有模具约束的并行机批量流调度问题研究

A Research on Lot Streaming in Parallel Machines Scheduling with Mold Constraint

大多数调度问题均假设产品以单个或整批的方式进行生产,而实际生产过程中,会把产品分批后再进行生产。但当考虑模具约束时,对如何解决产品分批以及制定合理调度方案的问题,本文以最小化最大完工时间为优化目标,建立了考虑模具约束的并行机批量流调度模型,并提出了一种基于遗传算法和差分算法结合的混合差分遗传算法(DEGA),实现分批与调度两个问题并行优化。最后通过对算例测试,DEGA算法得到更优的解,证明了该算法的优越性和稳定性。结合实际案例,验证了模型和算法的可行性。

Most scheduling problems assume that the job is produced in a single or batch mode, while in the real manufacturing environment, the job can be split. In order to solve the problem of splitting the job and making a reasonable scheduling scheme when considering the mold constraint, an optimized objective model of lot streaming in parallel machine scheduling with mold constraint is built for minimizing the makespan. Meanwhile, a mixed differential evolution genetic algorithm（DEGA）, based on combination of genetic algorithm and differential evolution, is proposed to solve this model. It can implement parallel optimization of job splitting and scheduling problems. Numerical experiments and a realistic case are performed by DEGA, and the results indicate that the model and mixed algorithm are efficient and feasible.