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A comparative analysis of meta-heuristic approaches for facility layout design problem: a case study for an elevator manufacturer

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Abstract

In this study, a facility layout problem having NP-hard problem characteristic is attempted to be solved by using two different meta-heuristic approaches—Genetic Algorithm (GA) and Simulated Annealing (SA)—and a hybrid approach—Genetic Algorithm/Simulated Annealing (HGASA). The case study is completed for a company which can be seen as a small or a medium size enterprise. First, parameter values of GA and SA are determined by testing for various combinations of them. Then, the algorithms are run for one hundred times. The results of the algorithms are compared based on their fitness values and calculation time requirements using the paired-t test, mean and standard values. The results show that SA performs better than the others in terms of the fitness values and the time requirements. In this study, we also test the performance of our GA, SA and HGASA methodologies using some of the well-known test problems from the literature. We obtain very close results to those in literature.

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References

  • Aiello G., Enea M., Galante G. (2006) A multi-objective approach to facility layout problem by genetic search algorithm and Electre method. Robotics and Computer-Integrated Manufacturing 22: 447–455

    Article  Google Scholar 

  • Alvarenga A. G., Negreiros-Gomes F. J., Mestria M. (2000) Metaheuristic methods for a class of the facility layout problem. Journal of Intelligent Manufacturing 11: 421–430

    Article  Google Scholar 

  • Armour G. C., Buffa E. S. (1963) A heuristic algorithm and simulation approach to relative allocation of facilities. Management Science 9: 294–309

    Article  Google Scholar 

  • Balakrishnan J., Cheng C. H., Wong K. F. (2003) FACOPT: A user friendly facility layout optimization system. Computers & Operations Research 30: 1625–1641

    Article  Google Scholar 

  • Bozer Y. A., Meller R. D. (1997) A re-examination of the distance-based facility layout problem. IIE Transactions 29(7): 549–560

    Google Scholar 

  • Bozer Y. A., Meller R. D., Erlabacher S. J. (1994) An improvement type layout algorithm for single and multiple-floor facilities. Management Science 40(7): 918–932

    Article  Google Scholar 

  • Burkard R.E., Karisch S.E., Rendl F. (1997) QAPLIB: A quadratic assignment problem library. Journal of Global optimization 10: 391–403

    Article  Google Scholar 

  • Deb S. K., Bhattacharyya B. (2005) Solution of facility layout problems with pickup/drop-off locations using random search techniques. International Journal of Production Research 43(22): 4787–4812

    Article  Google Scholar 

  • Enea M., Galante G., Panascia E. (2005) The facility layout problem approached using a fuzzy model and a genetic search. Journal of Intelligent Manufacturing 16: 303–316

    Article  Google Scholar 

  • Garey M. R., Johnson D. S. (1979) Computers and intractability: A guide to theory of NP-completeness. WH Freemen, New York

    Google Scholar 

  • Gen M., Cheng R. (1997) Genetic algorithms and engineering design. Wiley, New York

    Google Scholar 

  • Goetschalckx M. (1992) An interactive layout heuristic based on hexagonal adjacency graphs. European Journal of Operations Research 63: 304–321

    Article  Google Scholar 

  • He R. S., Hwang S. E. (2006) Damage detection by an adaptive real-parameter simulated annealing genetic algorithms. Computers and Structures 84: 2231–2243

    Article  Google Scholar 

  • Heragu S., Kusiak A. (1991) Efficient models for the facility layout problems. European Journal of Operations Research 53: 1–13

    Article  Google Scholar 

  • Holland J. H. (1975) Adaptation in natural and artificial systems: An introductory analysis with applications to biology control, and artificial intelligence. University of Michigan Press, Ann Arbor, MI

    Google Scholar 

  • Hu M. H., Wang M. J. (2004) Using genetic algorithms on facilities layout problems. International Journal of Advanced Manufacturing Technology 23: 301–310

    Article  Google Scholar 

  • Jwo W. S., Liu C. W., Liu C. C. (1999) Large-scale optimal VAR planning by hybrid simulated annealing/genetic algorithm. Electrical Power and Energy Systems 21: 39–44

    Article  Google Scholar 

  • Kirkpatrick S., Gelatt C. D., Vecchi M. P. (1983) Optimization by Simulated Annealing. Science 220(4598): 671–680

    Article  Google Scholar 

  • Konak A., Coit D. W., Smith A. E. (2006) Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering and System Safety 91: 992–1007

    Article  Google Scholar 

  • Kusiak A., Heragu S. S. (1987) The facility layout problems. European Journal of Operations Research 29: 229–251

    Article  Google Scholar 

  • Lee R., Moore J. M. (1967) CORELAP-computerized relationship layout planning. Journal of Industrial Engineering 18(3): 195–200

    Google Scholar 

  • Lee Y. H., Lee M. H. (2002) A shape-based block layout approach to facility layout problems using hybrid genetic algorithm. Computers & Industrial Engineering 42: 237–248

