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A biased random-key genetic algorithm for the unequal area facility layout problem

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  • Gonçalves, José Fernando
  • Resende, Mauricio G.C.

Abstract

This paper presents a biased random-key genetic algorithm (BRKGA) for the unequal area facility layout problem (UA-FLP) where a set of rectangular facilities with given area requirements has to be placed, without overlapping, on a rectangular floor space. The objective is to find the location and the dimensions of the facilities such that the sum of the weighted distances between the centroids of the facilities is minimized. A hybrid approach combining a BRKGA, to determine the order of placement and the dimensions of each facility, a novel placement strategy, to position each facility, and a linear programming model, to fine-tune the solutions, is developed. The proposed approach is tested on 100 random datasets and 28 of benchmark datasets taken from the literature and compared with 21 other benchmark approaches. The quality of the approach was validated by the improvement of the best known solutions for 19 of the 28 extensively studied benchmark datasets.

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  • Gonçalves, José Fernando & Resende, Mauricio G.C., 2015. "A biased random-key genetic algorithm for the unequal area facility layout problem," European Journal of Operational Research, Elsevier, vol. 246(1), pages 86-107.
    • Handle: RePEc:eee:ejores:v:246:y:2015:i:1:p:86-107
    DOI: 10.1016/j.ejor.2015.04.029
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    5. Zhao, Zhiwei & Yang, Jingming & Hu, Ziyu & Che, Haijun, 2016. "A differential evolution algorithm with self-adaptive strategy and control parameters based on symmetric Latin hypercube design for unconstrained optimization problems," European Journal of Operational Research, Elsevier, vol. 250(1), pages 30-45.
    6. Xie, Yue & Zhou, Shenghan & Xiao, Yiyong & Kulturel-Konak, Sadan & Konak, Abdullah, 2018. "A β-accurate linearization method of Euclidean distance for the facility layout problem with heterogeneous distance metrics," European Journal of Operational Research, Elsevier, vol. 265(1), pages 26-38.
    7. Jerzy Grobelny & Rafal Michalski, 2017. "A novel version of simulated annealing based on linguistic patterns for solving facility layout problems," WORking papers in Management Science (WORMS) WORMS/17/07, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
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    10. Paes, Frederico Galaxe & Pessoa, Artur Alves & Vidal, Thibaut, 2017. "A hybrid genetic algorithm with decomposition phases for the Unequal Area Facility Layout Problem," European Journal of Operational Research, Elsevier, vol. 256(3), pages 742-756.
    11. Ghorashi Khalilabadi, S. M. & Roy, D. & de Koster, M.B.M., 2022. "A Data-driven Approach to Enhance Worker Productivity by Optimizing Facility Layout," ERIM Report Series Research in Management ERS-2022-003-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
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