Particle swarm optimization for integrated production-distribution scheduling problem

This paper introduces an artificial intelligent technique to solve a complex problem, an integrated production-distribution scheduling (IPDS) problem. The problem governs with capacitated single-facility, multi-product, multi-retailer, and a homogenous vehicle fleet. The objective is to minimize the total costs of the whole supply chain. We make use of an evolutionary computation technique, particle swarm optimization (PSO), to solve the problem. The proposed method is tested on 7 datasets and compared with a competitive optimizer, CPLEX. The results show that the proposed method yields acceptable solutions with low computational time.