Enhancement of Job Shop Planning with Sequential Dependent Setups Using a Hybrid Method

Abstract

Job-shop scheduling is conventionally followed in the real world; therefore, this study focuses on ceramic substrate assembly lines concerning machine idle problem. Genetic algorithm (GA) is an efficient heuristic widely used in production scheduling environment. This study integrates a new type of crossover rule and fitness function into the traditional GA with a due slot and sequence-dependent setup for constraints in the job-shop scheduling system with minimal stocks and machine idle for cost-cutting. Accordingly, simulated data verify the effectiveness and robustness of the suggested method. The results indicate that the suggested approach can potentially replace GA for solving such issues.