Distributed appointment assignment and scheduling under uncertainty

AbstractWe investigate a stochastic distributed appointment assignment and scheduling problem, which consists of assigning appointments to distributed service units and determining service sequences at each service unit. In particular, the service time duration and release time uncertainties are well-considered. The solution to this generic problem finds interesting applications in distributed production systems, healthcare systems, and post-disaster operations. We formulate the problem as a two-stage stochastic program to minimise the total transportation cost and expected makespan, idle time or overtime, and apply the sample average approximation method to make the problem tractable. We then develop a stochastic logic-based Benders decomposition method, decomposing the problem into a master problem and a subproblem. The master problem determines the appointment assignment variables, and the subproblem handles the sequence and service start time variables. Benders optimality cuts are generated from the subproblem's solution and added to the master problem. The developed stochastic logic-based method is advantageous since it can manage many scenarios in parallel. We further consider each appointment's due date, minimise the weighted earliness and tardiness, and adjust the developed method to solve this variant. Experiments on random instances demonstrate the excellent performance of the proposed model and methods.Keywords: Appointment schedulinguncertaintysample average approximationstochastic programminglogic-based Benders decomposition AcknowledgmentsWe thank two anonymous referees for their constructive comments that help improve a prior version of the paper.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are openly available at https://www.dmu-yantongli.com/instances.Additional informationFundingThis study is supported by the National Natural Science Foundation of China under Grant 72201044, 71901177 and 71971036, the Humanities and Social Sciences Foundation of the Ministry of Education under Grant 22YJC630071, the Social Science Planning Fund of Liaoning Province under Grant L22CGL007, the Postdoctoral Science Foundation of China under Grant 2022M710018, the Key Project Fund of Dalian Federation of Social Science under Grant 2022dlskzd238, the Natural Science Foundation of Shaanxi Province under Grant 2020JQ-224, and the Research Committee of The Hong Kong Polytechnic University under Grant P0039455 (W227).Notes on contributorsLi XueXue Li is an Assistant Professor in the School of Management at Northwestern Polytechnical University, China. He received the B.S. degree in electrical engineering from Xi'an Jiaotong University, China, in 2011 and the jointly awarded Ph.D. degree in management science and engineering from Xi'an Jiaotong University and City University of Hong Kong, in 2018. His research interests include the modeling, analysis, and optimization of complex systems. His research papers are published or accepted by some international journals, including Omega, European Journal of Operational Research, International Journal of Production Research.Yantong LiYantong Li received the B.S. degree in traffic and transportation from Beijing Jiaotong University, Beijing, China, in 2011, the M.S. degree in transportation planning and management from Military Transportation University, Tianjin, China, in 2013, and the Ph.D. degree in Automation from the University of Paris Saclay, Evry, France, in 2019.Li is an Associate Professor at Dalian Maritime University, Dalian, China. His research interests include planning and scheduling in production and logistics systems, integrated optimization in supply chain, smart logistics, and mathematical programming-based methods. His research papers are published or accepted by some international journals, including 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, and Computers & Operations Research.Zheng WangZheng Wang received his Ph.D. degree in Management Science from the Dalian University of Technology in 2007. He is currently a Full Professor at the Dalian Maritime University, Liaoning, China. His research interests include intelligent logistics systems and smart city logistics. He has published more than 70 papers at peer-review flag journals including Production and Operations Management, Transportation Research Part B: Methodological, Transportation Research Part E: Logistics and Transportation Review, Transportation Research Part C: Emerging Technologies, Annals of Operations Research and Computers & Operations Research till now.Sai-Ho ChungSai-Ho Chung is an Associate Professor and Associate Head in Department of Industrial Systems Engineering, The Hong Kong Polytechnic University. His research interests include logistics and supply chain management, supply chain collaboration, production scheduling, distributed scheduling, distribution network, etc. He has been the principal investigator of about ten research projects and authored and coauthored more than 90 international journal papers. He has guest edited a number of special issues for various journals, e.g. Transportation Research Part E, Industrial Management & Data Systems.Xin WenXin Wen is an Assistant Professor in Department of Industrial and Systems Engineering at The Hong Kong Polytechnic University. She has published in journals such as IEEE Transactions on Systems, Man, and Cybernetics – Systems, International Journal of Production Research, International Journal of Production Economics, and Transportation Research – Part E. Her current research interest is on transportation and logistics engineering.