines, we propose heuristics and an adapted genetic algorithm. Some numerical experiments are done to evaluate the proposed algorithms.Keywords: Schedulingpreemptionsetup timesmakespandynamic programmingFPTAS 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.Notes1 mod(n,m) is the remainder of the Euclidean division of n by m.Additional informationNotes on contributorsAmina HanedAmina Haned received her PhD in mathematics at the University USTHB of Algiers. She is a lecturer at the Faculty of Economic Sciences, Commercial Sciences and Management Sciences, University Algiers 3. Amina is deeply interested in the fields of optimisation, operational research, and data science, with a particular focus on scheduling and operations optimisation.Abida KerdaliAbida Kerdali received her PhD in National Higher School of Statistics and Applied Economics. She is a Lecturer at the same School in University center of Kola, Algeria. Her research area is operational research, with a focus on economic problems.Mourad BoudharMourad Boudhar received his PhD in mathematics at the University USTHB of Algiers. He is a professor at the Department of Operational Research, University USTHB. His research interests include issues related to operational research and optimisation, with a particular focus on scheduling problems with new constraints as transportation, conflict, recirculation, multi-agents, etc. He has published several research papers in national and international journals and conference proceedings.
{"title":"Scheduling on identical machines with preemption and setup times","authors":"Amina Haned, Abida Kerdali, Mourad Boudhar","doi":"10.1080/00207543.2023.2276825","DOIUrl":"https://doi.org/10.1080/00207543.2023.2276825","url":null,"abstract":"AbstractIn this paper, we address the problem of scheduling jobs on identical machines for minimising the maximum completion time (makespan). Each job requires a sequence-independent setup time, which represents the time needed to prepare the machines for job execution. Then, we introduce a dynamic programme to solve the case with two machines, and show that this problem admits a fully polynomial time approximation scheme. For the case of m machines, we propose heuristics and an adapted genetic algorithm. Some numerical experiments are done to evaluate the proposed algorithms.Keywords: Schedulingpreemptionsetup timesmakespandynamic programmingFPTAS 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.Notes1 mod(n,m) is the remainder of the Euclidean division of n by m.Additional informationNotes on contributorsAmina HanedAmina Haned received her PhD in mathematics at the University USTHB of Algiers. She is a lecturer at the Faculty of Economic Sciences, Commercial Sciences and Management Sciences, University Algiers 3. Amina is deeply interested in the fields of optimisation, operational research, and data science, with a particular focus on scheduling and operations optimisation.Abida KerdaliAbida Kerdali received her PhD in National Higher School of Statistics and Applied Economics. She is a Lecturer at the same School in University center of Kola, Algeria. Her research area is operational research, with a focus on economic problems.Mourad BoudharMourad Boudhar received his PhD in mathematics at the University USTHB of Algiers. He is a professor at the Department of Operational Research, University USTHB. His research interests include issues related to operational research and optimisation, with a particular focus on scheduling problems with new constraints as transportation, conflict, recirculation, multi-agents, etc. He has published several research papers in national and international journals and conference proceedings.","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"131 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135342198","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 : 2023-11-07DOI: 10.1080/00207543.2023.2279130
Ziliang Wang, Chenhao Zhou, Ada Che, Jingkun Gao
