On Energy Consumption and Productivity in a Mixed-Model Assembly Line Sequencing Problem

Iwona Paprocka () and Damian Krenczyk
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Iwona Paprocka: Department of Engineering Processes Automation and Integrated Manufacturing Systems, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, Poland
Damian Krenczyk: Department of Engineering Processes Automation and Integrated Manufacturing Systems, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, Poland

Energies, 2023, vol. 16, issue 20, 1-19

Abstract: Mixed and multi-model assembly line sequencing problems are more practical than single-product models. The methods and selection criteria used must keep up with the constantly increasing level of variability, synchronize flows between various—often very energy-intensive production departments—and cope with high dynamics resulting from interrupted supply chains. The requirements for conscious use of Earth’s limited natural resources and the need to limit energy consumption and interference in the environment force the inclusion of additional evaluation criteria focusing on the environmental aspect in optimization models. Effective sustainable solutions take into account productivity, timeliness, flow synchronization, and the reduction of energy consumption. In the paper, the problem of determining the sequence of vehicles for a selected class of multi-version assembly lines, in which the order restrictions were determined taking into account the above criteria, is presented. Original value of the paper is the development of the Grey Wolf Optimizer (GWO) for the mixed-model assembly lines sequencing problem. In the paper, a comparative analysis of the greedy heuristics, Simulated Annealing and GWO for a real case study of a mixed vehicle assembly line is presented. The GWO outperforms other algorithms. Overall research performance of the GWO on the sequencing problem is effective.

Keywords: energy-efficient sequencing; grey wolf optimizer; mixed-model assembly line; car sequencing problem (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: 2023
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