Optimization Of Production Scheduling Using Self-Crossover Genetic Algorithm

Abstract

Production scheduling is not only a necessary part of manufacturing enterprises to ensure normal production work, but also affects the operating costs of enterprises. At present, production scheduling of many manufacturing enterprises only aim at ensuring normal production work, without taking into account the impact of production scheduling on enterprise costs. In order to improve the economic efficiency of the enterprise, this paper research on optimization of the production scheduling. A new optimization algorithm called the Self-Crossover Genetic Algorithm is proposed to support model optimization. A numerical study using actual factory data is implemented in this paper. The result shows that scientific production scheduling can reduce costs indeed without affecting the normal operation of the enterprise. In order to increase the fitness of the optimization, the numerical study adds four sensitivity analyses, which analyzed the optimization effect with different parameters, such as night shift allowance, order required production, self-crossover rate and the shift time. In summary, Self-Crossover Genetic Algorithm can provide a certain degree of reference for enterprises to develop a suitable production schedule.