AbstractThe container pre-marshalling problem (CPMP) aims to minimise the number of reshuffling moves, ultimately achieving an optimised stacking arrangement in each bay based on the priority of containers during the non-loading phase. Given the sequential decision nature, we formulated the CPMP as a Markov decision process (MDP) model to account for the specific state and action of the reshuffling process. To address the challenge that the relocated container may trigger a chain effect on the subsequent reshuffling moves, this paper develops an improved policy-based Monte Carlo tree search (P-MCTS) to solve the CPMP, where eight composite reshuffling rules and modified upper confidence bounds are employed in the selection phases, and a well-designed heuristic algorithm is utilised in the simulation phases. Meanwhile, considering the effectiveness of reinforcement learning methods for solving the MDP model, an improved Q-learning is proposed as the compared method. Numerical results show that the P-MCTS outperforms all compared methods in scenarios where all containers have different priorities and scenarios where containers can share the same priority.KEYWORDS: Container pre-marshalling problemMonte Carlo tree searchMarkov decision processQ-learning algorithmAutomated container terminal AcknowledgementThis research was made possible with funding support from National Natural Science Foundation of China [72101203, 71871183], Shaanxi Provincial Key R&D Program, China [2022KW-02], and China Scholarship Council [grant number 202206290124].Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData sharing not applicable – no new data generated.Additional informationFundingThis work was supported by National Natural Science Foundation of China: [Grant Number 72101203, 71871183]; China Scholarship Council: [Grant Number 202206290124]; Shaanxi Provincial Key R&D Program, China: [Grant Number 2022KW-02].Notes on contributorsZiliang WangMr. Ziliang Wang, is a Doctoral student from School of Management in Northwestern Polytechnical University.Chenhao ZhouDr. Chenhao Zhou, is a Professor from School of Management in Northwestern Polytechnical University. Prior to this, he was a Research Assistant Professor in the Department of Industrial Systems Engineering and Management, National University of Singapore. His research interests are transportation systems and maritime logistics using simulation and optimization methods.Ada CheDr. Ada Che, is a Professor from School of Management in Northwestern Polytechnical University. He received the B.S. and Ph.D. degrees in Mechanical Engineering from Xi’an Jiaotong University in 1994 and 1999, respectively. Since 2005, he has been a Professor in School of Management in Northwestern Polytechnical University. His current research interests include transportation planning and optimisation, production scheduling, and operations research.Jingkun GaoMr. Jingkun Gao, is
{"title":"A policy-based Monte Carlo tree search method for container pre-marshalling","authors":"Ziliang Wang, Chenhao Zhou, Ada Che, Jingkun Gao","doi":"10.1080/00207543.2023.2279130","DOIUrl":"https://doi.org/10.1080/00207543.2023.2279130","url":null,"abstract":"AbstractThe container pre-marshalling problem (CPMP) aims to minimise the number of reshuffling moves, ultimately achieving an optimised stacking arrangement in each bay based on the priority of containers during the non-loading phase. Given the sequential decision nature, we formulated the CPMP as a Markov decision process (MDP) model to account for the specific state and action of the reshuffling process. To address the challenge that the relocated container may trigger a chain effect on the subsequent reshuffling moves, this paper develops an improved policy-based Monte Carlo tree search (P-MCTS) to solve the CPMP, where eight composite reshuffling rules and modified upper confidence bounds are employed in the selection phases, and a well-designed heuristic algorithm is utilised in the simulation phases. Meanwhile, considering the effectiveness of reinforcement learning methods for solving the MDP model, an improved Q-learning is proposed as the compared method. Numerical results show that the P-MCTS outperforms all compared methods in scenarios where all containers have different priorities and scenarios where containers can share the same priority.KEYWORDS: Container pre-marshalling problemMonte Carlo tree searchMarkov decision processQ-learning algorithmAutomated container terminal AcknowledgementThis research was made possible with funding support from National Natural Science Foundation of China [72101203, 71871183], Shaanxi Provincial Key R&D Program, China [2022KW-02], and China Scholarship Council [grant number 202206290124].Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData sharing not applicable – no new data generated.Additional informationFundingThis work was supported by National Natural Science Foundation of China: [Grant Number 72101203, 71871183]; China Scholarship Council: [Grant Number 202206290124]; Shaanxi Provincial Key R&D Program, China: [Grant Number 2022KW-02].Notes on contributorsZiliang WangMr. Ziliang Wang, is a Doctoral student from School of Management in Northwestern Polytechnical University.Chenhao ZhouDr. Chenhao Zhou, is a Professor from School of Management in Northwestern Polytechnical University. Prior to this, he was a Research Assistant Professor in the Department of Industrial Systems Engineering and Management, National University of Singapore. His research interests are transportation systems and maritime logistics using simulation and optimization methods.Ada CheDr. Ada Che, is a Professor from School of Management in Northwestern Polytechnical University. He received the B.S. and Ph.D. degrees in Mechanical Engineering from Xi’an Jiaotong University in 1994 and 1999, respectively. Since 2005, he has been a Professor in School of Management in Northwestern Polytechnical University. His current research interests include transportation planning and optimisation, production scheduling, and operations research.Jingkun GaoMr. Jingkun Gao, is","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"273 9‐13","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135474816","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 : 2023-11-05DOI: 10.1080/00207543.2023.2276818
Manolis N. Kritikos, George Ioannou
AbstractIn this paper, we introduce the non-unit demand capacitated minimum spanning tree problem with arc time windows and flow costs. The problem is a variant of the capacitated minimum spanning tree problem with arc time windows (CMSTP_ATW). We devise a mixed integer programming (MIP) formulation to model the problem and solve it using CPLEX. Furthermore, we propose three sets of inequalities, and we prove that they are valid. These valid inequalities tighten the model and lead to better lower bounds. To examine the quality of the solutions obtained, we convert the original data sets of Solomon (Citation1987, “Algorithms for the Vehicle Routing and Scheduling Problem with Time Window Constraints.” Operations Research 35 (2): 254–265. https://doi.org/10.1287/opre.35.2.254) to approximate the non-unit demand CMSTP_ATW instances and provide results for the problems with 100 nodes. We execute extensive computational experiments, and the results show the positive effect of the inclusion of valid inequalities in the MIP.KEYWORDS: Capacitated minimum spanning treearc time windowsmixed integer programming formulationvalid inequalitiesflow costs AcknowledgementsThe authors would like to thank the anonymous reviewers, the Associate Editor and the Special Issue Editor for their acute comments and constructive suggestions that helped improve the content and the presentation of the manuscript.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, [M.N.K.], upon request.Additional informationNotes on contributorsManolis N. KritikosManolis Kritikos is Professor of Operations Research and Information Systems at the Department of Management Science and Technology, Athens University of Economics and Business (AUEB). He obtained his Ph.D. in Management Science from AUEB and his MSc in Operations Research and Information Systems and BSc in Mathematics, both from the University of Athens. His doctoral research has been funded by the EDAMBA (European Doctoral Programme Association in Management and Business Administration) programme, with host institute the Rotterdam Business School. He is serving as Director of the Management Science Laboratory (MSL) of AUEB. His research interests include combinatorial optimisation, mathematical programming models, design and analysis of algorithms for operational research problems and performance measurement. In recent years, he published papers on top-ranked journal including OMEGA, Expert Systems with Applications, the International Journal of Production Economics, Journal of the Operational Research Society, International Transactions in Operational Research, Socio-Economic Planning Sciences, Applied economics, and Operational Research. He is associate editor of the Journal of Statistics and Management Systems. He was awarded the Certificate of Outstanding Contribution in Review
摘要本文讨论了具有弧时间窗和流成本的非单位需求最小生成树问题。该问题是带弧时间窗的有能力最小生成树问题(CMSTP_ATW)的变体。我们设计了一个混合整数规划(MIP)公式来对问题进行建模，并使用CPLEX进行求解。进一步，我们提出了三组不等式，并证明了它们的有效性。这些有效的不等式加强了模型，并导致更好的下界。为了检验得到的解的质量，我们转换了Solomon (Citation1987)的原始数据集，“带时间窗口约束的车辆路线和调度问题的算法”。运筹学研究35(2):254-265。https://doi.org/10.1287/opre.35.2.254)来近似非单元需求CMSTP_ATW实例，并提供具有100个节点的问题的结果。我们进行了大量的计算实验，结果显示了在MIP中包含有效不等式的积极效果。关键词:有能力最小生成树时间窗口混合整数规划公式有效不等式流动成本致谢作者要感谢匿名审稿人、副主编和特刊编辑的意见和建设性建议，这些意见和建议有助于改进稿件的内容和呈现。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本研究结果的数据可从通讯作者[M.N.K.]，应要求。作者简介:manolis N. Kritikos，雅典经济与商业大学管理科学与技术系运筹学与信息系统教授。他获得了AUEB管理科学博士学位，以及雅典大学运筹学和信息系统理学硕士学位和数学学士学位。他的博士研究得到了EDAMBA(欧洲管理和工商管理博士课程协会)项目的资助，主办机构是鹿特丹商学院。现任管理科学实验室(MSL)主任。他的研究兴趣包括组合优化、数学规划模型、运筹学问题算法的设计和分析以及性能测量。