Coordinated delivery to shopping malls with limited docking capacity

• Main Authors: SONG, Ruidian, LAU, Hoong Chuin, LUO, Xue, ZHAO, Lei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Coordinated delivery; Docking capacity; Endogenous time window; Stochastic travel and service times; Adaptive large neighborhood search; Numerical Analysis and Scientific Computing; Operations and Supply Chain Management; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/6233; https://ink.library.smu.edu.sg/context/sis_research/article/7236/viewcontent/TSci_2021_mall_delivery_sv.pdf; https://ink.library.smu.edu.sg/context/sis_research/article/7236/filename/0/type/additional/viewcontent/TSci_2021_mall_delivery_appendices.pdf
• Institution: Singapore Management University

Shopping malls are densely located in major cities such as Singapore and Hong Kong. Tenants in these shopping malls generate a large number of freight orders to their contracted logistics service providers, who independently plan their own delivery schedules. These uncoordinated deliveries and limited docking capacity jointly cause congestion at the shopping malls. A delivery coordination platform centrally plans the vehicle routes for the logistics service providers and simultaneously schedules the dock time slots at the shopping malls for the delivery orders. Vehicle routing and dock scheduling decisions need to be made jointly against the backdrop of travel time and service time uncertainty and subject to practical operations rules. We model this problem as a two-stage stochastic mixed integer program, develop an Adaptive Large Neighborhood Search algorithm that approximates the second stage recourse function using various sample sizes, and examine the associated in-sample and out-of-sample stability. Our numerical study on a testbed of instances based on real data in Singapore demonstrates the value of coordination and the value of stochastic solutions.