Genetic Algorithm Based Method for Optimizing Machine Arrangement in Manufacturing Cells

A manufacturing cell that maximizes forward flows and minimizes backtracking flows is expected to achieve a high-performance rating across various measures. Therefore, the primary focus here is on increasing the forward flow distance while minimizing backtracking. In this paper, a genetic algorithmbased method is introduced to determine the machine sequence within each manufacturing cells, aiming to maximize Average Cell Movement Efficiency (ACME). The algorithm was tested, and the results demonstrate superior performance The comparison was made using Cell Movement Efficiency (CME) and ACME, with the proposed algorithm outperforming other methods for the intra-cell arrangement of machines

Publication Details

Journal
Computer Science Engineering and Technology
Published
2025-08-21
DOI
https://doi.org/10.46632/cset/3/3/6
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
Field-Weighted Citation Impact
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article

Genetic Algorithm Based Method for Optimizing Machine Arrangement in Manufacturing Cells

Computer Science Engineering and Technology
Advanced Manufacturing and Logistics Optimization
article

Genetic Algorithm Based Method for Optimizing Machine Arrangement in Manufacturing Cells

article en

Abstract

A manufacturing cell that maximizes forward flows and minimizes backtracking flows is expected to achieve a high-performance rating across various measures. Therefore, the primary focus here is on increasing the forward flow distance while minimizing backtracking. In this paper, a genetic algorithmbased method is introduced to determine the machine sequence within each manufacturing cells, aiming to maximize Average Cell Movement Efficiency (ACME). The algorithm was tested, and the results demonstrate superior performance The comparison was made using Cell Movement Efficiency (CME) and ACME, with the proposed algorithm outperforming other methods for the intra-cell arrangement of machines

Computer Science Engineering and TechnologyVol. 3(3 September 2025)
Openalex Percentile: Top 70%
Advanced Manufacturing and Logistics Optimization
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Genetic Algorithm Based Method for Optimizing Machine Arrangement in Manufacturing Cells · Computer Science Engineering and Technology (2025) | TGRS Research Map | TGRS