基于改进型NSGAII的织造车间多目标大规模动态调度

织造车间调度规模普遍在300台织机、1000个织轴以上,遗传算法搜索极易陷入局部最优,针对传统动态调度机制在织造插单、打样等复杂生产场景中适应性不强的问题,提出一种改进NSGAII算法.从织造多织机、多织轴、多产品的大规模调度出发,基于织造和穿经之间独特的逆工序调度关系,构建以逾期损失、最大完工时间和织机空闲时间均最小...

Multi-objective large-scale dynamic scheduling for weaving workshops based on improved NSGAII

As the number of looms exceeds 300 with more than 1 000 weaver's beams in the weaving workshop, the genetic algorithm is easy to fall into local optimal solution when solving such large-scale scheduling problems, and the traditional dynamic scheduling mechanism is not adaptable enough for complex production scenarios such as order insertion and proofing. An improved NSGAII algorithm was proposed in the paper. Considering the facts that the scheduling of a large-scale weaving workshop involves large numbers of looms, weaver's beams and products, and the unique inverse process scheduling relationship between weaving and drawing-in, a multi-objective large-scale scheduling model for weaving was constructed, aiming at the minimization of overdue loss, makespan, and idle time of loom. The encoding of heuristic rules was improved to reduce the solution space, and a greedy evolution operator was used in local and global correlation optimization to avoid falling into local optimization. A dynamic scheduling mechanism based on dominance relationship evaluation was adopted to improve the poor dynamic response mechanism and low ability against disturbance during production. Experiments show that the scheduling ability of the algorithm remains superior over other algorithms in a situation where there are 500 looms with 4 000 weaver's beams in a weaving workshop.