Abstract
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.
Highlights
Cold chain logistics are developing rapidly with constant improvement of living standard of the people and increasing demand for fresh food [1]
In this paper, properties of cold chain logistics are considered from the perspective of location-routing problem (LRP)-based model; economic and environmental benefits and distribution timeliness are taken into account; thereby a biobjective LRP of cold chain logistics considering fuel consumption and distribution period is proposed, and one multiobjective hyperheuristic (MOHH) is proposed to model and solve this problem. erefore, main contributions of this paper are described as follows: (i) Problem model: with logistics cost and distribution period as objective functions, a multiobjective mathematical model for cold chain logistics is developed considering environmental benefit arising from fuel consumption and social benefits like customer satisfaction arising from customer time window
The machine learning methods have been used to solve the above dilemmas, such as obstacle recognition based on machine learning [14], this paper focuses on the another strategy, namely, hyperheuristics, which are capable of solving the above problems effectively
Summary
Cold chain logistics are developing rapidly with constant improvement of living standard of the people and increasing demand for fresh food [1]. Based on optimization objective functions of the minimum cost and the maximum customer satisfaction, Liu et al [4] developed a mathematical model for multiobjective location-routing problem (LRP). In this paper, properties of cold chain logistics are considered from the perspective of LRP-based model; economic and environmental benefits and distribution timeliness are taken into account; thereby a biobjective LRP of cold chain logistics considering fuel consumption and distribution period is proposed, and one multiobjective hyperheuristic (MOHH) is proposed to model and solve this problem. (i) Problem model: with logistics cost and distribution period as objective functions, a multiobjective mathematical model for cold chain logistics is developed considering environmental benefit arising from fuel consumption and social benefits like customer satisfaction arising from customer time window. (iii) Management suggestions and comments: based on experimental results, several insights and suggestions about logistics distribution are provided from management perspective
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