Collaborative logistics pickup and delivery problem with eco-packages based on time–space network

• Published in: Expert systems with applications 2021-05, Vol.170, p.114561, Article 114561
• Main Authors: Wang, Yong, Peng, Shouguo, Guan, Xiangyang, Fan, Jianxin, Wang, Zheng, Liu, Yong, Wang, Haizhong
• Format: Article
• Language: English
• Subjects: Cluster analysis; Clustering; Collaboration; Collaborative alliance; Dynamic programming; Eco-package; Freight transportation; Genetic algorithms; Integer programming; Logistics; Mixed integer; Multiple objective analysis; Operating costs; Packages; Pickup and delivery problem; Resource sharing; Sorting algorithms; Synchronism; Time synchronization; Time-space network; Transportation networks; Transportation systems; Trucks; Vector quantization
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2021.114561

•Collaborative logistics pickup and delivery problem with eco-packages is studied.•A multi-objective mixed-integer programming model is developed based on TS network.•A hybrid method combining multidimensional K-means, DP and RP-NSGA-II is devised.•The collaborative profit and alliance stability are tested under different methods.•Limited truck storage space sometimes undermines logistics operation efficiency. Collaboration among logistics companies offers a simple and effective way of increasing logistics operation efficiency. This study designs an optimal collaboration strategy by solving the collaborative logistics pickup and delivery problem with eco-packages (CLPDPE). This problem seeks to minimize the total operational costs by forming collaborative alliances and allocating trucking resources based on time–space (TS) network properties. The synchronization of two-echelon logistics networks is improved by solving this problem. Moreover, this study considers the stability of collaboration (i.e., the willingness of logistics companies to join and remain in collaborative alliances) by comparing different profit allocation strategies in the CLPDPE solving process. A novel methodology that combines multi-objective mixed integer programming, multidimensional K-means clustering, reference point based non-dominated sorting genetic algorithm-II (RP-NSGA-II), forward dynamic programming and improved Shapley value method is developed to formulate and solve CLPDPE. Our results show that the proposed algorithm outperforms most other algorithms in minimizing the total cost, waiting time and number of vehicles. An empirical case study in Chongqing city, China suggests that the proposed collaborative mechanism and transportation resource sharing strategy based on TS network can reduce cost, improve distribution efficiency, and contribute to efficient, smart, intelligent and sustainable urban logistics and transportation systems.