Dynamic community partitioning for e-commerce last mile delivery with time window constraints

Ouyang，Zhiyuan1; Leung，Eric K.H.2; Cai，Yiji3; Huang，George Q.1

2023-12-01

10.1016/j.cor.2023.106394

Computers and Operations Research   影响因子和分区

0305-0548

1873-765X

Community logistics (CL) is a recently proposed delivery strategy designed to deal with e-commerce last-mile delivery scheduling by dynamically assigning vehicles to designated delivery regions partitioned into “communities”. Since optimizing vehicle routes is not mandatory in the CL spectrum, the delivery solution format and optimization process can be greatly simplified. Nevertheless, abandoning vehicle routes means vehicle arrival time at each customer specified delivery destination is unknown, resulting in the inability of handling time window constraints of e-commerce orders. To expand the application scope of CL, this study introduces community time window, an aggregation of identical or adjacent order time windows. Once the community time window for a delivery community is satisfied, all orders in this community can be received within designated time windows without determining vehicle routes. With this new concept, the application range of the CL is extended to e-commerce last mile delivery contexts where order time window constraints are considered. A dynamic community partitioning problem with the time window is presented based on the Markov decision process (MDP). An efficient heuristic solution framework based on policy function approximation is proposed to solve the MDP model. Numerical results show that the CL is very effective in dealing with the time window constraints of e-commerce orders.

Business to customer e-commerce Dynamic community partitioning problem Last mile delivery Logistics Time window constraints

SCI ; EI

英语

其他

Guangdong Special Support Talent Program - Innovation and Entrepreneurship Leading Team (China)[2019BT02S593] ; 2018 Guangzhou Leading Innovation Team Program[201909010006] ; HKSAR RGC GRF Project[17203518]

Computer Science ; Engineering ; Operations Research & Management Science

Computer Science, Interdisciplinary Applications ; Engineering, Industrial ; Operations Research & Management Science

WOS:001068417300001

PERGAMON-ELSEVIER SCIENCE LTD

20233614665546

Electronic commerce ; Optimization ; Vehicles

Computer Applications:723.5 ; Optimization Techniques:921.5 ; Probability Theory:922.1

COMPUTER SCIENCE

2-s2.0-85169292345

Scopus

1.Department of Industrial and Systems Engineering,The Hong Kong Polytechnic University,Hung Hom,Hong Kong
2.Management School,University of Liverpool,Liverpool,United Kingdom
3.College of Business,Southern University of Science and Technology,Shenzhen, Guangdong,China

Ouyang，Zhiyuan,Leung，Eric K.H.,Cai，Yiji,et al. Dynamic community partitioning for e-commerce last mile delivery with time window constraints[J]. Computers and Operations Research,2023,160.

Ouyang，Zhiyuan,Leung，Eric K.H.,Cai，Yiji,&Huang，George Q..(2023).Dynamic community partitioning for e-commerce last mile delivery with time window constraints.Computers and Operations Research,160.

Ouyang，Zhiyuan,et al."Dynamic community partitioning for e-commerce last mile delivery with time window constraints".Computers and Operations Research 160(2023).