An improved salp swarm algorithm for permutation flow shop vehicle routing problem.

Permutation flow shop is a typical production method in discrete manufacturing. In reality, in order to reduce the inventory cost, enterprises need to deliver the produced products to customers in time. Therefore, enterprises need to consider the logistics transportation scheme when making production plans, and minimize the total cost of production and transportation through the collaborative optimization of production scheduling and logistics transportation scheduling. Permutation flow shop vehicle routing problem is studied in this paper. Aiming at the requirements of collaborative optimization of production scheduling and logistics transportation scheduling, a mathematical model of the problem is established, and an improved salp swarm algorithm is proposed to solve it. In order to improve the performance of the algorithm, the proposed algorithm incorporates local search operation to enhance the exploration of the population space. Simulation results show that compared with simulated annealing, genetic algorithm and particle swarm optimization algorithm, the proposed algorithm has better optimization ability. The example application shows that the proposed algorithm can effectively solve permutation flow shop vehicle routing problem.