Decomposition-based hyperheuristic approaches for the bi-objective cold chain considering environmental effects. (November 2020)

• Record Type: Journal Article
• Authors: Leng, Longlong; Zhang, Jingling; Zhang, Chunmiao; Zhao, Yanwei; Wang, Wanliang; Li, Gongfa
• Is Part Of: Computers & operations research. Volume 123(2020)
• Publication Date: 2020-11
• Subjects: Cold chain logistics -- Fuel consumption and carbon emission -- Location-routing problem -- Multi-objective hyperheuristic -- MOEA/D; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2020.105043
• Languages: English
• ISSNs: 0305-0548

Abstract

Highlights: A bi-objective model for the LRPLCCC with environmental, economic and social effects. Practical factors including SPD, HTW, HF, and the mixed transport are considered. A hyper-heuristic method based on MOEA/D are designed to solve the proposed problem. Abstract: This paper proposed a novel approach for a practical version of the cold chain, namely location-routing problem-based low-carbon cold chain (LRPLCCC). In the proposed bi-objective model, the first objective is the total logistics cost, including the fixed costs of the opened depots and leased vehicles, as well as the cost of fuel consumption and carbon emissions, and the second is to minimize the amount of quality degradation that aims at improving clients' satisfaction and maintain product freshness. The cargos of clients are classified into three types: general, refrigerated, and frozen cargos. Since the presented problem is NP-hard, a novel multi-objective hyperheuristic (MOHH) was proposed to obtain the Pareto solutions. In this framework, three selection strategies were developed to improve the performance of MOHH, that is, random simple, choice function, and FRR-MAB (fitness rate rank based multi-armed bandit), and three acceptance criteria using the decomposition approaches in MOEA/D were also developed, namely penalty-based boundary intersection, Tchebycheff, and modified Tchebycheff approaches. Extensive experiments were provided to verify the efficiency of the proposed algorithms and assessed the effects of algorithm parameters on the Pareto front. The results showed that the efficiency of the proposed algorithm outperforms several existing well-known multi-objective evolutionary algorithms (MOEA).