Energy-efficient Optimization of Flexible Job Shop Scheduling and Preventive Maintenance

In recent years, there has been growing concern on energy efficiency in the manufacturing enterprises. Since scheduling problem has a direct impact on energy consumption, developing the effective production scheduling is among the priorities in industries. Moreover, in practice, production and maintenance operations have been viewed as major source of energy consumption in industrial system. In this paper, we propose a stochastic mathematical model for a joint production and maintenance operations scheduling problem in a flexible job shop industrial environment in which both traditional and energy efficient aspects are modeled. The objective of this research is to minimize the expected makespan in the scheduling problem focusing on CO2 emissions reduction in an actual workshop which breakdowns can happen at any moment and make machines unavailable for processing operations. In fact, energy usage associated with the CO2 emissions of the industrial shop floor are formulated in the constraints with respect to different states of operation and idle. To address this problem effectively, the Genetic Algorithm (GA) is applied for the proposed stochastic model to minimize the expected makespan. From an operation management viewpoint, the proposed model provides a scientific and helpful guideline for manufacturing system to plan production and maintenance simultaneously, with both economic and environmental benefits.