Abstract:

Job-shop scheduling is essential to advanced manufacturing and modern management. In light of the difficulty of obtaining the optimal solution using simple genetic algorithms in the process of solving multi-objective job-shop scheduling problems, with maximum customer satisfaction and minimum makespan in mind, we constructed a multi-objective job-shop scheduling model with factory capacity constraints and propose an improved NSGA-II algorithm. This algorithm not only uses an improved elitism strategy to dynamically update the elite solution set, but also enhances the Pareto sorting algorithm to make density computations more accurate, thereby ensuring population diversity. An example is given to verify that this algorithm can effectively enhance global search capabilities, save computing resources, and lead to a better optimal solution. Using this algorithm for job-shop scheduling optimization oriented towards multi-objective decision-making can provide corporate executives with a scientific quantitative basis for management and decision-making, thereby enhancing their companies’ competitiveness.