APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOPSCHEDULING PROBLEM

A new heuristic algorithm is proposed for the problem of finding the minimummakespan in the job-shop scheduling problem. The new algorithm is based on the principles ofparticle swarm optimization (PSO). PSO employs a collaborative population-based search, which isinspired by the social behavior of bird flocking. It combines local search (by self experience) andglobal search (by neighboring experience), possessing high search efficiency. Simulated annealing(SA) employs certain probability to avoid becoming trapped in a local optimum and the search processcan be controlled by the cooling schedule. By reasonably combining these two different searchalgorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, isdeveloped. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated byapplying it to some benchmark job-shop scheduling problems and comparing results with otheralgorithms in literature. Comparing results indicate that PSO-based algorithm is a viable andeffective approach for the job-shop scheduling problem.

工期不确定的模具车间柔性Flow-shop调度问题

由于模具制造属于非重复性单件订货生产,模具加工的任务工期具有较强的不确定性,导致生产调度混乱。为制定合理可行的生产调度方案,建立了任务工期离散概率模型,以最大完工时间的期望值最小为目标,建立不确定工期柔性Flow-shop调度模型;在遗传算法交叉、变异等操作中融入模拟退火操作,将遗传算法的全局搜索能力与模拟退火算法的良好局部搜索能力相结合,设计了不确定工期的柔性Flow-shop调度问题混合遗传模拟退火算法。利用混合遗传模拟退火算法对调度模型进行求解,通过仿真实验表明,该研究对于解决工期不确定的模具车间柔性Flow-shop调度问题是行之有效的。