Multi-objective hybrid job-shop scheduling with multiprocessor task (HJSMT) problem with cooperative effect

In order to solve the problem of discrete manufacturing customization and personalized production scheduling, considering the influence of manual labor on processing time, we propose a multi-objective Hybrid Job-shop Scheduling with Multiprocessor Task(HJSMT) problem with cooperative effect model. Based on the actual production, two optimization objectives are set, i. e. minimizing the maximum completion time and the total tardiness. Firstly, considering the situation where workers' cooperation reduces job processing time, the cooperative effect of workers co-processing is considered by referring to the learning effect curve in the model. Subsequently, we develop an Improved Non-dominated Sorting Genetic Algorithm-II (INSGA-II) to solve the multi-objective HJSMT problem by improving Precedence Operation Crossover (POX) and Multiple Mutations (MM) operations. Finally, the scheduling results and the C values are compared with other algorithms to verify the effectiveness of the algorithm. Simultaneously, the multi-objective HJSMT problem with the cooperative effect is solved by the INSGA-II algorithm, and the experimental results also demonstrate the superior performance of the algorithm.