关键词: 车间作业排序, 仿真, 优化, 贪婪随机自适应搜索算法

Abstract: Considering the inherent complexity of job shop scheduling problems, especially its difficulty in analytical solution, an integration system framework based on optimization and simulation was established. A new modeling solution philosophy was proposed for the problems. By this way, a general-purpose and powerful simulator, Arena, was employed for modeling job shop problems firstly. And a kind of virtual preemption rule was recommended to improve the modeling capability. Then an efficient optimization procedure Greedy Randomized Adaptive Search Procedure (GRASP) was utilized to find the optimal solution. Based on Arena class library and its interface with VB, the interaction between Arena and GRASP could be effectively implemented, and an optimization & simulation integrated framework was thus achieved. With this framework, various stochastic factors could be conveniently introduced into the model, hence the capability for modeling the real-life systems was greatly improved. Finally, an example was analyzed to demonstrate the effectiveness of the method.

Key words: job shop scheduling, simulation, optimization, greedy randomized adaptive search procedure