关键词: 易腐品供应链网络规划, 多渠道选择, 自适应混合策略, 改进麻雀搜索算法

Abstract: With the development of the economy and consumers’ demand for high-quality products, enterprises have to strengthen their competitiveness through the overall planning of the supply chain. Considering only the traditional single-channel operation mode, we are unable to meet the current market requirements, so the multi-channel distribution choice of products is now the problem of supply chain network planning. Compared with other products, perishables have the perishability. The quality of products will decrease with time, affect consumer satisfaction with the products and increase the cost of the supply chain network. The operational model for improving the competitiveness of perishables enterprises should simultaneously consider the cost and customer satisfaction.
This paper combines the concept of perishables and constructs a perishable supply chain network, consisting of multiple manufacturers, processing factories, distribution markets, and consumer markets. This supply chain network can be applied to different types of tangible perishables. A cost function is constructed based on the multi-channel distribution network, where the total cost of perishables is jointly influenced by factors such as their own sales price, transportation time, transportation volume, and the sensitivity coefficient of perishables themselves. Simultaneously, based on the initial freshness of the product and transportation time, the customer satisfaction function is constructed to achieve the goal of minimizing the cost of the perishable supply chain network and maximizing customer satisfaction. By using linear weighting method, the multi-objective is transformed into a single objective numerical problem to solve the problem of minimum total cost and maximum customer satisfaction for different weights by the decision maker, plan the perishables supply chain network and provide a basis for management decision-making.
Aiming at the deficiencies of sparrow search algorithm(SSA), an improved sparrow search algorithm (ISSA) is proposed. On the basis of SSA, Tent chaotic mapping is introduced to initialize the population, increase the population number and merge the two populations. Then the elite population is obtained by the elite strategies to improve the quality of initial solutions. Introducing an adaptive periodic convergence factor α is to enhance search capability and convergence speed. The update method for the follower and warning position is adjusted to prevent the algorithm from falling into local optimization to a certain extent. Polynomial variation perturbation is introduced to solve the problem of SSA falling into local optimization. In order to verify the improvement effect of ISSA, genetic algorithm(GA), particle swarm optimization(PSO), SSA and test functions of different dimensions are introduced to predict the performance of the algorithm. The optimization results show that the quality, stability, and convergence speed of the algorithm solved by ISSA significantly improve, which is superior to the compared algorithms mentioned above. The use of time complexity analysis proves that the performance improvement of ISSA algorithm is not achieved by sacrificing time.
This paper takes the supply chain network of a certain dairy product as the research object, provides parameter information of the perishable supply chain network based on enterprise operation data, and uses the improved sparrow search algorithm to solve the model. The results indicate that an increase in total cost mainly depends on the increase in perishable costs, and the sensitivity of perishables to time determines the level of perishable costs. By analyzing different decision weights, the results show that there is a balanced compromise solution in the network planning of the perishable supply chain to obtain the optimal network planning scheme with overall satisfaction.
The model in this paper is based on the assumption that there are no transportation delays or other situations. However, in real life, transportation delays and other situations are commonly present in supply chain networks. Therefore, relevant case data can be collected for further verification and optimization research.

Key words: supply chain network planning of perishables, multi-channel selection, adaptive hybrid strategy, improved sparrow search algorithm