近年来在《OMEGA》、《Expert Systems with Applications》、《International journal of Production Economics》、《运筹学学会学报》、《运筹学国际汇刊》、《社会经济计划科学》、《应用经济学》、《运筹学》等顶级期刊上发表论文。他是统计与管理系统杂志的副主编。他被ELSEVIER授予杰出评审贡献奖，以表彰他对ESWA期刊质量的贡献。他曾担任希腊发展部信息系统顾问。2015年至2021年，Manolis Kritikos担任东南欧数学学会(MASSEE)秘书长。乔治IoannouDr。乔治·约安努是雅典经济与商业大学管理科学与技术系运营管理学教授。他曾担任Hellenic Energy Exchange Group的首席执行官(2019-2022)，德勤咨询公司的高级经理和AUEB管理科学实验室的主任(2012-2019)。他曾担任AUEB国际MBA项目主任和弗吉尼亚理工大学工业和系统工程系助理教授。他在雅典国立技术大学获得机械工程文凭，并在英国帝国理工学院获得机器人和自动化硕士学位。他是马里兰大学系统研究所(Institute for Systems Research, University of Maryland, USA)的GRA，并在那里获得机械工程博士学位。他是微软卓越教育奖和希腊能源经济协会杰出成就奖的获得者，并因其MBA课程和创新发展而获得多项卓越奖。他是Hellenic railways SA的董事会成员，发展部创新委员会成员，AUEB参议院成员和美国商会创新委员会成员，并曾担任ΣΥΖΕΥΞΙΣ ΙΙ (Information Society SA)评估委员会主任。
{"title":"Valid inequalities for the non-unit demand capacitated minimum spanning tree problem with arc time windows and flow costs","authors":"Manolis N. Kritikos, George Ioannou","doi":"10.1080/00207543.2023.2276818","DOIUrl":"https://doi.org/10.1080/00207543.2023.2276818","url":null,"abstract":"AbstractIn this paper, we introduce the non-unit demand capacitated minimum spanning tree problem with arc time windows and flow costs. The problem is a variant of the capacitated minimum spanning tree problem with arc time windows (CMSTP_ATW). We devise a mixed integer programming (MIP) formulation to model the problem and solve it using CPLEX. Furthermore, we propose three sets of inequalities, and we prove that they are valid. These valid inequalities tighten the model and lead to better lower bounds. To examine the quality of the solutions obtained, we convert the original data sets of Solomon (Citation1987, “Algorithms for the Vehicle Routing and Scheduling Problem with Time Window Constraints.” Operations Research 35 (2): 254–265. https://doi.org/10.1287/opre.35.2.254) to approximate the non-unit demand CMSTP_ATW instances and provide results for the problems with 100 nodes. We execute extensive computational experiments, and the results show the positive effect of the inclusion of valid inequalities in the MIP.KEYWORDS: Capacitated minimum spanning treearc time windowsmixed integer programming formulationvalid inequalitiesflow costs AcknowledgementsThe authors would like to thank the anonymous reviewers, the Associate Editor and the Special Issue Editor for their acute comments and constructive suggestions that helped improve the content and the presentation of the manuscript.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, [M.N.K.], upon request.Additional informationNotes on contributorsManolis N. KritikosManolis Kritikos is Professor of Operations Research and Information Systems at the Department of Management Science and Technology, Athens University of Economics and Business (AUEB). He obtained his Ph.D. in Management Science from AUEB and his MSc in Operations Research and Information Systems and BSc in Mathematics, both from the University of Athens. His doctoral research has been funded by the EDAMBA (European Doctoral Programme Association in Management and Business Administration) programme, with host institute the Rotterdam Business School. He is serving as Director of the Management Science Laboratory (MSL) of AUEB. His research interests include combinatorial optimisation, mathematical programming models, design and analysis of algorithms for operational research problems and performance measurement. In recent years, he published papers on top-ranked journal including OMEGA, Expert Systems with Applications, the International Journal of Production Economics, Journal of the Operational Research Society, International Transactions in Operational Research, Socio-Economic Planning Sciences, Applied economics, and Operational Research. He is associate editor of the Journal of Statistics and Management Systems. He was awarded the Certificate of Outstanding Contribution in Review","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"59 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135726126","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 : 2023-11-03DOI: 10.1080/00207543.2023.2244844
Bin Liu, Kerem Akartunali, Stéphane Dauzère-pérès, Shaomin Wu
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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":"32 7","pages":"0"},"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":"34 11","pages":"0"},"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":"63 3","pages":"0"},"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":"79 23-24","pages":"0"},"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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