基于瓶颈工序的多资源多目标机械加工车间调度研究  

Research on multiple resources constrained & multi-objective machining job shop scheduling based on bottleneck operations

作　　者：李鹏[1] 邱顺流 宋豫川[1] 徐修文[1] 

机构地区：[1]重庆大学机械传动国家重点实验室,重庆400044 [2]重庆红江机械有限责任公司,重庆402162

出　　处：《现代制造工程》2013年第1期1-6,共6页Modern Manufacturing Engineering

基　　金：国家自然科学基金资助项目(51075414);国家863资助项目(2007AA040701-02)

摘　　要：为求解多资源约束的机械加工车间调度问题,建立了包括最大完工时间、平均流经时间、总拖期惩罚和生产成本在内的多目标优化模型,并结合免疫遗传算法和约束理论提出了一种基于瓶颈工序的机械加工车间调度算法。算法依据约束理论提出了一种基于工序的多参数级联编码方法和基于鼓-缓冲器-绳索(DBR)的四阶段解码方法,以及有效的交叉、变异操作。基于瓶颈工序的免疫操作及基于浓度的选择更新机制,保证了多目标优化问题的收敛性以及Pareto解集的多样性。仿真结果表明了该算法的可行性和有效性。To solve the multiple resources constrained machining job shop scheduling problem more efficiently, the multi-objective optimization model is built in which the makespan, the mean flow-time, the total tardiness punishment and production cost are considered, a method based on bottleneck operations which combining theory of constraints and immune genetic algorithm is pro- posed. A multi-parameter coding method based on processes and a four-stage decoding method based on DBR are designed based on theory of constraints, two effective crossover and mutation operations are designed too. The convergence of multi-objective opti- mization problem and the variety of Pareto solutions set are guaranteed by the immune operation based on bottleneck operations and the immune select mechanism based on antibody concentration. The effectiveness of the proposed algorithm is validated by the simulation results.

关 键 词：瓶颈工序 多资源约束 多目标调度 约束理论 免疫遗传算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]