双资源约束的鲁棒Job Shop调度问题研究

Research on the Dual-resource Constrained Robust Job Shop Scheduling Problems

针对机器-工人双资源约束下加工时间具有随机性的Job shop调度问题(Job shop scheduling problems,JSSP),考虑工人熟练程度差异和工人数量不足的约束,采用鲁棒调度的方法建立机器-工人双资源约束的鲁棒Job shop调度模型(Dual-resource constrained robust JSSP,DR-RJSSP)。鉴于DR-RJSSP同时考虑工人合理指派和双目标优化,提出机器-工人两阶段指派方法,在主动降低加工时间随机扰动的同时最小化工人约束对调度性能的影响。其次,提出多目标混合分布估计算法求解DR-RJSSP,以得到兼顾调度性能和鲁棒性的Pareto解集。最后,采用8组仿真算例将所提出的兼顾工人熟练程度和负载均衡的指派策略与基于熟练程度的指派策略和随机指派策略进行对比,验证了所提指派策略的Pareto优化性能。此外,通过对制造企业调度案例的仿真分析,验证了基于两阶段指派策略的MO-HEDA求解DR-RJSSP的有效性。

The Job shop scheduling problems with stochastic processing times under machine-worker dual resource constraints is studied.Considering the difference in worker proficiency and the insufficient number of workers,a robust scheduling approach is adopted,and then the machine-worker dual-resource constrained robust Job shop scheduling problems model(DR-RJSSP)is formulated.Because of the requirements of DR-RJSSP for workers’rational assignment and bi-objective optimization,a heuristic based on a two-stage assignment strategy is proposed,which can minimize the random disturbances of the processing times as well as its impact on scheduling efficiency.Thereafter,a multi-objective hybrid estimation of distribution algorithm is employed to solve the DR-RJSSP.Thereby,the solution set considers both the performance and the robustness of the schedule can be obtained.At last,the proposed two-stage assignment strategy(TSAS)is compared with the proficiency-based assignment strategy as well as the machine-worker randomly assigned strategy through simulation experiments.Finally,eight standard instances and a problem instance obtained from a manufacturing company are employed.According to the simulation results,the Pareto optimization performance of the proposed TSAS and its effectiveness to solve the Job shop scheduling problem of the actual manufacturing system are verified.