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).

中文翻译：

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考虑环境影响的双目标冷链基于分解的超启发式方法

摘要 本文提出了一种实用型冷链的新方法，即基于位置路由问题的低碳冷链（LRPLCCC）。在提出的双目标模型中，第一个目标是总物流成本，包括开设的仓库和租赁车辆的固定成本，以及燃料消耗和碳排放的成本，第二个是最小化物流成本。旨在提高客户满意度和保持产品新鲜度的质量降级。客户的货物分为三类：普通货物、冷藏货物和冷冻货物。由于所提出的问题是 NP-hard，因此提出了一种新的多目标超启发式 (MOHH) 来获得帕累托解。在这个框架中，开发了三种选择策略来提高 MOHH 的性能，即，随机简单、选择函数和 FRR-MAB（基于适应度等级的多臂老虎机），以及使用 MOEA/D 中的分解方法的三个验收标准，即基于惩罚的边界交叉、Tchebycheff 和修改的 Tchebycheff 方法. 提供了大量实验来验证所提出算法的效率并评估算法参数对帕累托前沿的影响。结果表明，所提出算法的效率优于现有的几种著名的多目标进化算法（MOEA）。提供了大量实验来验证所提出算法的效率并评估算法参数对帕累托前沿的影响。结果表明，所提出算法的效率优于现有的几种著名的多目标进化算法（MOEA）。提供了大量实验来验证所提出算法的效率并评估算法参数对帕累托前沿的影响。结果表明，所提出算法的效率优于现有的几种著名的多目标进化算法（MOEA）。