    Article  Google Scholar 

  • Liu Q., Meller R. D. (2007) A sequence-pair representation and MIP-model-based heuristic for the facility layout problem with rectangular departments. IIE Transactions 39: 377–394

    Article  Google Scholar 

  • Matsuzaki K., Irohara T., Yoshimoto K. Y. (1999) Heuristic algorithm to solve the multi-floor layout problem with the consideration of elevator utilization. Computers & Industrial Engineering 36: 487–502

    Article  Google Scholar 

  • Mckendall A. R., Shang J., Kuppusamy S. (2006) Simulated annealing heuristics for the dynamic facility layout problem. Computers & Operations Research 33(8): 2431–2444

    Article  Google Scholar 

  • Meller R. D., Gau K. Y. (1996) The facility layout problem: Recent and emerging trends and perspectives. Journal of Manufacturing Systems 15: 351–366

    Article  Google Scholar 

  • M’Hallah R. (2007) Minimizing total earliness and tardiness on a single machine using a hybrid heuristic. Computers & Operations Research 34: 3126–3142

    Article  Google Scholar 

  • Montreuil B. (1990) A modeling framework for integrating layout design and flow network design. In Proceedings from the material handling research colloquium, Hebron, KY (pp. 43–58)

  • Norman B. A., Smith A. E. (2006) A continuous approach to considering uncertainty in facility design. Computers & Operations Research 33(6): 1760–1775

    Article  Google Scholar 

  • Pasandideh, S. H. R., & Niaki, S. T. A. (in press). Genetic application in a facility layout problem with random demand within queing framework. Journal of Intelligent Manufacturing.

  • Seckiner A. U., Kurt M. (2007) A Simulated annealing approach to the solution of job rotation scheduling problems. Applied Mathematics and Computation 187(1): 31–45

    Article  Google Scholar 

  • Seehof J. M., Evans W. O. (1967) Automated layout design program. Journal of Industrial Engineering 18(12): 690–695

    Google Scholar 

  • Singh S. P., Sharma R. R. K. (2006) A review of different approaches to the facility layout problems. International Journal of Advanced Manufacturing Technology 30: 425–433

    Article  Google Scholar 

  • Tarkesh H., Atighehchian A., Nookabadi A. S. (2009) Facility layout design using virtual multi-agent system. Journal of Intelligent Manufacturing 20: 347–357

    Article  Google Scholar 

  • Tompkins J. A., Reed R. J. (1976) An applied for the facilities design problem. International Journal of Production Research 14(5): 583–595

    Article  Google Scholar 

  • Tuzkaya G., Gülsün B., Bildik E. (2011) Reverse logistics network design using a hybrid genetic algorithm and simulated annealing methodology. In: Mahdavi I., Mohebbi S., Cho N. (eds) Electronic supply network coordination in intelligent and dynamic environment: Modeling and implementation. IGI Global, USA, Hershey, PA, pp 168–186

    Google Scholar 

  • Tuzkaya U. R., Ertay T., Ruan D. (2005) Simulated annealing approach for the multi-objective facility layout problem. Intelligent data mining: Techniques and applications. Springer, New York

    Google Scholar 

  • Wang M. J., Hu M. H., Ku M. Y. (2005) A solution to the unequal area facilities layout problem by genetic algorithm. Computers in Industry 56: 207–220

    Article  Google Scholar 

  • Yao R., Yang B., Cheng G., Tao X., Meng F (2003) Kinetics research for the synthesis of branch ether using genetic-simulated annealing algorithm with multi-pattern evolution. Chemical Engineering Journal 94: 113–119

    Article  Google Scholar 

  • Yoo M., Gen M. (2007) Scheduling algorithm for real-time tasks using multiobjective hybrid genetic algorithm in heterogeneous multiprocessors system. Computers & Operations Research 34: 3084–3098

    Article  Google Scholar 

  • Yu H., Fang H., Yao P., Yuan Y. (2000) A combined genetic algorithm/simulated annealing algorithm for large scale system energy integration. Computers and Chemical Engineering 24: 2023–2035

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Industrial Engineering, Yildiz Technical University, Barbaros Street, Yildiz, 34349, Istanbul, Turkey

    Gülfem Tuzkaya, Bahadır Gülsün, Umut R. Tuzkaya, Semih Onut & Ender Bildik

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  1. Gülfem Tuzkaya
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  2. Bahadır Gülsün
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  3. Umut R. Tuzkaya
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  4. Semih Onut
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  5. Ender Bildik
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Correspondence to Gülfem Tuzkaya.

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Tuzkaya, G., Gülsün, B., Tuzkaya, U.R. et al. A comparative analysis of meta-heuristic approaches for facility layout design problem: a case study for an elevator manufacturer. J Intell Manuf 24, 357–372 (2013). https://doi.org/10.1007/s10845-011-0599-0

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  • DOI: https://doi.org/10.1007/s10845-011-0599-0

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