International Journal of Production Research最新文献

{"title":"A Pareto-based hybrid genetic simulated annealing algorithm for multi-objective hybrid production line balancing problem considering disassembly and assembly","authors":"Xiang Sun, Shunsheng Guo, Jun Guo, Baigang Du, Zhijie Yang, Kaipu Wang","doi":"10.1080/00207543.2023.2280696","DOIUrl":"https://doi.org/10.1080/00207543.2023.2280696","url":null,"abstract":"ABSTRACTMost existing studies about line balancing problems mainly focus on disassembly and assembly separately, which rarely integrate these two modes into a system. However, as critical activities in the remanufacturing field, assembly and disassembly share many similarities, such as working tools and processing sequence. Thus, this paper proposes a multi-objective hybrid production line balancing problem with a fixed number of workstations (HPLBP-FNW) considering disassembly and assembly to optimise cycle time, total cost, and workload smoothness simultaneously. And a novel Pareto-based hybrid genetic simulated annealing algorithm (PB-HGSA) is designed to solve it. In PB-HGSA, the two-point crossover and hybrid mutation operator are proposed to produce potential non-dominated solutions (NDSs). Then, a local search method based on a parallel simulated annealing algorithm is designed for providing a depth search around the NDSs to balance the global and local search ability. Numerical results by comparing PB-HGSA with the well-known algorithms verify the effectiveness of PB-HGSA in solving HPLBP-FNW. Moreover, the managerial insights based on a case study are given to inspire enterprise companies to consider hybrid production line in the remanufacturing process, which is beneficial to reduce the cycle time and total cost and improve the service life of the equipment.KEYWORDS: Hybrid production line balancingdisassembly and assemblycycle timeworkload smoothnesshybrid genetic simulated annealing Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData will be made available on request.Additional informationFundingThis work was supported by the National Natural Science Foundation of China under Project (No. 51705386) and by China Scholarship Council (No. 201606955091).Notes on contributorsXiang SunXiang Sun received the B.Eng degree from Huazhong Agricultural University, Wuhan, China, in 2018. He is pursuing the Ph.D. degree at Wuhan University of Technology, Wuhan, China. His current research interests include manufacturing scheduling, machine learning and intelligent optimization algorithms.Shunsheng GuoShunsheng Guo received the B.Sc. degree in Mechanical manufacturing and automation from Huazhong University of Science and Technology, Wuhan, China, in 1984 and the Ph.D. degree in Mechanical Design and Theory from Wuhan University of Technology, Wuhan, China, in 2001. He is currently a Professor with the School of Mechanical and Electronic Engineering, Wuhan, China. His current research interests include manufacturing informatization and intelligent manufacturing.Jun GuoJun Guo received the M.S. degree (2009) and Ph.D. degree (2012) in Mechanical Engineering from Wuhan University of Technology, Wuhan, China. He is currently an Associate Professor with the School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China. His current research interests include p","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991400","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 2-dimensional guillotine cutting stock problem with variable-sized stock for the honeycomb cardboard industry","authors":"Paula Terán-Viadero, Antonio Alonso-Ayuso, F. Javier Martín-Campo","doi":"10.1080/00207543.2023.2279129","DOIUrl":"https://doi.org/10.1080/00207543.2023.2279129","url":null,"abstract":"AbstractThis paper introduces novel mathematical optimisation models for the 2-Dimensional guillotine Cutting Stock Problem with Variable-Sized Stock that appears in a Spanish company in the honeycomb cardboard industry. This problem mainly differs from the classical cutting stock problems in the stock, which is considered variable-sized, i.e. we have to decide the panel dimensions, width, and length. This approach is helpful in industries where the stock is produced simultaneously with the cutting process. The stock is then cut into smaller rectangular pieces that must meet the customers' requirements, such as the type of item, dimensions, demands, and technical specifications. Furthermore, in the problem tackled in this paper, the cuts are guillotine, performed side to side. The proposed mathematical models are validated using real data from the company, obtaining results that drastically reduce the produced material and leftovers, reducing operation times and economic costs.Keywords: Cutting stock problem2-dimensional cuttingvariable-sized stockmixed integer linear optimisationcardboard industry AcknowledgmentsThe authors would like to thank the company managers for providing us with real data and for giving us insight into the company's current operation.Data availability statementDue to the nature of the research, due to commercial supporting not all data is available. We refer the readers to Terán-Viadero, Alonso-Ayuso, and Martín-Campo (Citation2023) where for six instances, input data and results obtained are reported.