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With the rising concern of environmental pollution caused by fossil fuels, the energy efficiency becomes a key issue for the energy sensitive company in order to save the electricity cost and take responsibility for the sustainable development. Thus, an effective energy-aware operations management for the manufacturing plant is crucial to improve its competitiveness in the global market nowadays. We propose a mathematical model integrating three interrelated operational aspects including the production, maintenance and energy for the flow shops under Time-of-Use electricity tariff. A two-layer math-heuristic is devised to solve the model efficiently based on the decomposition of decision variables. In the outer layer, the jobs’ sequences and the buffer times are optimized using the metaheuristic based on genetic algorithm. In the inner layer, the maintenances’ positions and machines’ on/off are optimized using the exact method based on dynamic programming algorithm. Compared with CPLEX and traditional GA, the math-heuristic can get the near optimal solution with a small gap for the small-sized problems and perform well for the large-sized problems. The tradeoff between energy cost and makespan shows that more profit can be achieved using our model for the instance with a later production deadline. Finally, numerical experiments are also conducted to analyze the structure of the optimal solution in order to provide the managerial insights.
Weiwei Cui; Biao Lu. Energy-aware operations management for flow shops under TOU electricity tariff. Computers & Industrial Engineering 2020, 151, 106942 .
AMA StyleWeiwei Cui, Biao Lu. Energy-aware operations management for flow shops under TOU electricity tariff. Computers & Industrial Engineering. 2020; 151 ():106942.
Chicago/Turabian StyleWeiwei Cui; Biao Lu. 2020. "Energy-aware operations management for flow shops under TOU electricity tariff." Computers & Industrial Engineering 151, no. : 106942.
With the growing concern of energy shortage and environment pollution, the energy aware operation management problem has emerged as a hot topic in industrial engineering recently. An integrated model consisting of production scheduling, preventive maintenance (PM) planning, and energy controlling is established for the flow shops with the PM constraint and peak demand constraint. The machine’s on/off and the speed level selection are considered to save the energy consumption in this problem. To minimize the makespan and the total energy consumption simultaneously, a multi-objective algorithm founded on NSGA-II is designed to solve the model effectively. The key decision variables are coded into the chromosome, while the others are obtained heuristically using the proposed decoding method when evaluating the chromosome. Numerical experiments were conducted to validate the effectiveness and efficiency by comparing the proposed algorithm and the traditional rules in manufacturing plant. The impacts of constraints on the Pareto frontier are also shown when analyzing the tradeoff between two objectives, which can be used to explicitly assess the energy consumption.
Weiwei Cui; Biao Lu. A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint. Sustainability 2020, 12, 4110 .
AMA StyleWeiwei Cui, Biao Lu. A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint. Sustainability. 2020; 12 (10):4110.
Chicago/Turabian StyleWeiwei Cui; Biao Lu. 2020. "A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint." Sustainability 12, no. 10: 4110.