Simulation-based heuristic method for container supply chain network optimization

• Published in: Advanced engineering informatics Vol. 29; no. 3; pp. 339 - 354
• Main Authors: He, Junliang, Huang, Youfang, Chang, Daofang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2015
• Subjects: Computer simulation; Container supply chain; Containers; Genetic algorithm; Genetic algorithms; Heuristic methods; Mathematical models; Network architecture; Networks; Particle swarm optimization; Searching; Simulation-based heuristic method; Supply chains; Network architecture; Container supply chain; Simulation-based heuristic method; Genetic algorithm; Particle swarm optimization
• ISSN: 1474-0346
• DOI: 10.1016/j.aei.2014.08.001

•We propose a novel idea that considers optimization problem of multi-echelon container supply chain network.•We develop a mixed integer programming model to minimize the total container supply chain service cost.•We propose a simulation-based heuristic method to efficiently solve the problem. With the emerging of free trade zones (FTZs) in the world, the service level of container supply chain plays an important role in the efficiency, quality and cost of the world trade. The performance of container supply chain network directly impacts its service level. Therefore, it is imperative to seek an appropriate method to optimize the container supply chain network architecture. This paper deals with the modeling and optimization problem of multi-echelon container supply chain network (MCSCN). The problem is formulated as a mixed integer programming model (MIP), where the objective is subject to the minimization of the total supply chain service cost. Since the problem is well known to be NP-hard, a novel simulation-based heuristic method is proposed to solving it, where the heuristic is used for searching near-optimal solutions, and the simulation is used for evaluating solutions and repairing unfeasible solutions. The heuristic algorithm integrates genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, where the GA is used for global search and the PSO is used for local search. Finally, computational experiments are conducted to validate the performance of the proposed method and give some managerial implications.