Multi-Temperature Co-Matching Path Optimization Considering Customer Value Under Fuzzy Time Windows

Customer relationship theory is widely used in the field of logistics, and effective customer relationship management cannot be achieved without the support of customer value. Accurate analysis of customer value can aid enterprises in resource allocation and maximizing mutual interests. Therefore, a multi-objective multi-temperature co-distribution path optimization model is established based on differences among customer values. This model adopts a fuzzy time window distribution method that closely reflects reality and considers constraints such as commodity perishability and vehicle loading capacity. To solve the problem, an improved artificial bee colony algorithm is designed by considering the disadvantages of the artificial bee colony algorithm, such as slow convergence speed and prematureness. The AP (Affinity Propagation) algorithm is used for initial clustering, while the clustering behavior in the artificial fish colony algorithm is introduced to enhance its merit-seeking ability. The experimental results demonstrate that compared to the model that does not consider customer value and satisfaction, the model has improved cold chain logistics service by increasing customer value and satisfaction by 15.81% and 13.06%, respectively, with a 1.12% increase in total cost.