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work has been supported by grant PID2021-122640OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ‘ERDF A way of making Europe’.Notes on contributorsPaula Terán-ViaderoPaula Terán-Viadero is a PhD student who received her Master's degree in 2019 in Mathematical Engineering from the Complutense University of Madrid (UCM), Spain. She specialised in operations research in 2019 when she was part of the Statistics and Operational Research department in the Faculty of Mathematical Sciences at UCM, developing optimisation models for a company in the hospitality sector. Since then, she has worked in the private sector, developing integer linear mathematical optimisation models to solve problems arising from real-world applications.Antonio Alonso-AyusoAntonio Alonso-Ayuso received his PhD in Mathematics from the Complutense University of Madrid, Spain, in 1997. He is currently a Full Professor in Statistics and Operational Research at Rey Juan Carlos University, Spain. His main research interests include linear and integer mathematical optimisation, decision models, and stochastic optimisation applied to combinatorial problems. He has developed several projects jointly with companies in different sectors (steel, oil and paper, among others).F. Javier Martín-CampoF. Javier Martín-Campo received his PhD from the Rey","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136348308","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":"Graph-enabled cognitive digital twins for causal inference in maintenance processes","authors":"Kendrik Yan Hong Lim, Theresia Stefanny Yosal, Chun-Hsien Chen, Pai Zheng, Lihui Wang, Xun Xu","doi":"10.1080/00207543.2023.2274335","DOIUrl":"https://doi.org/10.1080/00207543.2023.2274335","url":null,"abstract":"AbstractThe increasing complexity of industrial systems demands more effective and intelligent maintenance approaches to address manufacturing defects arising from faults in multiple asset modules. Traditional digital twin (DT) systems, however, face limitations in interoperability, knowledge sharing, and causal inference. As such, cognitive digital twins (CDTs) can add value by managing a collaborative web of interconnected systems, facilitating advanced cross-domain analysis and dynamic context considerations. This paper introduces a CDT system that leverages industrial knowledge graphs (iKGs) to support maintenance planning and operations. By employing a design structure matrix (DSM) to model dependencies and relationships, a semantic translation approach maps the knowledge into a graph-based representation for reasoning and analysis. An automatic solution generation mechanism, utilising graph sequencing with Louvain and PageRank algorithms, derives feasible solutions, which can be validated via simulation to minimise production disruption impacts. The CDT system can also identify potential disruptions in new product designs, thus enabling preventive actions to be taken. A case study featuring a print production manufacturing line illustrates the CDT system's capabilities in causal inference and solution explainability. The study concludes with a discussion of limitations and future directions, providing valuable guidelines for manufacturers aiming to enhance reactive and predictive maintenance strategies.KEYWORDS: Cognitive digital twinsindustrial knowledge graphscausal inferencesemantic modellingquality assurancemaintenance AcknowledgementsThe authors would like to acknowledge the professional advice of Teo Man Ru and Tiong Je Min from Tetra Pak Jurong Pte Ltd.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData is not available due to commercial restrictions. Due to the sensitive nature of this study, the participants of this study did not consent to public sharing of their data, so support data is not available.Additional informationNotes on contributorsKendrik Yan Hong LimKendrik Yan Hong LIM is a Ph.D. candidate at the School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore and a senior research engineer at Singapore’s Agency of Science and Technology (A*STAR). He holds a bachelor’s degree in mechanical engineering from NTU, and a master’s degree in Industry Engineering from Chiba University, Japan. His research interests include engineering informatics, digital twins, and smart product-service systems.Theresia Stefanny YosalTheresia Stefanny Yosal is currently working as an equipment engineer at a manufacturing company. She holds a bachelor’s degree in mechanical engineering from Nanyang Technological University (NTU), Singapore. Her research interests are digital twins, product design and development, and manufacturing.Chun-Hsien Ch","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135137330","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":"Modeling