Multi-Objective Optimization of Integrated Process Planning and Scheduling Considering Energy Savings

Xu Zhang, Hua Zhang and Jin Yao
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Xu Zhang: Business School, Sichuan University, Chengdu 610064, China
Hua Zhang: School of Economics and Management, Zhaoqing University, Zhaoqing 526061, China
Jin Yao: School of Mechanical Engineering, Sichuan University, Chengdu 610064, China

Energies, 2020, vol. 13, issue 23, 1-31

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 ? (NSGA-?) 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-? approaches were evaluated quantitatively in the case study. The C-metric values for NSHMSGA and NSGA-? 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-? approach in order to solve the multi-objective IPPS problem proposed in this paper.

Keywords: integrated process planning and scheduling; energy consumption; multi-objective optimization; genetic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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