In response to violent market competition and demand for low-carbon economy, cold chain logistics companies have to pay attention to customer satisfaction and carbon emission for better development. In this paper, a biobjective mathematical model is established for cold chain logistics network in consideration of economic, social, and environmental benefits; in other words, the total cost and distribution period of cold chain logistics are optimized, while the total cost consists of cargo damage cost, refrigeration cost of refrigeration equipment, transportation cost, fuel consumption cost, penalty cost of time window, and operation cost of distribution centres. One multiobjective hyperheuristic optimization framework is proposed to address this multiobjective problem. In the framework, four selection strategies and four acceptance criteria for solution set are proposed to improve the performance of the multiobjective hyperheuristic framework. As known from a comparative study, the proposed algorithm had better overall performance than NSGA-II. Furthermore, instances of cold chain logistics are modelled and solved, and the resulting Pareto solution set offers diverse options for a decision maker to select an appropriate cold chain logistics distribution network in the interest of the logistics company.

中文翻译：

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考虑燃料消耗的冷链物流选址问题的超启发式方法。

为了应对激烈的市场竞争和对低碳经济的需求，冷链物流公司必须关注客户满意度和碳排放，以实现更好的发展。本文基于经济，社会和环境效益，建立了冷链物流网络的双目标数学模型。换句话说，优化了冷链物流的总成本和分配周期，而总成本包括货物损坏成本，制冷设备的制冷成本，运输成本，燃料消耗成本，时间窗的惩罚成本以及运营成本。配送中心。提出了一种多目标超启发式优化框架来解决该多目标问题。在框架中，为提高多目标超启发式框架的性能，提出了四种选择策略和四种接受标准。从比较研究中可以看出，该算法比NSGA-II具有更好的整体性能。此外，对冷链物流的实例进行了建模和求解，并且最终的Pareto解决方案集为决策者提供了多种选择，使决策者可以选择适合物流公司利益的冷链物流分销网络。