Energy efficient modeling and optimization for assembly sequence planning using moth flame optimization

Abdullah, Arif and M. F. F., Ab Rashid and Ponnambalam, S. G. and Zakri, Ghazalli (2019) Energy efficient modeling and optimization for assembly sequence planning using moth flame optimization. Assembly Automation, 39 (2). pp. 356-368. ISSN 0144-5154. (Published)

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Purpose Environmental problems in manufacturing industries are a global issue owing to severe lack fossil resources. In assembly sequence planning (ASP), the research effort mainly aims to improve profit and human-related factors, but it still lacks in the consideration of the environmental issue. This paper aims to present an energy-efficient model for the ASP problem. Design/methodology/approach The proposed model considered energy utilization during the assembly process, particularly idle energy utilization. The problem was then optimized using moth flame optimization (MFO) and compared with well-established algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). A computational test was conducted using five assembly problems ranging from 12 to 40 components. Findings The results of the computational experiments indicated that the proposed model was capable of generating an energy-efficient assembly sequence. At the same time, the results also showed that MFO consistently performed better in terms of the best and mean fitness, with acceptable computational time. Originality/value This paper proposed a new energy-efficient ASP model that can be a guideline to design assembly station. Furthermore, this is the first attempt to implement MFO for the ASP problem.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Ant colony optimization; Artificial intelligence; Assembly; Energy utilization; Genetic algorithms
Subjects: T Technology > TS Manufactures
Faculty/Division: Faculty of Mechanical & Manufacturing Engineering
Depositing User: Dr. Mohd Fadzil Faisae Ab. Rashid
Date Deposited: 21 Nov 2019 03:18
Last Modified: 21 Nov 2019 03:18
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