Hybrid Flow-Shop Scheduling Approach Based On Multi-Objective Genetic Particle Swarm Hybrid Algorithm

With the rapid development of the global economy, the manufacturing business confronts new challenges, to secure victories in the competitive market, enterprises should have the lowest cost, best quality, fasted speed and best service to respond to the market, ob shop scheduling is to solve the problem of how to use limited resources to satisfy the various production constraint, determine the processing sequence and time of the work piece and equipment, in order to get the optimal. However, the actual production scheduling in the process of enterprise, generally will not only consider a target, often at the same time to consider multiple objectives, therefore, multi-objective hybrid flow-shop scheduling problem(HFSP) has great significance to research.Based on the genetic algorithm and particle swarm algorithm, we propose a multi-objective genetic particle swarm hybrid algorithm for HFSP. Genetic algorithm has robust and group strong searching ability, but it has the existence of premature convergence and low search efficiency in later period problem. Particle swarm optimization has the simple in calculation and high efficiency characteristics, but it also has the shortcomings of premature and local optimum. On the analysis basis of genetic algorithm and particle swarm algorithm" advantages and disadvantages, keeping the evolution direction on the whole by genetic algorithm’excellent ability on group optimization aspect, taking advantage of characteristics of particle swarm algorithm about simple in calculation and high efficiency. Firstly, start the evolutions of multiple particle swarm independently, particle swarm algorithm is also carried out between each individual migration, to expand the search field, then collect the best individual in the particle swarm to make up the initial population of genetic algorithm, then make the genetic manipulation, the obtained solution is dominated individual in lieu of the more inferior individuals, so the cycle to repeat, until the optimal solution is found efficiently. Based on the detailed analysis of the HFSP, we established a complete set of multi-objective genetic particle swarm hybrid algorithm.We have realized how to use multi-objective genetic particle swarm hybrid algorithm to solve HFSP, According to the common optimization of enterprise in the production, HFSP model is established, based on it, tested by a classic example of HFSP, analysis and evaluate the efficiency of the algorithm, and the conclusion of this algorithm is compared with other algorithms, the results show that, the algorithm has obvious advantages, can effectively solve HFSP, has a good application prospect.