【摘要】 统筹考虑晶圆加工过程中的多品种、p-s-d(past-sequence-dependent)换模时间及衰退效应等约束特征,以总加权提前/拖期惩罚成本最小为优化目标,建立了单机MOJ(multiple orders per job)调度数学规划模型.在此基础上,对决策变量进行分离,提出具有双层嵌套编码机制的改进型遗传蚁群调度算法.该算法将遗传算法融合到动态自适应蚁群算法的每一次迭代过程中,并为有效提高算法的收敛性能,引入ATCS(apparent tardiness cost with setups)修正准则.最后,仿真实验结果表明,该算法是有效、可行的.更多还原

【Abstract】 Taking a comprehensive consideration of the characteristics of multiple product types,the past-sequence-dependent( p-s-d) setup time and the deterioration effects constraints in processes of wafer fabrication,with an objective function of minimizing total weighted earlinesstardiness penalties cost,a mathematical programming model of scheduling multiple orders per job( MOJ) in a single machine was built. On this basis,the decision-making variables were separated,and a modified genetic algorithm-ant colony optimization( M GA-ACO) algorithm adopting two-level encoding mechanism was put forw ard. Genetic algorithm was converged to the process of dynamic and adaptive ant colony iterations. To improve the algorithm convergence performance,a modified rule of apparent tardiness cost with setups( ATCS) was applied.Finally,the simulation results indicated that the developed algorithm is valid and feasible.更多还原