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MIP model and elitist strategy hybrid GA–SA algorithm for layout design

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Abstract

It is most important for any manufacturing industry to have an efficient layout for their production environment to participate in global competition. One of the prime objectives of such an organisation is to decide an optimal arrangement of their facilities (machines or departments) in a two-dimensional planar region satisfying desired objectives, which is termed facility layout problem. To overcome the drawbacks of traditional layout design methodology, it is attempted to solve three important layout design problems such as inter-cell layout design, determination of optimum location for input/output stations and flow path layout design of material handling system simultaneously in an integrated manner. The quality of the final layout is evaluated by minimizing the total material handling cost, where the perimeter distance metric is used for the distance measurement. Sequence-pair, an elegant representation technique is used for layout encoding. The translation from sequence-pair to layout is efficiently done by longest common subsequence computation methodology. Due to the non-polynomial hard nature of the problem considered, an elitist strategy based hybrid genetic algorithm that uses simulated annealing as local search mechanism (ESHGA) is developed and tested with test problem instances available in the literature. The results indicate that proposed integrated methodology with developed mixed integer programming based mathematical model along with ESHGA could generate realistic layouts compared to reported result.

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References

  • Aiello, G., Enea, M., & Galante, G. (2002). An integrated approach to the facilities and material handling system design. International Journal of Production Research, 40, 4007–4017.

    Article  Google Scholar 

  • Arapoglu, R. A., Norman, B. A., & Smith, A. E. (2001). Locating input and output points in facilities design—A comparison of constructive, evolutionary, and exact methods. IEEE Transactions on Evolutionary Computation, 3, 192–203.

    Article  Google Scholar 

  • Ariafar, Sh, & Ismail, N. (2009). An improved algorithm for layout design in cellular manufacturing systems. Journal of Manufacturing Systems, 28, 132–139.

    Article  Google Scholar 

  • Armour, G. C., Buffa, E. S., & Vollmann, T. E. (1964). Allocating facilities with CRAFT. Harvard Business Review, 42, 136–158.

    Google Scholar 

  • Banerjee, P., Zhou, Y., & Montreuil, B. (1997). Genetically assisted optimization of cell layout and material flow path skeleton. IIE Transactions, 29, 277–291.

    Google Scholar 

  • Caccetta, L., & Kusumah, Y. S. (2001). Computational aspects of the facility layout design problem. Non-Linear Analysis, 7, 5599–5610.

    Article  Google Scholar 

  • Cochran, J. K., Horng, S. M., & Fowler, J. W. (2003). A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines. Computers and Operations Research, 30, 1087–1102.

    Article  Google Scholar 

  • Cormen, T. H., Leiserson, C. E., & Rivest, R. L. (1990). Introduction to algorithms. New York: McGraw-Hill and MIT Press.

    Google Scholar 

  • Das, S. (1993). A facility layout method for flexible manufacturing systems. International Journal of Production Research, 31, 279–297.

    Article  Google Scholar 

  • Deb, S. K., & Bhattacharyya, B. (2003). Manufacturing facility layout design based on simulated annealing. In Proceedings of the National Conference on Advances in Manufacturing Systems, India, pp. 117–122.

  • 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, 4787–4812.

    Article  Google Scholar 

  • Deb, S. K., Bhattacharyya, B., & Sorkhel, S. K. (2003). Facility layout and material handling equipment selection planning using hybrid methodology. International Journal of Industrial Engineering, 10, 436–443.

    Google Scholar 

  • Drira, A., Pierreval, H., & Hajri-Gabouj, S. (2007). Facility layout problems: A survey. Annual Reviews in Control, 31, 255–267.

    Article  Google Scholar 

  • Dumitrescu, D., Lazzerini, B., Jain, L. C., & Dumitrescu, A. (2000). Evolutionary Computation. Boca Raton, FL: CRC Press.

    Google Scholar 

  • Dunker, T., Radons, G., & Westkamper, E. (2003). A coevolutionary algorithm for a facility layout problem. International Journal of Production Research, 34, 2565–2585.

    Google Scholar 

  • Egbelu, P. J., & Tanchoco, J. M. A. (1986). Potential for bi-directional guide path for automated guided vehicle based systems. International Journal of Production Research, 24, 1075–1097.

    Article  Google Scholar 

  • Ficko, M., Brezocnik, M., & Balic, J. (2004). Designing the layout of single- and multiple-rows flexible manufacturing system by genetic algorithms. Journal of Materials Processing Technology, 157, 150–158.

    Article  Google Scholar 

  • Gómez, A., Fernández, Q. I., De la Fuente García, D., & García, P. J. (2003). Using genetic algorithms to resolve layout problems in facilities where there are aisles. International Journal of Production Economics, 84, 271–282.

    Article  Google Scholar 

  • Goncalves, J. F., & Resende, M. G. C. (2011). A parallel multi-population genetic algorithm for a constrained two-dimensional orthogonal packing problem. Journal of Combinatorial Optimization, 22, 180–201.

    Article  Google Scholar 

  • Hani, Y., Amodeo, L., Yalaoui, F., & Chen, H. (2007). Ant colony optimization for solving an industrial layout problem. European Journal of Operational Research, 183, 633–642.

