International Journal of Production Research最新文献

{"title":"The impact of incentive-based programmes on job-shop scheduling with variable machine speeds","authors":"Marc Füchtenhans, Christoph H. Glock","doi":"10.1080/00207543.2023.2266765","DOIUrl":"https://doi.org/10.1080/00207543.2023.2266765","url":null,"abstract":"AbstractGiven the high demand for energy in the manufacturing industry and the increasing use of renewable but volatile energy sources, it becomes increasingly important to coordinate production and energy availability. With the help of incentive-based programmes, grid operators can incentivise consumers to adjust power demand in critical situations such that grid stability is not threatened. On the consumer side, energy-efficient scheduling models can be used to make energy consumption more flexible. This paper proposes a bi-objective job-shop scheduling problem with variable machine speeds that aims on minimising the total energy consumption and total weighted tardiness simultaneously. We use a genetic algorithm to solve the model and derive Pareto frontiers to analyse the trade-off between both conflicting objectives. We gain insights into how incentive-based programmes can be integrated into machine scheduling models and analyse the potential interdependencies and benefits that result from this integration.KEYWORDS: Job-shop schedulingenergy-efficient production planninggenetic algorithmsustainable manufacturingdemand response programmesincentive-based programmes AcknowledgementsThis paper is a revised and extended version of the conference paper ‘Energy-efficient job shop scheduling considering processing speed and incentive-based programmes’ that was presented at 10th IFAC Conference on Manufacturing Modelling, Management and Control in Nantes, France, 2022. The authors are grateful to the anonymous reviewers for their constructive comments on an earlier version of this 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 upon reasonable request.Additional informationFundingThis work was supported by the State of Hesse for energy subsidies within the scope of the Hessian Energy Act (Hessischen Energiegesetztes, HEG) of 9 October 2019 with funds from the State of Hesse and with the kind support of the House of Energy [grant number E/411/71632164].Notes on contributorsMarc FüchtenhansMarc Füchtenhans received B.Sc. and M.Sc. degrees in business mathematics from Technical University of Darmstadt in 2014 and 2018. Since 2018, he is a Research Associate and Ph.D. student at the Institute of Production and Supply Chain Management at Technical University of Darmstadt. His research interests include sustainable solutions in the context of production and supply chain management. His works have appeared in the International Journal of Production Research and the International Journal of Logistics Research and Applications, among others.Christoph H. GlockChristoph H. Glock is a full professor and head of the Institute of Production and Supply Chain Management at Technical University of Darmstadt. His research interests include inventory management, supply chain management, warehousing, sustai","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114005","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":"Employing disabled workers in production: simulating the impact on performance and service level","authors":"Paweł Litwin, Dario Antonelli, Dorota Stadnicka","doi":"10.1080/00207543.2023.2266066","DOIUrl":"https://doi.org/10.1080/00207543.2023.2266066","url":null,"abstract":"AbstractDisabled people can be successfully employed in most production processes, provided that one knows how to exploit their abilities and take into account their limitations in order to give them an appropriate job. However, because the level and type of production must be constantly adapted to the needs of the market, the involvement of disabled people in the production process may also change. Additionally, people with disabilities have limitations as well as additional rights that must be considered. As a result, the organisation and planning of their work, side by side with other employees, becomes more complex. Computer simulations can be a support for organising and planning the involvement of employees with disabilities in production processes. The aim of the article is to show how simulations can facilitate the organisation of work of employees with disabilities, with the changing demand for manufactured products. The paper identifies the factors that should be considered, and then presents how the employment of disabled people can affect the operation of the production line and the commercial image of the company. The study uses a combination of System Dynamics and Discrete Event Simulations. The relevant data for the simulation were derived from a production company.KEYWORDS: Manufacturing systemsmodellingsystem dynamicsdiscrete event simulationdisabled employees Disclosure statementNo potential conflict of interest was reported by the author(s).Availability of dataThe authors confirm that the data supporting the findings are available in the article.Additional informationNotes on contributorsPaweł LitwinPaweł Litwin is an assistant professor at the Faculty of Mechanical and Aeronautical Engineering, Rzeszow