Collaborative multiple centers fresh logistics distribution network optimization with resource sharing and temperature control constraints. (1st March 2021)

• Record Type: Journal Article
• Authors: Wang, Yong; Zhang, Jie; Guan, Xiangyang; Xu, Maozeng; Wang, Zheng; Wang, Haizhong
• Is Part Of: Expert systems with applications. Volume 165(2021)
• Publication Date: 2021-03-01
• Subjects: Fresh logistics distribution network -- Collaborative mechanism -- Hybrid heuristic algorithm -- Cost saving -- Profit allocation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113838
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Propose a collaborative mechanism to coordinate logistics participants in an FLDN. Establish a multi-objective linear optimization model to formulate the CMCVRP-RSTC. Design a hybrid method including extended k-means and TS-NSGA-II to solve the model. Conduct an empirical study to demonstrate the applicability of proposed approach. Implement a sensitivity analysis to identify the optimal controlled temperatures. Abstract: Collaboration such as resource sharing among logistics participants (LPs) can effectively increase the efficiency and sustainability of logistics operations, especially in the transportation and distribution of fresh and perishable products that require special infrastructure (e.g., refrigerated trucks/vehicles). This study tackles a collaborative multi-center vehicle routing problem with resource sharing and temperature control constraints (CMCVRP-RSTC). Solving the CMCVRP-RSTC by minimizing the total cost and the number of refrigerated vehicles returns a fresh logistics operational strategy that pinpoints how a multi-center fresh logistics distribution network can be reorganized to highlight potential collaboration opportunities. To find the solution to the CMCVRP-RSTC, we develop a hybrid heuristic algorithm that combines the extended k-means clustering and tabu search non-dominated sorting genetic algorithm-II (TS-NSGA-II) to search a large solution space. This hybrid heuristic algorithm ensures that the optimal solution is found efficiently through initial solution filtering and the combination of local and global searches. Furthermore, we explore how to motivate individual LPs to collaborate by analyzing the benefits of collaboration to each LP. Using the minimum costs remaining savings method and the strictly monotonic path rule, a cost saving calculation model is proposed to find the best profit allocation scheme where each collaborating LP keeps benefiting from long-term collaboration. An empirical case study of Chongqing City, China indicates the efficiency of our proposed collaborative mechanism and optimization algorithms. Our study will help improve the efficiency of logistics operation significantly and contribute to the development of more intelligent logistics systems and smart cities.