An Improved Genetic Algorithm with Local Search for Dynamic Job Shop Scheduling Problem

Ming Wang, Peng Zhang, Peng Zheng, Junjie He, Jie Zhang, Jinsong Bao

Abstract: Dynamic disturbances such as rush job arrivals and process delay are inevitable occurrences in production environment. Dynamic job shop scheduling problem (DJSSP) is known as NP-hard combinatorial optimization problem, this paper introduces an efficient strategy for the problem. Inspired by rolling horizon strategy, the hybrid periodic and event-driven rolling horizon strategy (HRS) is presented to trigger rescheduling in a dynamic environment with process delay and rush job arrivals. Within the framework, an improved genetic algorithm (IGA) with local search is proposed to generate the rescheduling scheme of unprocessed and new jobs. To evaluate the performance of proposed algorithm, various benchmark problems and different dynamic disturbances are considered to carry out detailed experiments. The results indicate that the proposed algorithm produces superior solutions for benchmark problems and solves the DJSSP effectively with different disturbances under dynamic manufacturing environment.