University of Technology (Rzeszow, Poland). His main research area is the industrial application of numerical simulation: analysis of material and information flow in manufacturing systems and supply chains, operation of industrial systems in the socio-economic environment. He also carries out research in engineers education for the industry of the future and sustainable development goals achievement.Dario AntonelliDario Antonelli is an associate professor at the Department of Management and Production Engineering, Politecnico di Torino (Torino, Italy). He lectures on Manufacturing Systems, Advanced Die Design and Production Technology courses. His recent research includes Human-Robot Collaboration in Assembly, Die-life Estimation through Finite Elements Simulation, Inclusive Production supported by Machine Learning and Robotics.Dorota StadnickaDorota Stadnicka works at Rzeszów University of Technology in the Faculty of Mechanical Engineering and Aeronautics as Associate Professor and head of Lean Learning Academy Polska. Her research interests are related to: Production Engineering; Intelligent manufacturing systems; Human-robot collaboration; System engineering; Sustainable development; Production Management; Knowledge Managem","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136097952","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":"Deep reinforcement learning for solving steelmaking-continuous casting scheduling problems under time-of-use tariffs","authors":"Ruilin Pan, Qiong Wang, Jianhua Cao, Chunliu Zhou","doi":"10.1080/00207543.2023.2267693","DOIUrl":"https://doi.org/10.1080/00207543.2023.2267693","url":null,"abstract":"AbstractThis paper proposes a novel intelligent scheduling method based on deep reinforcement learning (DRL) to solve the multi-objective steelmaking-continuous casting (SCC) scheduling problem, under time-of-use (TOU) tariffs for the first time. The intelligent scheduling system architecture is designed, and a mathematical model is established to minimise the total sojourn time and electricity cost. To effectively reduce production costs by avoiding peak periods of electricity consumption, the ‘start time’ of the system is generated based on the Markov Decision Process (MDP), and heuristic scheduling rules related to power cost are used as the action space, with corresponding reward functions designed according to the characteristics of these two objectives. To satisfy the continuous casting which is a particular SCC constraint, a backward strategy is developed. Additionally, a branching duelling double deep Q-network (BD3QN) is adapted to guide action selection and avoid blind search in the iteration process, and then applied to real-time scheduling. Numerical experiments demonstrate that the proposed method outperforms comparison algorithms in terms of solution quality and CPU times by a large margin.KEYWORDS: Steelmaking-continuous castingschedulingdeep reinforcement learningtime-of-use tariffsmulti-objective optimisation Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research work is supported by the National Natural Science Foundation of China [grant number 71772002], University Natural Science Research Project of Anhui Province (Key Project) [grant number KJ2021A0384], University Synergy Innovation Program of Anhui Province [grant number GXXT-2022-098].Notes on contributorsRuilin PanRuilin Pan received the Ph.D. degree in Enterprise Management from Dalian University of Technology, Dalian, China, in 2010. He is currently a Professor of Operations Management with the School of Management Science and Engineering, Anhui University of Technology, Anhui, China. His research interests include industrial data science, machine learning, and reinforcement learning. He has published papers in journals such as Annals of Operations Research, Swarm and Evolutionary Computation, Journal of Intelligent Manufacturing, European Journal of Operational Research, and Computers & Industrial Engineering.Qiong WangQiong Wang received the M.E. degree in Management Science and Engineering from Anhui University of Technology, Anhui, China, in 2022. Her research interests include operations planning and scheduling problems in production, mathematical modelling, optimisation and heuristic methods.Jianhua CaoJianhua Cao received the Ph.D. degree in Business Administration from Zhejiang University of Technology, Hangzhou, China, in 20","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136211412","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":"Integrating production, replenishment and fulfillment decisions for supply chains: a target-based robust optimisation approach","authors":"Daoheng Zhang, Hasan Hüseyin Turan, Ruhul Sarker, Daryl Essam","doi":"10.1080/00207543.2023.2266063","DOIUrl":"https://doi.org/10.1080/00207543.2023.2266063","url":null,"abstract":"AbstractIn this paper, a three-echelon supply chain problem under demand uncertainty is considered. The problem is formulated as a multiperiod two-stage stochastic optimisation model. The first stage, consisting of production and replenishment decisions, is integrated with the second stage, which comprises reactive fulfillment decisions, allowing seamless determination as demands are revealed over time. The demand in each period is