Treffer: A dynamic multi-objective evolutionary greedy algorithm for distributed hybrid flow shop rescheduling problem.

• A distributed hybrid flowshop rescheduling problem with new job arrivals and random machine breakdowns is studied. • A dynamic multi-objective evolutionary greedy algorithm is proposed. • Based on problem-specific knowledge, the acceleration mechanism and population evolution mechanism of the algorithm are investigated. • A deep reinforcement learning algorithm is adopted to determine whether dynamic events in production require rescheduling. This paper addresses a distributed hybrid flowshop rescheduling problem (DHFRP) with new job insertion and machine breakdowns, which exists widely in modern industry. The objective is to minimize makespan and total tardiness time. A dynamic multi-objective evolutionary greedy algorithm is used to solve this rescheduling problem. An initialization strategy is designed to generate a high-quality initial population. An adaptive perturbation process and a local search procedure further enhance the quality of the population. For dynamic changes in the processing environment, the valid information in the original non-dominated solution set is utilized in order to efficiently obtain a rescheduling solution. In addition, a deep reinforcement learning algorithm is used to make decisions on the rescheduling strategy to be adopted. This operation maintains the stability of the production process and effectively reduces the transportation cost during processing. Finally, a series of numerical experiments demonstrate the effectiveness of the proposed algorithm in solving DHFRP. This is further supported by a case study based on real industrial production. [ABSTRACT FROM AUTHOR]