Integrated multi-plant collaborative production, inventory, and hub-spoke delivery of make-to-order products

AbstractMotivated by make-to-order applications with committed delivery dates in a variety of industries, we investigate the integrated multi-plant collaborative production, inventory, and hub-spoke delivery problem in a complex production-distribution network. This network includes multi-location heterogeneous plants, distribution centers, and customers, for producing customized and splittable orders with one or more general-size multi-type jobs. Completed jobs are transported from plants to distribution centers, and then the orders whose all constituent jobs have arrived are delivered from distribution centers to customer sites. The objective is to make integrated scheduling decisions for production, inventory, and delivery, for minimizing total cost composed of production, transportation, tardiness, and inventory. We first formulate this problem as a mixed-integer programming model, and analyze its intractability by proving that the problem is NP-hard and no approximation algorithms exist with a constant worst-case ratio. We then reformulate this problem as a binary integer linear programming model to select a feasible schedule for each job, and propose a combined column generation and two-layer column enumeration algorithm to solve it. Through extensive numerical experiments, we demonstrate that our proposed algorithm is capable of generating optimal or near-optimal solutions expeditiously and outperforms four benchmark approaches, and gain valuable managerial insights for practitioners.Keywords: Customized and splittable ordersintegrated schedulingmulti-plant production and hub-spoke deliverymixed-integer programmingcolumn generation and column enumerationDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Additional informationNotes on contributorsKefei LiuKefei Liu is a Ph.D. candidate in Management Science and Engineering from Antai College of Economics & Management, Shanghai Jiao Tong University (SJTU), Shanghai, China. Her main research interests include operations management of manufacturing systems.Zhibin JiangZhibin Jiang is currently a distinguished Professor with the Antai College of Economics & Management, SJTU, Shanghai, China. He is also the Dean of the Sino-US Global Logistics Institute of SJTU. He received a Ph.D. degree in Engineering Management from the City University of Hong Kong, Hong Kong, China, in 1999. He is a fellow of the Institute of Industrial and Systems Engineers and an Associate Editor of the International Journal of Production Research. His research interests include discrete-event modeling and simulation, and operations management in manufacturing and health care systems.Liping ZhouLiping Zhou is currently an Associate Professor with the Sino-US Global Logistics Institute, Antai College of Economics & Management, SJTU, Shanghai, China. He received a Ph.D. degree in Industrial Engineering from SJTU, Shanghai, China, in 2019. His research interests include operations management of manufacturing systems.