The Inventory Routing Problem Under Uncertainty

Zheng Cui (), Daniel Zhuoyu Long (), Jin Qi () and Lianmin Zhang ()
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Zheng Cui: School of Management, Zhejiang University, Hangzhou 310058, China
Daniel Zhuoyu Long: Department of Systems Engineering and Engineering Management, Chinese University of Hong Kong, New Territories, Hong Kong, China
Jin Qi: Department of Industrial Engineering and Decision Analytics, Hong Kong University of Science and Technology, Kowloon, Hong Kong, China
Lianmin Zhang: Department of Management Science and Engineering, School of Management and Engineering, Nanjing University, Nanjing 210093, China; Smart City, Transport and Logistics Big Data Lab, Shenzhen Research Institute of Big Data, Shenzhen 518172, China

Operations Research, 2023, vol. 71, issue 1, 378-395

Abstract: We study an uncertain inventory routing problem with a finite horizon. The supplier acts as a central planner who determines the replenishment quantities and also, the delivery times and routes to all retailers. We allow ambiguity in the probability distribution of each retailer’s uncertain demand. Adopting a service-level viewpoint, we minimize the risk of uncertain inventory levels violating a prespecified acceptable range. We quantify that risk using a novel decision criterion, the service violation index , that accounts for how often and how severely the inventory requirement is violated. The solutions proposed here are adaptive in that they vary with the realization of uncertain demand. We provide algorithms to solve the problem exactly and then, demonstrate the superiority of our solutions by comparing them with several benchmarks.

Keywords: Transportation; inventory routing; distributionally robust optimization; risk management; adjustable robust optimization; decision rule (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:71:y:2023:i:1:p:378-395

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