Optimizing the ground intra-city express delivery network: An integrated multiple centrality assessment, multi-criteria decision-making, and multi-objective integer programming model

Abstract

Optimization of an intra-city express delivery network from three to two levels is of great interest to suppliers and customers for reducing costs and improving service efficiency. One feasible solution is to identify critical nodes in the three-level network and upgrade them as transshipment facilities in the two-level one. However, traditional optimization models seldom combine empirical business data, composite metrics, and objective evaluation rules. We proposed an approach integrating empirical data, multi-criteria decision-making methods based on the real-world application of the SF Express Chengdu branch. We also developed a mathematical optimization model using statistical and operations management techniques combined with logistics expertise for a location decision. First, the appropriateness of each service point as a candidate transshipment facility is evaluated from internal and external perspectives by applying multiple centrality assessment from complex network theory and fuzzy Technique for Order Preference by Similarity to an Ideal Solution, respectively. Second, 16 candidate transshipment facilities are selected by combining these two ways. Then, a multi-objective integer programming model is built to obtain the optimal number, locations of transshipment facilities, and the corresponding service points covered by each transshipment facility. Using this multi-methodologic approach, we show that the optimized two-level network is economically feasible and simply applicable, with the total cost and average delivery time reduced by 18.41% and 6 h, respectively. This article is of practical significance and provides an important reference for optimizing ground express service networks for other large cities.

Keywords:

• Fuzzy TOPSIS
• ground intra-city express delivery network
• multi-objective integer programming
• multiple centrality assessment
• optimization

Disclosure statement

No potential conflict of interest was reported by the authors.

Acknowledgment

We would like to thank the SF Express Chengdu branch for their data assistance in the investigation.

Additional information

Funding

This work was supported by the Joint Project of China Society of Logistics and China Federation of logistics & Purchasing (grant number 2021CSLKT3-146), Sichuan Research Center of Electronic Commerce and Modern Logistics (grant number DSWL21-12, DSWL-1), Sichuan Research Center of Educational Informatization Application and Development (grant number JYXX21-005), Cold Chain Logistics Scientific Research and Innovation Team Construction Project of Sichuan Tourism University (grant number 21SCTUTY08), Research Center of Cuisine Development (grant number CC22G02), and Sichuan Research Center of Minority Mountain Economic Development (grant number SDJJ202216).