Collaborative multicenter logistics delivery network optimization with resource sharing

Abstract

Collaboration among logistics facilities in a multicenter logistics delivery network can significantly improve the utilization of logistics resources through resource sharing including logistics facilities, vehicles, and customer services. This study proposes and tests different resource sharing schemes to solve the optimization problem of a collaborative multicenter logistics delivery network based on resource sharing (CMCLDN-RS). The CMCLDN-RS problem aims to establish a collaborative mechanism of allocating logistics resources in a manner that improves the operational efficiency of a logistics network. A bi-objective optimization model is proposed with consideration of various resource sharing schemes in multiple service periods to minimize the total cost and number of vehicles. An adaptive grid particle swarm optimization (AGPSO) algorithm based on customer clustering is devised to solve the CMCLDN-RS problem and find Pareto optimal solutions. An effective elite iteration and selective endowment mechanism is designed for the algorithm to combine global and local search to improve search capabilities. The solution of CMCLDN-RS guarantees that cost savings are fairly allocated to the collaborative participants through a suitable profit allocation model. Compared with the computation performance of the existing nondominated sorting genetic algorithm-II and multi-objective evolutionary algorithm, AGPSO is more computationally efficient. An empirical case study in Chengdu, China suggests that the proposed collaborative mechanism with resource sharing can effectively reduce total operational costs and number of vehicles, thereby enhancing the operational efficiency of the logistics network.

Multi-Depot Pickup and Delivery Problem with Resource Sharing

“…Wang et al [14] adopted the cooperation strategy to optimize the MDVRPPDTW with minimization of the operating cost of the transportation network and the total number of vehicles. Deng et al [21] allowed the capabilities of the logistics facility, the vehicle resources, and the customer information to be shared through RS strategies, and they proved that this strategy can improve the utilization of logistics resources. In the study of Nourinejad et al [72], vehicles can be used multiple times to reduce fleet size by extending vehicle reservation time.…”

“…RS is an effective strategy that can optimize logistics operation costs and resource allocation in a multicenter logistics network with pickups and deliveries [21,35]. In this study, the logistics network consists of multiple distribution centers (DCs), multiple pickup centers (PCs), and multiple customers.…”

“…First, the reallocation of customers between facilities facilitates the rearrangement of vehicles, which can avoid staggering and long-distance service. C20 [10,11] C1 [10,15] C5 [11,14] C27 [11,12] C34 [9,10] C13 [11,12] C42 [13,14] C12 C45 [14,15] C15 [17,18] C16 [15,17] C36 [15,16] C52 [12, 13] [11, 12] C32 C28 [11,12] C30 [13,14] C37 [11,12] [10, 11] C9 C38 [14,15] C40 [14,15] C22 [15,16] C51 [16,17] C46 [21,22] C47 [20,21] C44 [19,20] C48 [11,12] C50 [12,13] C43 [13,14] C49…”

“…Customer clustering is an important measure to reduce the complexity of solving MDVRPPDTW [36]. K-means algorithm is widely used to solve MDVRPTW due to its simplicity and efficiency [14,21]. e k-means clustering pseudocode based on Manhattan distance is listed in Algorithm 1.…”

“…With regard to RS, it is often jointly adopted with collaboration or cooperation between LSPs to optimize the logistics networks with multiple depots [21,22]. RS strategy supports the sharing of customer information and transport resources to improve the resource configuration among logistics facilities to optimize the logistics network [23,24].…”