characterised by an uncertainty set based on the nominal value and demand bounds. We propose a target-based robust optimisation (TRO) approach to determine the most robust planning with respect to a pre-specified cost target. The proposed TRO approach can trade off the total cost (performance) and model feasibility in the presence of demand perturbation (robustness) by fine-tuning the cost target. The robust counterpart is converted to a quadratically constrained linear programming (QCLP) problem, which can be solved by commercial solvers. Numerical experiments demonstrate that the TRO approach can outperform traditional robust optimisation methods in terms of both cost and feasibility against demand uncertainty by enabling precise adjustment of the cost target. Importantly, the TRO approach provides a flexible means to strike a balance between performance and robustness metrics, making it a valuable tool for supply chain planning under uncertain conditions.Keywords: Supply chain planningtarget-based robust optimisationdemand fulfillmentinventory poolinglateral transshipments Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementDerived data supporting the findings of this study are available from the corresponding author, Daoheng Zhang, on request.Additional informationFundingThis work was supported by University of New South Wales Canberra [Tuition Fee Scholarship].Notes on contributorsDaoheng ZhangDaoheng Zhang Daoheng Zhang is currently a Ph.D. student in Computer Science at UNSW Canberra. He received an MS degree in Management Science and Engineering from Nanjing University in 2017. His research areas are robust optimisation and its application to supply chain management.Hasan Hüseyin TuranHasan Hüseyin Turan H. Turan is a Lecturer and the Research Lead at Capability Systems Centre, UNSW Canberra. Before joining UNSW Canberra, he worked as a post-doc research fellow at Qatar University, Mechanical and Industrial Engineering Department from 2015 to 2017. He obtained his Ph.D. and master's degrees both in Industrial and Systems Engineering from Istanbul Technical University and North Carolina State University, respectively.Ruhul SarkerRuhul Sarker Ruhul A Sarker is a Professor in the School of Systems and Computing at UNSW Canberra. He served as the Director of Faculty PG Research (June 2015 to May 2020) and as the Deputy Head of School (Research) of the School of Engineering and IT (2011-2014). Prof. Sarker's broad research interests are decision analytics, o","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135093684","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":"Break up or tolerate? The post-disruption cooperation in global supply chains","authors":"Shibo Jin, Yong He, Shanshan Li, Xuan Zhao","doi":"10.1080/00207543.2023.2263584","DOIUrl":"https://doi.org/10.1080/00207543.2023.2263584","url":null,"abstract":"AbstractDue to globalisation and outsourcing, a manufacturer may suffer supply disruptions from the overseas supplier whose capacity is impaired by unruly events such as pandemic and geopolitical tensions. Since the recovery process of the overseas supplier’s capacity after the disruption is unpredictable, the manufacturer faces a choice of whether to continue cooperation or to shift to localised procurement. This paper first explores the effects of disruptions on the global supply chain, then considers the option to order from local suppliers. The results reveal that the overseas supplier whose capacity is affected by disruption at various degrees would take different actions, including raising the wholesale price, disguising its capacity impaired, or passing up the opportunity to cooperate with the manufacturer. In addition, we propose a tolerating strategy for the manufacturer and provide a long-term insight into supplier selection. The results show that the tolerating strategy can foster cooperation and enhance supply chain visibility. Notably, we find that manufacturers serving large markets can benefit from allowing the overseas supplier to recover gradually. Moreover, we discuss the importance of flexibility in designing the tolerating strategy.KEYWORDS: Global supply chainpost-disruptionsupplier selectionordering strategytolerating strategy AcknowledgementsThe work is supported by the National Natural Science Foundation of China (Nos. 72171047, 71771053 and 72001113), the Natural Science Foundation of Jiangsu Province (No. BK20201144), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX22_0249), and the Natural Science and Engineering Research Council of Canada Discovery Grant (No. 2018-06690).Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementAll data are available upon request.Notes1 https://www.accenture.com/us-en/about/company/coronavirus-supply-chain-impact2 https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/reimagining-the-auto-industrys-future-its-now-or-never3 https://www.tirebusiness.com/manufacturers/michelin-raising-consumer-tire-prices-us-canada-march-164 https://edition.cnn.com/2020/02/19/business/jaguar-land-rover-chinese-parts-coronavirus/index.html5 https://www.yicaiglobal.com/news/chinese-car-parts-makers-resort-to-charter-flights-to-keep-global-clients6 https://www.brecorder.com/news/5791417 https://english.kyodonews.net/news/2020/02/5734789c1857-update1-toyota-to-further-delay-restart-of-china-plants-due-to-virus-outbreak.html8 https://www.cnbc.com/2019/12/30/tesla-shanghai-factory-is-reportedly-making-1000-model-3s-per-week.htmlAdditional informationFundingThis work was supported by National Natural Science Foundation of China: [Grant Number 72171047, 71771053 and 72001113]; Natural Science Foundation of Jiangsu Province: [Grant Number BK20201144]; Natural Science and Engineering Research Council of Canad","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135481905","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 comprehensive literature review of the flowshop group scheduling problems: systematic and bibliometric reviews","authors":"Nilgün İnce, Derya Deliktaş, İhsan Hakan Selvi","doi":"10.1080/00207543.2023.2263577","DOIUrl":"https://doi.org/10.1080/00207543.2023.2263577","url":null,"abstract":"AbstractThis paper deals with an overview of flowshop group scheduling problems in the manufacturing environment. The aim of this paper is twofold: (i) making a comprehensive survey of research on flowshop group scheduling problems in manufacturing systems, and (ii) presenting a bibliometric analysis. We address the general definition of flowshop group scheduling problems and provide a taxonomy of methodologies used in previous literature. The papers are presented from several perspectives, including the utilised objective functions, a transformation of problem structure, benchmarks in existing literature, and solution approaches. Additionally, bibliometric analysis, including keyword and journal analyses, is conducted for articles published between 1986 and 2022. Finally, suggestions for future developments are listed to further consolidate this area.Keywords: Flowshop group scheduling problembibliometric analysissystematic analysiscellular manufacturingVOSviewer Disclosure statementNo potential conflict of interest was reported by the author(s).Data Availability StatementData sharing is not applicable to this article as no new data were created or analysed in this study.Additional informationNotes on contributorsNilgün İnceNilgün İnce is a Ph.D. candidate at Department of Industrial Engineering, Sakarya University, Turkey. She obtained BS degree in industrial engineering from Kütahya Dumlupınar University and MS degree in manufacturing systems engineering and management from University of Warwick (WMG) in 2018. She is funded by Republic of Turkey Ministry of National Education during master studies and participated projects in automotive manufacturing in UK. Her research interests include optimisation, hyper-heuristics and scheduling. She currently works as a lecturer at Alanya Alaaddin Keykubat University.Derya DeliktaşDerya Deliktaş is an associate professor at Department of Industrial Engineering in Faculty of Engineering, Kütahya Dumlupınar University, Turkey. She received the B.S. degree in industrial engineering and Ph.D. degree in industrial engineering from Erciyes University and Sakarya University, respectively. She did her post-doctoral research as a researcher supported by Scientific and Technological Research Council of Turkey (TÜBİTAK) in Computer Science and Operational Research with the Computational Optimisation and Learning (COL) Lab in the School of Computer Science at the University of Nottingham (UoN) in UK. Her research interests and activities are scheduling problems, assembly line balancing problems, portfolio optimisation, artificial intelligence methods, multi-criteria decision making methods, and data mining.İhsan Hakan Selviİhsan Hakan Selvi is an associate professor in Information Systems Engineering Department at Sakarya University, Turkey. He received the B.S. and Ph.D.degrees in industrial engineering from Sakarya University. He has been at Missouri Science and Technology University as a guest researcher. He works","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135480861","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":"Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic","authors":"Sreejith Balasubramanian, Vinaya Shukla, Nazrul Islam, Arvind Upadhyay, Linh Duong","doi":"10.1080/00207543.2023.2263102","DOIUrl":"https://doi.org/10.1080/00207543.2023.2263102","url":null,"abstract":"The COVID-19 pandemic exposed vulnerabilities in global healthcare systems and highlighted the need for innovative, technology-driven solutions like Artificial Intelligence (AI). However, previous research on the topic has been limited and fragmented, leading to an incomplete understanding of the ‘what’, ‘where’ and ‘how’ of its application, as well as its associated benefits and challenges. This study proposes a comprehensive AI framework for healthcare and assesses its effectiveness within the UAE's healthcare sector. It provides valuable insights into AI applications for healthcare stakeholders that range from the molecular to the population level. The study covers the different computational techniques employed, from machine learning to computer vision, and the various types of data inputs fed into these techniques, including clinical, epidemiological, locational, behavioural and genomic data. Additionally, the research highlights AI's capacity to enhance healthcare's operational, quality-related and social outcomes, and