Applying the coral reefs optimization algorithm for solving unequal area facility layout problems

被引:33
|
作者
Garcia-Hernandez, L. [1 ]
Salas-Morera, L. [1 ]
Garcia-Hernandez, J. A. [1 ]
Salcedo-Sanz, S. [2 ]
Valente de Oliveira, J. [3 ]
机构
[1] Univ Cordoba, Area Project Engn, Cordoba, Spain
[2] Univ Alcala, Dept Signal Proc & Commun, Madrid 28805, Spain
[3] Univ Algarve, Faro, Portugal
关键词
Unequal area facility layout problem; Coral reefs optimization; Facility layout; Meta-heuristic; Bio-inspired algorithms; BAY STRUCTURE REPRESENTATION; GENETIC ALGORITHM; FEATURE-SELECTION; SUBSTRATE LAYERS; DESIGN-PROBLEMS; BLOCK LAYOUT; SLICING TREE; TABU SEARCH; MODEL; SIMULATION;
D O I
10.1016/j.eswa.2019.07.036
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Coral Reefs Optimization (CRO) is a recently proposed evolutionary-type algorithm which has shown promising results to tackle many complex optimization problems. This paper discusses the performance of this meta-heuristic in Unequal Area Facility Layout Problems (UA-FLPs). The UA-FLP is an important problem in industrial production, which considers a rectangular region and a set of rectangular facilities. These facilities must be allocated in the plant in the most adequate way satisfying certain constraints. The Flexible Bay Structure has been selected in order to represent solutions for the UA-FLP in the proposed CRO algorithm. In this paper, we detail the implementation of the algorithm and provide the results of different tests in several UA-FLP instances with different size and setting. The obtained results confirm the excellent performance of the proposed algorithm in solving UA-FLPs, improving alternative algorithms devoted to this problem in the literature. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] A Novel Coral Reefs Optimization Algorithm for Multi-objective Problems
    Salcedo-Sanz, S.
    Pastor-Sanchez, A.
    Gallo-Marazuela, D.
    Portilla-Figueras, A.
    [J]. INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2013, 2013, 8206 : 326 - 333
  • [42] Assessing hypermutation operators of a clonal selection algorithm for the unequal area facility layout problem
    Ulutas, Berna Haktanirlar
    Kulturel-Konak, Sadan
    [J]. ENGINEERING OPTIMIZATION, 2013, 45 (03) : 375 - 395
  • [43] UNEQUAL-AREA FACILITY LAYOUT BY GENETIC SEARCH
    TATE, DM
    SMITH, AE
    [J]. IIE TRANSACTIONS, 1995, 27 (04) : 465 - 472
  • [44] Handling qualitative aspects in Unequal Area Facility Layout Problem: An Interactive Genetic Algorithm
    Garcia-Hernandez, L.
    Pierreval, H.
    Salas-Morera, L.
    Arauzo-Azofra, A.
    [J]. APPLIED SOFT COMPUTING, 2013, 13 (04) : 1718 - 1727
  • [45] Learning-based simulated annealing algorithm for unequal area facility layout problem
    Juan Lin
    Ailing Shen
    Liangcheng Wu
    Yiwen Zhong
    [J]. Soft Computing, 2024, 28 : 5667 - 5682
  • [46] A biased random-key genetic algorithm for the unequal area facility layout problem
    Goncalves, Jose Fernando
    Resende, Mauricio G. C.
    [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2015, 246 (01) : 86 - 107
  • [47] An artificial immune system based algorithm to solve unequal area facility layout problem
    Ulutas, Berna Haktanirlar
    Kulturel-Konak, Sadan
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (05) : 5384 - 5395
  • [48] Learning-based simulated annealing algorithm for unequal area facility layout problem
    Lin, Juan
    Shen, Ailing
    Wu, Liangcheng
    Zhong, Yiwen
    [J]. SOFT COMPUTING, 2024, 28 (06) : 5667 - 5682
  • [49] A robust optimization approach for unequal-area dynamic facility layout with demand uncertainty
    Xiao, Xi
    Hu, Yaoguang
    Wang, Weidong
    Ren, Weibo
    [J]. 52ND CIRP CONFERENCE ON MANUFACTURING SYSTEMS (CMS), 2019, 81 : 594 - 599
  • [50] A heuristic algorithm combining Pareto optimization and niche technology for multi-objective unequal area facility layout problem
    Liu, Jingfa
    Liu, Jun
    Yan, Xueming
    Peng, Bitao
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2020, 89