Energy-efficient production scheduling through machine on/off control during preventive maintenance

• DOI: 10.1016/j.engappai.2021.104359
• Publication Date: 2021-07-05T16:01:11Z
• Authors: Guiliang Gong; Raymond Chiong; Qianwang Deng; Wenwu Han; Like Zhang; Dan Huang
• Keywords: 670; machine maintenance; energy-efficient scheduling; machine on/off control; Sustainable Development Goals; 0202 electrical engineering, electronic engineering, information engineering; multi-objective optimisation; heuristics; 02 engineering and technology; SDG 7; 7. Clean energy

Abstract This paper studies an important extension of energy-efficient production scheduling research, where machine on/off control and machine maintenance are considered simultaneously. The inspiration of this extension is that a machine must be turned off if it needs to be maintained, and an already-turned-off machine can be maintained without needing to be restarted. We therefore formulate an energy-efficient production scheduling problem with machine maintenance through machine on/off control, aiming to optimise three objectives – the makespan, total number of machine restarts, and energy consumption – at the same time. Four rules are designed to set the machine on/off criteria, maintenance periods and predefined maintenance windows, based on solutions of the job shop scheduling problem (JSP) as a test case. Three heuristics are proposed to insert the maintenance activities into the solutions and move their maintenance-operation blocks to optimise the objectives. The effectiveness of the first rule and the moving of maintenance-operation blocks have been proven mathematically. Our proposed heuristics, unlike traditional heuristic algorithms, are expected to be applicable and effective even if we change the objectives and constraints, require minimal computational time (only a few seconds) to optimise a scheduling solution, and can solve different types of scheduling problems without needing any modification. Experiments undertaken indicate promising performance of the proposed heuristics based on 182 JSP benchmark instances.