Design and Profit Allocation in Two-Echelon Heterogeneous Cooperative Logistics Network Optimization

Yong Wang,Ke Gong,Haizhong Wang,Kevin Assogba,Maozeng Xu,Yingying Yuan,Yinhai Wang

doi:10.1155/2018/4607493

Abstract

In modern supply chain, logistics companies usually operate individually and optimization researches often concentrate on solving problems related to separate networks. Consequences like the complexity of urban transportation networks and long distance deliveries or pickups and pollution are leading problems to more expenses and more complaints from environment protection organizations. A solution approach to these issues is proposed in this article and consists in the adoption of two-echelon heterogeneous cooperative logistics networks (THCLN). The optimization methodology includes the formation of cooperative coalitions, the reallocation of customers to appropriate logistics facilities, and the determination of the best profit allocation scheme. First, a mixed integer linear programing model is introduced to minimize the total operating cost of nonempty coalitions. Thus, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm are hybridized to propose GA-PSO heuristics. GA-PSO is employed to provide good solutions to customer clustering units’ reallocation problem. In addition, a negotiation process is established based on logistics centers as coordinators. The case study of Chongqing city is conducted to verify the feasibility of THCLN in practice. The grand coalition and two heterogeneous subcoalitions are designed, and the collective profit is distributed based on cooperative game theory. The Minimum Cost Remaining Savings (MCRS) model is used to determine good allocation schemes and strictly monotonic path principles are considered to evaluate and decide the most appropriate coalition sequence. Comparisons proved the combination of GA-PSO and MCRS better as results are found closest to the core center. Therefore, the proposed approach can be implemented in real world environment, increase the reliability of urban logistics network, and allow decision makers to improve service efficiency.

Highlights

Cooperative game theory consists in the formation of coalitions and the effective allocation of the collective payoff to each participant
Through local and global search capabilities, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been successfully applied to solve problems in management, engineering, and pure science domains. Considering their individual abilities, we propose GA combined with PSO in a hybrid algorithm, which inherits the merits of both approaches and increases the probability of obtaining optimal solutions
In the design of the grand coalition, we have considered LCP joining first the coalition followed by PC1 and PC2 in LCP 11.9% PC1 20.2% PC2 14.9%

Summary

Introduction

Cooperative game theory consists in the formation of coalitions and the effective allocation of the collective payoff to each participant. Coalitions can be made up of facilities from either the same level or different echelons. In a network comprising logistics centers (LCs), Distribution Centers (DCs), Pickup Centers (PCs), and customers, coalitions are generally formed vertically with LCs, DCs, and PCs or horizontally among DCs or PCs. A. Two-echelon Heterogeneous Cooperative Logistics Network (THCLN) is a collaborative network with vertical synergies among LCs, DCs, and PCs, homogeneous horizontal synergies among DCs or PCs, and heterogeneous synergies among LCs and DCs or PCs. Properly optimizing THCLN can enhance the operational efficiency and avoid waste of resources, and maximize players’ profits.

Methods

Findings

Discussion

Conclusion