Scheduling for hybrid flow shop with energy-efficiency and machine preventive maintenance in sheet metal manufacturing system

Abstract

Hybrid flow shop scheduling (HFSS) is widely used in actual workshop production processes and is an important means of reducing delivery time, increasing cost savings, and improving production efficiency and quality. In this study, a HFSS with machining-speed-based energy efficiency and machine preventive maintenance (HFSE-PM) was investigated in the context of sheet metal processing, filling the gap in existing related researches. Based on the characteristics of HFSE-PM, the concepts of virtual machines, conventional machine maintenance, and effective machine maintenance were applied, and a linear programming model was established to minimize the makespan and total energy consumption of the machines. An improved teaching- and learning-based optimization (I-TLBO) algorithm framework was designed, in which a two-stage encoding operator, a decoding operator based on scenario evaluation, five types of neighborhood search operators in three phases, and a phased large-scale mutation strategy were also designed to generate initial solutions, avoid poor quality solutions, perform local optimization, and perform global optimization, respectively. Computational experiments demonstrated the effectiveness of the proposed model and the superiority of the proposed neighborhood search operators. In a comparison with three other excellent algorithms for solving similar problems, the superiority of I-TLBO in providing HFSE-PM was demonstrated. The model and research method constructed are not only applicable to production scheduling problems in the sheet metal processing industry but also to all practical production scheduling applications that can be modeled as HFSE-PM, HFSE, HFSS-PM, and HFSS.