An improved particle swarm optimization algorithm for the distribution of fresh products

In the application field of fresh products distribution. it is necessary to use inulti-compartment vehicles for distribution because of their particular demands for temperature. This paper studied the multi-compartment vehicle routing problem with soft time windows for multiple fresh products distribution. Firstly, a mathematical model of the issued problem was built, which aims to minimize the total cost including vehicle cost, delivery cost, refrigeration cost, damage cost and penalty cost of delivery time. Then, we presented an improved particle swarm optimization algorithm to solve this problem. In the process of particle updating, the sequential crossover operator usually used in genetic algorithm was introduced to enhance the diversity of particles. Finally, the proposed algorithm was evaluated on some benchmark instances, and the experiment results demonstrate its effectiveness and good stability, when compared with genetic algorithm and simulated annealing algorithm. It can draw a conclusion that the proposed algorithm can provide a reliable and stable solution approach for the distribution of fresh products.