Particle swarm optimization hybridized with genetic algorithm for uncertain integrated process planning and scheduling with interval processing time

Abstract

Integrated process planning and scheduling (IPPS) is a hot research topic on providing a blueprint of efficient manufacturing system. Most existing IPPS models and methods focus on the static machining shop status. However, in the real-world production, the machining shop status changes dynamically because of external and internal fluctuations. The uncertain IPPS can better model the practical machining shop environment but is rarely researched because of its complexity (including the difficulties of modelling and algorithm design). To deal with the uncertain IPPS problem, this paper presents a new uncertain IPPS model with uncertain processing time represented by the interval number. A new probability and preference-ratio based interval ranking method is proposed for precise interval computation. Particle swarm optimization (PSO) algorithm hybridizing with genetic algorithm (GA) is designed to achieve the good solution. To improve the search capability of the hybrid algorithm, the special genetic operators are adopted corresponding to the characteristics of uncertain IPPS problem. Some strategies are designed to prevent the particles from trapping into a local optimum. Six experiments which are adopted from some famous IPPS benchmark problems have been used to evaluate the performance of the proposed algorithm. The experimental results illustrate that the proposed algorithm has achieved good improvement and is effective for uncertain IPPS problem.