【摘要】 经典的粒子群优化算法是一个有效的寻找连续函数极值的方法。其在离散空间的应用还很不成熟。主要针对公共交货期下E/T(Earliness/Tardiness)指标的单机调度问题进行研究,并基于粒子群与启发式集成的优化算法(Particle Swarm Optimization integrated with Heuristic:PSO-H)对该问题进行求解。启发式信息由工件加工时间和拖期惩罚构成,它对算法的寻优性能有明显的改善。同时,采用OR-Library中的标准算例对该算法进行仿真实验,显示出理想的寻优结果。更多还原

【Abstract】 The classical Particle Swarm Optimization (PSO) is a powerful method to find the minimum for a function optimization problem, especially with a continuous solution space. So far, it is seldom used to solve those problems with discrete featuresThe problem of scheduling jobs on a single machine against common due dates with respect to earliness and tardiness (E/T) penalties was dealt. At the same time, a PSO strategy integrated with a kind of heuristic algorithm was proposed, where the heuristic information is composed of the processing time and tardiness penalty for each job. It is indicated that such strategy can significantly improve the performance of the solutions. Benchmark testing from OR-Library demonstrates that the algorithm is both effective and efficient in achieving satisfied solutions for scheduling problems with earliness and tardiness penalties.更多还原