recognises regulatory policies, technological infrastructure, stakeholder cooperation and innovation readiness as key facilitators of AI adoption. Lastly, we stress the importance of addressing challenges such as data privacy, security, generalisability and algorithmic bias. Our findings are relevant beyond the pandemic in facilitating the development of AI-related policy interventions and support mechanisms for building resilient healthcare sector that can withstand future challenges.","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135697799","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":"The time-window strategy in the online order batching problem","authors":"Sergio Gil-Borrás, Eduardo G. Pardo, Ernesto Jiménez, Kenneth Sörensen","doi":"10.1080/00207543.2023.2263884","DOIUrl":"https://doi.org/10.1080/00207543.2023.2263884","url":null,"abstract":"AbstractWhen an order arrives at a warehouse it is usually assigned to a batch and a decision is made on how long to wait before assigning the batch to a picker and starting the picking tour. If the idle time of the pickers is minimised, the batch is immediately assigned, and the picking starts. Alternatively, if a time window is introduced, other orders may arrive, and more efficient batches may be formed. The method to decide how long to wait (the time-window strategy) is therefore important but, surprisingly, almost completely overlooked in the literature. In this paper, we demonstrate that this lack of attention is unwarranted, and that the time-window method significantly influences the overall warehouse performance. In the context of the online order batching problem (OOBP), we first demonstrate that the effects of different time-window strategies are independent of the methods used to solve the other subproblems of the OOBP (batching and routing). Second, we propose two new time-window strategies, compare them to existing methods, and prove that our methods outperform those in the literature under various scenarios. Finally, we show how time-window methods influence different objective functions of the OOBP when varying numbers of orders and pickers.Keywords: Online order batching problemtime windowfixed time windowvariable time windoworder pickingwarehousing 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 is freely available upon request and in the Appendix of this paper.Additional informationNotes on contributorsSergio Gil-BorrásSergio Gil-Borrás obtained his Ph.D. in Computer Science from Universidad Politécnica de Madrid in 2022. Additionally, he received his degree in Computer Engineering from the same university and completed a Master's degree in Cybersecurity from Universidad Carlos III de Madrid. He is currently working as a professor at Universidad Politécnica de Madrid and also collaborating with a research group on warehouse process optimisation, particularly the order batching problems, among other issues.Eduardo G. PardoEduardo G. Pardo received his Ph.D. in Computer Science from Universidad Rey Juan Carlos (Spain) in 2011. His research is focused on solving complex optimisation problems using Artificial Intelligence techniques. Among others, he is expert in the development of heuristic and metaheuristic algorithms. Currently, he is professor at the Computer Science School at Universidad Rey Juan Carlos (Spain).Ernesto JiménezErnesto Jiménez graduated in Computer Science from the Universidad Politécnica de Madrid (Spain) and got a Ph.D. in Computer Science from the University Rey Juan Carlos (Spain) in 2004. His research interests include Fault Tolerance in Distributed Systems, Computer Networks and Parallel and Distributed Processing. He is currently an associate professor at the Universidad P","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135740117","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":"Optimal vehicle fleet planning and collaboration under carbon neutrality: a game-theoretic perspective","authors":"Su Xiu Xu, Yu Ning, Huibing Cheng, Abraham Zhang, Yuan Gao, George Q. Huang","doi":"10.1080/00207543.2023.2262053","DOIUrl":"https://doi.org/10.1080/00207543.2023.2262053","url":null,"abstract":"AbstractThis paper studies the optimal vehicle fleet planning and collaboration problem for a fuel vehicle (FV) transport service provider, a commercial electric vehicle (CEV) transport service provider, and a carbon emission treatment agency under carbon neutrality. The FV transport service provider pays a fixed fee or a portion of its sales revenue to a carbon emission treatment agency in exchange for technology to reduce its carbon emissions, and it can adopt three strategies (i.e., no emission reduction, purchasing technology for emission reduction, and entrusting a carbon emission treatment agency). We derive each party’s optimal fleet size, price, and profit in the three scenarios. Our results suggest that carbon emission reduction strategies may improve the market performance of the FV transport service provider. Then, we find no certain strategy is always preferable to another: the optimal cooperation strategy between the transport service provider and carbon emission treatment agency depends on the fixed technology fee, ratio of revenue sharing, government penalty, the transport service market potential, and consumer green preference, as well as the cost per CEV. This paper gives the transport service provider and carbon emission treatment agency a full picture of whether, when, and how to collaborate in green commerce.KEYWORDS: Carbon neutralityvehicle fleet planningcollaboration strategy selectioncommercial electric vehicle (CEV)carbon emission reduction technology AcknowledgementsThe authors thank the reviewers and editors for their critical but constructive comments.Compliance with ethical standardsWe declare that this is our original work entitled ‘Optimal vehicle fleet planning under carbon neutrality: A game-theoretic perspective’, which has not been submitted to any other journals.