third-party reverse logistics for healthcare waste recycling in the post-pandemic era","authors":"Meenu Singh, Sunil Kumar Jauhar, Millie Pant, Sanjoy Kumar Paul","doi":"10.1080/00207543.2023.2269584","DOIUrl":"https://doi.org/10.1080/00207543.2023.2269584","url":null,"abstract":"AbstractThe COVID-19 pandemic has increased the demand for life-saving devices known as ‘ventilators,’ which help critically ill patients breathe. Owing to the high global demand for ventilators and other medical equipment, many Indian nonmedical equipment companies have risen to meet this demand. This unexpected demand for ventilators during the COVID-19 pandemic, similar to that for other EOL electronic medical devices, has become a severe problem for the nation. Consequently, the healthcare industry must efficiently handle EOL ventilators, which can be outsourced to 3PRLPs. 3PRLPs play a vital role in a company’s reverse logistics activities. This study emphasises the 3PRLP selection process as a complex decision-making problem and the optimisation of order allocation to qualified 3PRLPs. As a result, this study proposes a two-phase hybrid decision-making problem. First phase combines the two multi-attribute decision-making methods to select 3PRLPs based on their assessed SPS and Second phase, the evaluated SPS was utilised as one of the objectives of a multi-objective linear programming model to allocate orders to the selected 3PRLPs. To solve the proposed model, both classical and modern approaches were used. The results show that the proposed framework can be successfully implemented in the current scenario of the healthcare industry.KEYWORDS: Reverse logisticswaste recyclingmulti-objective programmingorder allocationhealthcare industry Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability StatementThe data supporting this study’s findings are available on request from the corresponding author.Notes1 Source: https://www.statista.com/statistics/1067081/generation-electronic-waste-globally-forecast/2 Source: https://theroundup.org/global-e-waste-statistics/.3 Source: https://www.deccanherald.com/business/covid-19-automakers-medical-device-makers-join-hands-to-produce-ventilators-819878.html.4 Source: https://www.weforum.org/agenda/2020/04/covid-19-ventilator-shortage-manufacturing-solution/5 Source: https://straitsresearch.com/report/ventilators-market6 Source:https://www.indiatoday.in/india/story/did-ventilators-from-pm-cares-fund-fail-or-states-failed-to-manage-them-1803473-2021-05-17Additional informationNotes on contributorsMeenu SinghDr. Meenu Singh received a Ph.D. in operations research and decision science from the Department of Applied Mathematics and Scientific Computing at the Indian Institute of Technology (IIT) Roorkee, India, in 2022. She is currently a postdoctoral researcher at VŠB – Technical University of Ostrava, Ostrava, Czech Republic. Her research focuses on operational research, supply chain management, data analysis, mathematical modelling, multi-criteria decision-making (MCDM) process, and the application of soft computing techniques.Sunil Kumar JauharDr. Sunil Kumar Jauhar is currently working as an assistant professor in the operations and decision science area at IIM ","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135136537","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 systematic literature review exploring and linking circular economy and sustainable development goals in the past three decades (1991–2022)","authors":"Emilia Vann Yaroson, Soumyadeb Chowdhury, Sachin Kumar Mangla, Prasanta Dey, Felix T. S. Chan, Melanie Roux","doi":"10.1080/00207543.2023.2270586","DOIUrl":"https://doi.org/10.1080/00207543.2023.2270586","url":null,"abstract":"AbstractAmid the escalating environmental crises and economic disparities, Circular Economy (CE) has garnered recognition as a pragmatic mechanism for achieving Sustainable Development Goals (SDGs). In response, several supply chain organisations are integrating CE strategies into their business operations and production processes. Despite these developments and since the introduction of Business Charter for Sustainable Development by the International Chamber of Commerce in 1991, the academic corpus comprehensively connecting CE research themes, catalysts, deterrents, practices with the SDGs has remained limited. To bridge this gap, we present a systematic literature review (SLR) of CE research in operations, supply chain and production management encompassing a time span of 31 years (January 1991 – June 2022), by sourcing, screening, and analysing articles obtained from multiple research databases. Our thematic coding analysis generated ten research themes, and subsequently linking them with relevant SDGs. Additionally, we interweaved CE catalysts and deterrents, establishing a connection with the SDGs. This is further enriched with CE strategies aimed at equipping business practitioners to enhance sustainable business performance and contributing to specific