Integrated optimization of order splitting and distribution routing for the front warehouse mode e-retailing

Abstract

Along with the rapid growth of online purchasing, more and more e-retailers tend to adopt the front warehouse mode to improve the performance of customer service by locating multiple front warehouses with limited space and storage capacity closer to consumers. In this context, order splitting and distribution become increasingly crucial decisions that might impact the efficiency of order fulfillment. These two issues are investigated in the related works commonly as two independent problems, although they are inherently coupled with each other. This study established an integrated optimization model for order splitting and distribution routing for the front warehouse mode e-retailing. The model considers practical features, including order splitting constraints based on product type and quantity, finite inventory, heterogeneous vehicle routing constraints, and time windows. A branch-price-and-cut algorithm is proposed to solve the problem. Two logic-based Benders cuts are designed to deal with the infeasible distribution routes. The efficiency of the proposed algorithm is verified in an experimental study by considering CPLEX and two heuristic algorithms as benchmark methods. The dominance of our proposed algorithm is observed, especially for large-scale cases. The impacts of overlapping inventory levels, the combination of heterogeneous vehicles, and the width of time windows are also examined.

Keywords:

• E-retailing
• front warehouse
• order splitting
• vehicle routing problem
• benders decomposition
• branch-price-and-cut

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used to support the findings of this study are available from the corresponding author upon request.

Additional information

Funding

This work is partially supported by the National Natural Science Foundation of China [grant numbers 71974067, 71821001].

Notes on contributors

Jianqiang Tang

Jianqiang Tang received the B.E. degree in mathematics from Hefei University, HeFei, China, in 2017. He began to study in the School of Mathematics and Statistics, Huazhong University of Science and Technology in 2017–2019, engaged in operations research and cybernetics. He is currently a Ph.D. candidate in the School of Management at Huazhong University of Science and Technology. His research interests include logistics management and operations optimization, artificial intelligence, and emergency management.

Chao Qi

Chao Qi received her Ph.D. degree from Nanyang Technological University, Singapore, in 2006. She is currently a professor at School of Management, Huazhong University of Science and Technology. Her research interests focus on intelligent decision-making and automated planning and scheduling for management systems including production and logistics, social security and emergency management etc.

Hongwei Wang

Hongwei Wang received the B.Sc. degree in naval architecture and marine engineering and the Ph.D. degree in systems engineering from the Huazhong University of Science and Technology, Wuhan, China, in 1988 and 1993, respectivel. He is currently a Professor with the School of Management, Huazhong University of Science and Technology. His publications have appeared in the IEEE Transactions on Engineering Management, IEEE Transactions on Systems, Man and Cybernetics: Systems, IEEE Transactions on Fuzzy Systems, Omega, European Journal of Operational Research, International Journal of Production Research, Information Sciences, Computers and Industrial Engineering, Journal of Intelligent Manufacturing, and Applied Intelligence. His current research interests include engineering management, supply chain management, emergency management, and modeling and simulation of complex systems. Dr. Wang is the Executive Deputy Editor-in-Chief of Frontiers of Engineering Management.