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementAll data are included in the manuscript and can be used by anyone.Notes1 https://www.chinadaily.com.cn/a/202107/30/WS6103e3a8a310efa1bd6659f9.html.2 https://www.science.org/doi/full/10.1126/science.abm7149.3 https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement.4 https://www.europarl.europa.eu/news/en/headlines/priorities/climate-change/20190926STO62270/what-is-carbon-neutrality-and-how-can-it-be-achieved-by-2050.5 https://global.chinadaily.com.cn/a/202111/29/WS61a484f9a310cdd39bc78250.html.6 https://www.chinadaily.com.cn/a/202108/06/WS610c59d1a310efa1bd666f5d.html.7 http://en.yuandaem.com/.8 https://www.sglcarbon.com/en/.9 https://climate.ec.europa.eu/eu-action/transport-emissions/road-transport-reducing-co2-emissions-vehicles/co2-emission-performance-standards-cars-and-vans_en#penalties-for-excess-emissions10 https://www.clpsec.com/about-us/11 https://www.arup.com/services/technical-consulting/transport-consultingAdditional informationFundingThis work is supported by the National Natural Science Foundation of China u","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135740280","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":"Quality design based on kernel trick and Bayesian semiparametric model for multi-response processes with complex correlations","authors":"Shijuan Yang, Jianjun Wang, Xiaoying Cheng, Jiawei Wu, Jinpei Liu","doi":"10.1080/00207543.2023.2262065","DOIUrl":"https://doi.org/10.1080/00207543.2023.2262065","url":null,"abstract":"ABSTRACTProcesses or products are typically complex systems with numerous interrelated procedures and interdependent components. This results in complex relationships between responses and input factors, as well as complex nonlinear correlations among multiple responses. If the two types of complex correlations in the quality design cannot be properly dealt with, it will affect the prediction accuracy of the response surface model, as well as the accuracy and reliability of the recommended optimal solutions. In this paper, we combine kernel trick-based kernel principal component analysis, spline-based Bayesian semiparametric additive model, and normal boundary intersection-based evolutionary algorithm to address these two types of complex correlations. The effectiveness of the proposed method in modeling and optimisation is validated through a simulation study and a case study. The results show that the proposed Bayesian semiparametric additive model can better describe the process relationships compared to least squares regression, random forest regression, and support vector basis regression, and the proposed multi-objective optimisation method performs well on several indicators mentioned in the paper.KEYWORDS: Quality designBayesian inferencerandom walk priortensor B splinesemiparametric additive model Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data used to support the findings of the case study can be downloaded from the website https://figshare.com/articles/dataset/DATA_xlsx/22567336.Additional informationFundingThis work is supported by National Natural Science Foundation of China [grant numbers: 72301002, 72071001, 72171118]; Humanities and Social Sciences Planning Project of the Ministry of Education [grant numbers: 20YJAZH066, 21YJCZH148]; Excellent Young Talent Project of in Colleges and Universities of Anhui Province [grant number: gxyqZD2022001]; Science and Technology Project of Jiangxi Provincial Education Department [grant number: GJJ210528].Notes on contributorsShijuan YangShijuan Yang is a lecturer of School of Business at Anhui University, Hefei, China. She earned her Ph.D in Quality Management and Quality Engineering from Nanjing University of Science and Technology, China. Her research interests include applied statistics and quality management.Jianjun WangJianjun Wang is a Professor at the Department of Management Science and Engineering, Nanjing University of Science and Technology, China. He is a senior member of the Chinese Society of Optimization, Overall Planning, and Economical Mathematics. He is a reviewer of several international journals such as JQT, EJOR, IJPR, CAIE, and QTQM. His current research interests include quality engineering and quality management, robust parameter design, Bayesian modeling and optimisation, and industrial statistics.Xiaoying ChengXiaoying Chen is a Ph.D. candidate in Quality Management and Quality Engineering from Na","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136280343","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}