SDGs. Lastly, we delineate CE knowledge data management and priority actions frameworks to aid organisations to enhance employee capability and actively leverage digital technologies for implementing CE strategies.KEYWORDS: Circular economySystematic literature reviewThematic evolutionGreen and responsible supply chain managementSustainability development goalsSustainable business performance 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 upon reasonable request. The data are not publicly available due to the size of the dataset.Additional informationFundingThe work described in this paper was substantially supported by grants from Macau University of Science and Technology Faculty Research Grants (FRG) under grant number FRG-22–108-MSB, and The Macau Foundation Fund (MFP) under grant number MF-23-008-R.Notes on contributorsEmilia Vann YarosonEmilia Vann Yaroson is a lecturer in Operations and Analytics at the University of Huddersfield Business School. She earned her PhD in Operations and Information Management at the University of Bradford, UK. Her current research focus include theoretical improvement and applications of artificial intelligence, big data analytics, block chain, within health service operations, supply chain resilience and sustainable production and open innovation. She has published in leading peer reviewed journals such as International Journal of Production Research, Journal of Business Research and Supply Chain Management.Soumyadeb ChowdhurySoumyadeb Chowdhury is Associate Professor of Digital Sustainability Mana","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241824","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":"An adaptive artificial bee colony for hybrid flow shop scheduling with batch processing machines in casting process","authors":"Jing Wang, Deming Lei, Hongtao Tang","doi":"10.1080/00207543.2023.2279145","DOIUrl":"https://doi.org/10.1080/00207543.2023.2279145","url":null,"abstract":"AbstractHybrid flow shop scheduling problem (HFSP) with real-life constraints has been extensively considered; however, HFSP with batch processing machines (BPM) at a middle stage is seldom investigated. In this study, HFSP with BPM at a middle stage in hot & cold casting process is considered and an adaptive artificial bee colony (AABC) is proposed to minimise makespan. To produce high quality solutions, an adaptive search process with employed bee phase and adaptive search step is implemented. Adaptive search step, which may be onlooker bee phase or cooperation or empty, is decided by evolution quality and an adaptive threshold. Cooperation is performed between the improved solutions of one employed bee swarm and the unimproved solutions of another swarm. Six search operators are constructed and search operator is adaptively adjusted. A new scout phase is also given. A lower bound is provided and proved. Extensive experiments are conducted. The computational results validate that new strategies such as cooperation are effective and efficient and AABC can obtain better results than methods from existing literature on the considered problem.Keywords: Hot & cold castingartificial bee colonyhybrid flow shop scheduling problembatch processing machines Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData supporting this study are described in the first paragraph of Section 5.1.Additional informationFundingThis work is supported by the National Natural Science Foundation of China [61573264].Notes on contributorsJing WangJing Wang received the bachelor's degree in industrial engineering from the Hubei University of Technology, Wuhan, China, in 2017 and the master's degree in industrial engineering from Fuzhou University, Fuzhou, China, in 2020. She is currently pursuing the doctoral degree with the School of Automation, Wuhan University of Technology, Wuhan, China. Her current research interest includes manufacturing systems intelligent optimisation and scheduling.Deming LeiDeming Lei received the master's degree in applied mathematics from Xi'an Jiaotong University, Xi'an, China, in 1996 and the doctoral degree in automation science and engineering from Shanghai Jiaotong University, Shanghai, China, in 2005. He is currently a professor with the School of Automation, Wuhan University of Technology, Wuhan, China. He has published over 100 journal papers. His current research interests include intelligent system optimisation and control, and production scheduling.Hongtao TangHongtao Tang received the bachelor's degree in material molding and control engineering from the Wuhan University of Technology, Wuhan, China, in 2008, and the doctoral degree in digital material forming from Huazhong University of Science and Technology, Wuhan, China, in 2014. He is currently an associate professor with the School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan, China. His","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241465","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":"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}