Abstract
In this work, a multidepot multiperiod vehicle routing problem with pickups and deliveries (MDPVRPPD) is solved by optimizing logistics networks with collaboration and resource sharing among logistics service providers. The optimal solution can satisfy customer demands with periodic time characteristics and incorporate pickup and delivery services with maximum resource utilization. A collaborative mechanism is developed to rearrange both the open and closed vehicle routes among multiple pickup and delivery centers with improved transportation efficiency and reduced operational costs. The effects of resource sharing strategies combining customer information sharing, facility service sharing, and vehicle sharing are investigated across multiple service periods to maximize resource utilization and refine the resource configuration. A multiobjective optimization model is developed to formulate the MDPVRPPD so that the minimum total operational costs, waiting time, and the number of vehicles are obtained. A hybrid heuristic algorithm incorporating a 3D clustering and an improved multiobjective particle swarm optimization (IMOPSO) algorithm is introduced to solve the MDPVRPPD and find Pareto optimal solutions. The proposed hybrid heuristic algorithm is based on a selective exchange mechanism that enhances local and global searching capabilities. Results demonstrate that the proposed IMOPSO outperforms other existing algorithms. We also study profit allocation issues to quantify the stability and sustainability of long-term collaboration among logistics participants, using the minimum costs remaining savings method. The proposed model and solution methods are validated by conducting an empirical study of a real system in Chongqing City, China. This study contributes to the development of efficient urban logistics distribution systems, and facilitates the expansion of intelligent and sustainable supply chains.
Highlights
Reverse logistics has been widely adopted in the sustainable development of modern logistics, where the pickup and delivery systems are both incorporated to reduce the transportation cost and meet the regulations on environmental protection [1,2]
(1) A collaborative mechanism involving facilities providing pickup and delivery services is developed to improve the transportation efficiency in the MDPLNPD. (2) Resource sharing strategies are introduced to the MDPVRPPD to maximize resource utilization and facilitate sustainable logistics development
The 3D K-means clustering approach adopted in this paper focuses on reassigning each customer to their new nearest depot in accordance with the geographic coordinates and time windows, and generates the initial population of optimized vehicle routes for the multiobjective optimization approach
Summary
Reverse logistics has been widely adopted in the sustainable development of modern logistics, where the pickup and delivery systems are both incorporated to reduce the transportation cost and meet the regulations on environmental protection [1,2]. Many companies have begun controlling the complete life cycle of their products; in particular, organizers have taken it upon themselves to mitigate the environmental impact a product has during its lifetime from being produced to being consumed/disposed of/recycled In those cases, vehicles are more effectively utilized when performing commodity distribution to customers (i.e., forward logistics) [5] and returning products to the depot (i.e., reverse logistics) [6]. A collaborative mechanism and resource sharing strategy are constructed to determine optimal open–closed mixed vehicle routes, coordinate pickup and delivery services with improved operational efficiency and reasonable resource configuration, and, solve the MDPVRPPD. A real-life logistics network with pickups and deliveries is analyzed to study the collaborative mechanism and resource sharing strategies given multiple facilities and service periods, and demonstrate the validity and applicability of the proposed solution methodology.
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