Soft Computing

, Volume 23, Issue 3, pp 961–986 | Cite as

Bi-objective corridor allocation problem using a permutation-based genetic algorithm hybridized with a local search technique

  • Zahnupriya Kalita
  • Dilip DattaEmail author
  • Gintaras Palubeckis
Methodologies and Application


As a more practical form of the double-row layout problem, the bi-objective corridor allocation problem (bCAP) was introduced, in which a given number of facilities are to be placed on opposite sides of a central corridor so as to minimize both the overall flow cost among the facilities and the length of the corridor. Further, the bCAP seeks the placement of the facilities starting from the same level along the corridor without allowing any gap between two facilities of a row. In the initial proposal, the bCAP was solved as an unconstrained optimization problem using a permutation-based genetic algorithm (pGA). It is observed that the pGA alone is not sufficient to reach to the potential solutions of the complicated bCAP. In this work, incorporating a promising local search technique in the pGA, the hybridized pGA is found outperforming the simple pGA as well as a simulated annealing and tabu search-based approach in a number of instances of sizes 60 and above, in terms of both the best objective values and statistical analysis. Moreover, the hybridized pGA could explore multiple optimal solutions for some of such instances.


Combinatorial optimization Corridor allocation Multi-objective optimization Genetic algorithm 


Compliance with ethical standards

Conflict of interest

All authors listed have agreed to be named as authors on this manuscript and they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


Included all works (data, text, and theories) of other authors have been cited properly.


The work is original, and neither it was published previously nor it has been submitted somewhere simultaneously for publication in any form.


  1. Ahonen H, de Alvarenga AG, Amaral ARS (2014) Simulated annealing and tabu search approaches for the corridor allocation problem. Eur J Oper Res 232:221–233MathSciNetCrossRefzbMATHGoogle Scholar
  2. Amaral ARS (2012) The corridor allocation problem. Comput Oper Res 39(12):3325–3330CrossRefzbMATHGoogle Scholar
  3. Anjos MF, Fischer A, Hungerl\(\ddot{\rm a}\)nder P (2015) Solution approaches for equidistant double- and multi-row facility layout problems. Les Cahiers du GERAD, ISSN: 0711–2440, GERAD HEC Montréal, 3000, chemin de la Côte-Sainte-Catherine, Montréal (Québec) Canada H3T 2A7Google Scholar
  4. Anjos MF, Kennings A, Vannelli A (2005) A semidefinite optimization approach for the single-row layout problem with unequal dimensions. Discret Optim 2:113–122MathSciNetCrossRefzbMATHGoogle Scholar
  5. Chung J, Tanchoco JMA (2010) The double row layout problem. Int J Prod Res 48(3):709–727CrossRefzbMATHGoogle Scholar
  6. Conover WJ (1999) Practical nonparametric statistics, 3rd edn. Wiley, New YorkGoogle Scholar
  7. Datta D, Amaral ARS, Figueira JR (2011) Single row facility layout problem using a permutation-based genetic algorithm. Eur J Oper Res 213(2):388–394MathSciNetCrossRefzbMATHGoogle Scholar
  8. Datta D, Figueira JR (2012) Some convergence-based M-ary cardinal metrics for comparing performances of multi-objective optimizers. Comput Oper Res 39(7):1754–1762MathSciNetCrossRefzbMATHGoogle Scholar
  9. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, ChichesterzbMATHGoogle Scholar
  10. Deb K, Agarwal S, Pratap A, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRefGoogle Scholar
  11. Ficko M, Brezocnik M, Balic J (2004) Designing the layout of single-and multiple-rows flexible manufacturing system by genetic algorithms. J Mater Process Technol 157:150–158CrossRefGoogle Scholar
  12. Ghosh D, Kothari R (2012) Population heuristics for the corridor allocation problem. Technical Report W.P. No. 2012-09-02, Indian Institute of Management, Ahmedabad, IndiaGoogle Scholar
  13. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, New JerseyzbMATHGoogle Scholar
  14. Kalita Z, Datta D (2014) Solving the bi-objective corridor allocation problem using a permutation-based genetic algorithm. Comput Oper Res 52:123–134MathSciNetCrossRefzbMATHGoogle Scholar
  15. Kothari R, Ghosh D (2013) Tabu search for the single row facility layout problem using exhaustive 2-opt and insertion neighborhoods. Eur J Oper Res 224(1):93–100MathSciNetCrossRefzbMATHGoogle Scholar
  16. Mann PS (2004) Introductory statistics, 5th edn. Wiley, HobokenzbMATHGoogle Scholar
  17. Palubeckis G (2015) Fast local search for single row facility layout. Eur J Oper Res,
  18. Samarghandi H, Eshghi K (2010) An efficient tabu algorithm for the single row facility layout problem. Eur J Oper Res 205:98–105MathSciNetCrossRefzbMATHGoogle Scholar
  19. Wang S, Zuo X, Liu X, Zhao X, Li J (2015) Solving dynamic double row layout problem via combining simulated annealing and mathematical programming. Appl Soft Comput 37:303–310CrossRefGoogle Scholar
  20. Zuo X, Murray CC, Smith AE (2014) Solving an extended double row layout problem using multiobjective tabu search and linear programming. IEEE Trans Autom Sci Eng 11(4):1122–1132CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringTezpur UniversityNapaam, TezpurIndia
  2. 2.Faculty of InformaticsKaunas University of TechnologyKaunasLithuania

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