The combination processing of molds, electronic product joint inspection introduces the mandatory concurrent operations on the same machine break the constraint that the same machine cannot process different jobs at the same time in the job shop scheduling (JSP) . To solve this type of job shop scheduling problem, this study proposed an adaptive hybrid initialization genetic algorithm (AHIGA) . Firstly, this study defined the problem as a generalized JSP considering mandatory concurrent operations on the same machine (GJSPMCO) problem, and established an optimization model using mixed integer programming with the objective of minimizing the maximum completion time. Next, this study designed corresponding encoding and decoding techniques to support the representation and resolution of feasible scheduling plans within the constraints of mandatory concurrent operations on the same machine (MCOSM) . Meanwhile, the study established a population hybrid initialization method to generate high-quality feasible solutions. This study also designed a novel crossover and mutation operation methods to ensure the feasibility of newly generated solutions under the constraint of MCOSM. Additionally, this study constructed an adaptive crossover and mutation operators to enable the adaptive updating of offspring, thereby enhancing the algorithm's global search capability. Finally, this paper constructed 40 test cases based on JSP benchmark, and used these test cases and an example of joint inspection of electronic products for experiments. The results showed that the proposed model and algorithm can effectively solve GJSPMCO problem, the improvement strategies can enhance the quality of solutions, demonstrating the feasibility of the model and the superiority of AHIGA.

[2024-03-08] Accepted Paper /doc/2023.12.0609/v1