MACHINE LEARNING-ENHANCED GENETIC ALGORITHM FOR ROBUST LAYOUT DESIGN IN DYNAMIC FACILITY LAYOUT PROBLEMS
Implementation of Dynamic Facility Layout Problems
DOI:
https://doi.org/10.23055/ijietap.2023.30.6.9077Keywords:
Robust Layout, Facility Layout, Quadratic Assignment Problem, Genetic Algorithm, Machine Learning, Dynamic Faciity Layout ProblemAbstract
This paper proposes a new approach for solving dynamic facility layout problems (DFLP) using a genetic algorithm (GA) enhanced with machine learning techniques, namely clustering algorithms. The proposed course aims to design a robust layout that can adapt to changes in the input parameters. Traditionally, the DFLPs are solved using adaptive methods, i.e., the layout from period to period varies. However, in the robust approach, the layout remains the same throughout the different planning periods. The GA is used for generating the solutions, and the machine learning technique is used to cluster the solutions and select the candidate solution to undergo the local search procedure. The proposed approach is tested on a set of benchmark instances and compared with published approaches. The results show that the proposed approach outperforms the existing approaches in terms of solution quality, robustness, and computational efficiency.
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