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有效集成预防性机器和模具维护的生产调度方案能够大大提高生产系统的可靠性和稳定性,提高生产效率,降低生产成本。本文以注塑车间生产为背景,对考虑机器和模具预防性维护的生产调度问题进行研究,并运用结构重组的细菌觅食优化(SRBFO)算法对这一问题进行优化求解。传统的基于时间的、维护时长固定的维护策略并不能够很好的符合实际生产的需要,因此,本文考虑了动态随机的、机器和模具维护时长随机器和模具年龄变化的维护策略,将机器和模具维护同样当作决策变量之一整合进入产品生产调度决策中。接着,我们选择了SRBFO算法为这种典型的NP问题来寻找最优的生产调度方案。在求解过程中,为了实现算法中细菌“位置矢量”到“可行调度方案”的映射,必须对细菌进行编码解码,每个细菌需要携带五个方面的信息才能够完整的通过三种不同的解码方式转化为一个可行的调度方案。最后,将SRBFO算法用来求解5个不同规模的生产调度问题实例,并与PSO算法进行了对比,实验结果表明了无论是在解的质量还是算法稳定性上面,SRBFO算法在处理本文所研究的生产调度问题上的性能要优于PSO算法。同时为了进一步验证本文所提的维护策略的优越性,在实例求解中将传统的基于机器年龄的、维护时长固定的维护策略作为对比策略,实验结果表明本文所提出了动态随机的、预防性维护时长随机器和模具年龄变化的维护策略相比较基于加工时间的、维护时长固定的维护策略而言,更能够缩短最大完工时间。
Effective integration of preventive maintenance scheduling and machine production scheduling program can greatly improve the reliability and stability of the production system to improve production efficiency and reduce production costs. Based on the production of injection molding shop, this paper studies the production scheduling problem which takes preventive maintenance of machine and mold into account, and uses the SRBFO algorithm to solve this problem. The traditional maintenance strategy based on time and fixed maintenance time does not meet the needs of actual production. Therefore, this paper considers the dynamic and random maintenance strategy of machine and mold maintenance time varying with age of mold and machine, And mold maintenance as one of the same decision variables into the product production scheduling decision. Next, we choose the SRBFO algorithm to find the optimal production scheduling solution for this typical NP problem. In the process of solving, in order to realize the mapping of bacteria “position vector ” to “feasible scheduling scheme ” in the algorithm, the bacteria must be encoded and decoded, and each bacterium needs to carry five aspects of information to complete the three Different decoding methods into a viable scheduling solution. Finally, the SRBFO algorithm is used to solve five production scheduling problem instances with different scales, and compared with the PSO algorithm. The experimental results show that the SRBFO algorithm is superior to the PSO algorithm in both the quality of the solution and the stability of the algorithm. The performance on the production scheduling problem is better than the PSO algorithm. At the same time, in order to further verify the superiority of the maintenance strategy proposed in this paper, the traditional maintenance strategy based on the age of the machine and the fixed maintenance time is taken as the contrast strategy in the solution of the example. The experimental results show that the dynamic random and preventive maintenance Time-based maintenance strategies that vary with age of the machine and tooling can shorten the maximum finish time compared to maintenance-based maintenance strategies that are based on machining time.