Energy-efficient Approach to Minimizing the Energy Consumption in An Extended Job-shop Scheduling Problem

• Published in: Chinese journal of mechanical engineering 2015-09, Vol.28 (5), p.1048-1055
• Main Authors: Tang, Dunbing, Dai, Min
• Format: Article
• Language: English
• Subjects: Algorithms; Computer simulation; Cutting speed; Electrical Machines and Networks; Electronics and Microelectronics; Energy conservation; Energy consumption; Energy management; Engineering; Engineering Thermodynamics; Heat and Mass Transfer; Instrumentation; Integer programming; Job shop scheduling; Job shops; Machine tools; Machines; Manufacturing; Mechanical Engineering; Mixed integer; Optimization; Power Electronics; Processes; Production planning; Production scheduling; Schedules; Simulated annealing; Theoretical and Applied Mechanics; 优化目标; 平均能量; 混合整数规划; 生产计划; 能量消耗; 车间作业调度问题; 遗传模拟退火算法; 高效节能
• ISSN: 1000-9345; 2192-8258
• DOI: 10.3901/CJME.2015.0617.082

The traditional production planning and scheduling problems consider performance indicators like time, cost and quality as optimization objectives in manufacturing processes. However, environmentally-friendly factors like energy consumption of production have not been completely taken into consideration. Against this background, this paper addresses an approach to modify a given schedule generated by a production plarming and scheduling system in a job shop floor, where machine tools can work at different cutting speeds. It can adjust the cutting speeds of the operations while keeping the original assignment and processing sequence of operations of each job fixed in order to obtain energy savings. First, the proposed approach, based on a mixed integer programming mathematical model, changes the total idle time of the given schedule to minimize energy consumption in the job shop floor while accepting the optimal solution of the scheduling objective, makespan. Then, a genetic-simulated annealing algorithm is used to explore the optimal solution due to the fact that the problem is strongly NP-hard. Finally, the effectiveness of the approach is performed small- and large-size instances, respectively. The experimental results show that the approach can save 5%-10% of the average energy consumption while accepting the optimal solution of the makespan in small-size instances. In addition, the average maximum energy saving ratio can reach to 13%. And it can save approximately 1%-4% of the average energy consumption and approximately 2.4% of the average maximum energy while accepting the near-optimal solution of the makespan in large-size instances. The proposed research provides an interesting point to explore an energy-aware schedule optimization for a traditional production planning and scheduling problem.