Abstract: In recent years,the individualized demand of customers brings small batches and diversification of orders towards enterprises,which brings a new type of the job shop scheduling problem and has a fundamental and practical significance onthe competitiveness of an enterprise.Collaborative schedule of production and logistics resources on a shop floor is very challenging to meet this dynamical scheduling requirement.To solve the problem,a task consistency model of production-logistics resources based on swarm technology was constructed,the slack task model was built according to personalized orders and real-time workshop information characteristics,and then a production logistics real-time scheduling method considering the task consistency between production and logistics was proposed to realize the real-time adaptive collaboration of swarm resources in an intelligent job shop for random order arrival.The proposed algorithm was carried out with several simulation cases to evaluate its effectiveness.Experimental results showed that the proposed algorithm was superior to three existing algorithms.The proposed method had better robustness and could effectively overcome the shortage of “shortsightedness” of real-time scheduling methods.

Key words: production-logistics resources, swarm, consistency, resource coordination, real-time scheduling

摘要： 近年来,客户个性化需求给企业带来了小批量、多样化的随机订单,产生了一类工件到达时间随机的作业车间调度问题,对企业竞争力造成重要影响。这种动态调度需求对如何协同调度车间内的生产和物流资源形成了挑战。针对这一问题,引入群集技术,构建了群集生产—物流资源任务一致性模型,根据个性化订单特征构建了基于实时信息的任务松弛度模型,提出一种考虑生产—物流资源一致性的实时调度方法,实现了智能作业车间面向随机订单到达的群集资源实时自适应协同。结合仿真案例,对所提方法的有效性进行了评估。实验结果表明,所提算法优于3种对比算法,具有更好的鲁棒性,并可以有效克服实时调度方法“短视”的不足。

关键词: 生产—物流资源, 群集, 一致性, 资源协同, 实时调度