Solution Of Complex Production Scheduling Problem Based On Discrete Particle Swarm Optimization And Hybrid Differential Evolution Algorithm

With the recent development, Production scheduling problems (PSPs) often has some of special complexities, such as NP-hard, large-scale, and strong constraint. The study and investigates on the effective algorithms of PSPs are so many, and in order to the result, both industry and academia has paid more and more attention to this problem. As a simple and effective intelligent algorithm, particle swarm optimization algorithm(PSO) and differential evolution (DE) have attracted much attention in variety of fields, in addition, some people gained a lot of successful applications in their studies. Thus, this dissertation aims at studying scheduling algorithms based on hybrid DE and disperse PSO for three kinds of important PSPs. The main work of this dissertation is summarized as follows:Firstly, after analyzing and studying the structural properties of the no-wait reentrant flow-shop scheduling problems, we design an effective local search based on interchange solution evaluation method and reasonably fusing it with PSO to propose a disperse PSO. We use it to simulate many experiments. The simulation results and comparisons show that the efficacy of the proposed algorithm is great.Second of all, for the flow-shop scheduling problems with different degree of reentrancy, we extend the above solution evaluation method to this problem according to it structural properties, and then construct the front end omitted speed-up local search by using sub-neighborhood decomposition strategy, we combine DE with the local search to present an efficient hybrid DE. Simulation results and comparisons demonstrate the efficiency and robustness of the presented algorithm.Among the process industry, no-wait reentrant flow-shop scheduling problem is still hard to study, there is no scheduling algorithm to the problem based on differential evolution optimization, this paper discussed how to fuse the local search to optimize discrete differential evolution and discrete particle swarm, so the research has important academic and engineering, and has a certain innovation.