Abstract
With the emergence of the concept of green manufacturing, more manufacturers have attached importance to energy consumption indicators. The process planning and shop scheduling procedures involved in manufacturing processes can both independently achieve energy savings, however independent optimization approaches limit the optimization space. In order to achieve a better optimization effect, the optimization of energy savings for integrated process planning and scheduling (IPPS) was studied in this paper. A mathematical model for multi-objective optimization of IPPS was established to minimize the total energy consumption, makespan, and peak power of the job shop. A hierarchical multi-strategy genetic algorithm based on non-dominated sorting (NSHMSGA) was proposed to solve the problem. This algorithm was based on the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) framework, in which an improved hierarchical coding method is used, containing a variety of genetic operators with different strategies, and in which a population degradation mechanism based on crowding distance is adopted. The results from the case study in this paper showed that the proposed method reduced the energy consumption by approximately 15% for two different scheduling schemes with the same makespan. The computational results for NSHMSGA and NSGA-Ⅱ approaches were evaluated quantitatively in the case study. The C-metric values for NSHMSGA and NSGA-Ⅱ were 0.78 and 0, the spacing metric values were 0.4724 and 0.5775, and the maximum spread values were 1.6404 and 1.3351, respectively. The evaluation indexes showed that the NSHMSGA approach could obtain a better non-dominated solution set than the NSGA-Ⅱ approach in order to solve the multi-objective IPPS problem proposed in this paper.
Highlights
As an important pillar of human life and economic development around the world, the energy consumption of the manufacturing industry accounts for a significant proportion of the total global energy consumption
When one of the three objective functions in case 1 was optimal, the other two objective functions were as follows: (1) when the maximum completion time was optimal at Cmax = 310, the corresponding total energy consumption ETotal = 10,425.9 and the peak input power PTotal = 45.2; (2) when the total energy consumption was optimal at ETotal = 9784.5, the corresponding values of the other two objectives were Cmax = 483 and PTotal = 28.7; (3) when the peak input power was optimal at PTotal = 19.7, the corresponding values of other two objectives were Cmax = 655 and ETotal = 10,491
The result of the case study in this paper showed that the proposed method reduced the energy consumption by approximately 15% in two different scheduling schemes with the same makespan
Summary
As an important pillar of human life and economic development around the world, the energy consumption of the manufacturing industry accounts for a significant proportion of the total global energy consumption. It can be seen that the optimization of energy consumption in the manufacturing industry is crucial to global energy conservation and to the reduction of emissions [2]. Job shop scheduling arranges the production operation sequence based on the corresponding processing machine according to the process planning routes and manufacturing resource limits in order to optimize certain production indicators [5]. As two important subsystems in the manufacturing system [6], process planning and job shop scheduling are key links that affect a series of production indicators, such as the production efficiency, production costs, and energy utilization efficiency. Parameters such as the completion times for production tasks, production costs, and the robustness of the production system have received more attention
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