    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 

  • Helber, S., Böhme, D., Oucherif, F., Lagershausen, S., & Kasper, S. (2014). A hierarchical facility layout planning approach for large and complex hospitals. Department of Production Management, Leibniz University, Hannover, Germany. https://ideas.repec.org/p/han/dpaper/dp-527.html.

  • Herrmann, J. W., Ioannou, G., Minis, I., Nagi, R., & Proth, J. M. (1995). Design of material flow networks in manufacturing facilities. Journal of Manufacturing Systems, 14, 277–289.

    Article  Google Scholar 

  • Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press.

    Google Scholar 

  • Ho, Y. C., & Moodie, C. L. (2000). A hybrid approach for concurrent layout design of cells and their flow paths in a tree configuration. International Journal of Production Research, 38, 895–928.

    Article  Google Scholar 

  • Honiden, T. (2004). Tree structure modelling and genetic algorithm based approach to unequal-area facility layout problem. Industrial Engineering & Management Systems, 3, 123–128.

    Google Scholar 

  • Hu, G. H., Chen, Y. P., Zhou, Z. D., & Fang, H. C. (2007). A genetic algorithm for the inter-cell layout and material handling system design. International Journal of Advanced Manufacturing Technology, 34, 1153–1163.

    Article  Google Scholar 

  • Hungerländer, P., & Rendl, F. (2012). A computational study and survey of methods for the single-row facility layout problem. Technical report, Alpen-Adria-Universitaet, Klagenfurt.

  • Jerin Leno, I., Saravana Sankar, S., & Ponnambalam S. G. (2013). An elitist strategy genetic algorithm using simulated annealing algorithm as local search for facility layout design. International Journal of Advanced Manufacturing Technology. doi:10.1007/s00170-013-5519-3.

  • Khan, S. A., & Engelbrecht, A. P. (2007). A new fuzzy operator and its application to topology design of distributed local area networks. Information Sciences, 177, 2692–2711.

    Article  Google Scholar 

  • Kim, J. G., & Goetschalckx, M. (2005). An integrated approach for the concurrent determination of the block layout and the input and output point locations based on the contour distance. International Journal of Production Research, 43, 2027–2047.

    Article  Google Scholar 

  • Kim, J. G., & Kim, Y. D. (2000). Layout planning for facilities with fixed shapes and input and output point. International Journal of the operational research society, 38, 4635–4653.

  • Kirkpatrick, S., Gelatt, C., & Vecchi, M. (1983). Optimization by simulated annealing. Science, 220, 671–680.

    Article  Google Scholar 

  • Kochhar, J. S., Foster, B. T., & Heragu, S. S. (1998). Hope: A genetic algorithm for the unequal area facility layout problem. Computers and Operations Research, 25, 583–594.

    Article  Google Scholar 

  • Koopmans, T. C., & Beckman, M. J. (1957). Assignment problems and the location of economic activities. Econometrica, 25, 53–76.

    Article  Google Scholar 

  • Kouvelis, P., Kurawarwala, A. A., & Gutiérrez, G. J. (1992). Algorithms for robust single and multiple period layout planning for manufacturing systems. European Journal of Production Research, 63, 287–303.

    Google Scholar 

  • Kulturel-Konak, S. (2007). Approaches to uncertainties in facility layout problems: Perspectives at the beginning of the 21st century. Journal of Intelligent Manufacturing, 18, 273–284.

    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, K. Y., Han, S. N., & Myung, I. R. (2003). An improved genetic algorithm for facility layout problems having inner structure walls and passages. Computers & Operations Research, 30, 117–138.

  • Lee, M. L., Kim, J. G., & Kim, Y. D. (2009). Linear programming and Lagrangian relaxation heuristics for designing a material flow network on a block layout. International Journal of Production Research, 47, 5185–5202.

    Article  Google Scholar 

  • Lee, K. Y., Roh, M., & Jeong, H. (2005). An improved genetic algorithm for multi-floor facility layout problems having inner structure walls and passages. Computers & Operations Research, 32, 879–899.

    Article  Google Scholar 

  • Liang, L. Y., & Chao, W. C. (2008). The strategies of tabu search technique for facility layout optimization. Automation in Construction, 17, 657–669.

    Article  Google Scholar 

  • Li, H., & Love, P. E. D. (2000). Genetic search for solving construction site-level unequal-area facility layout problems. Automation in Construction, 9, 217–226.

    Article  Google Scholar 

  • Lin, Q. L., Liu, H. C., Wang, D. J., & Long. L. (2015). Integrating systematic layout planning with fuzzy constraint theory to design and optimize the facility layout for operating theatre in hospitals. Journal of Intelligent Manufacturing. doi:10.1007/s10845-013-0764-8.

  • Luggen, W. (1991). Flexible manufacturing cells and systems. Bergen, NJ: Englewood Cliffs.

    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. (1987). Integrating design of cell layout, input/output station configuration, and flow network of manufacturing systems. In C. R. Liu, A. Requicha, & S. Chandrasekar (Eds.), Intelligent and integrated manufacturing analysis and synthesis (Ped-vol. 25, pp. 315–326). ASME.

  • Montreuil, B. (1990). A modeling framework for integrating layout design and flow network design. In Proceedings from the Material Handling Research Colloquium (vol. 2, pp. 43–58), Hebron, KY.

  • Montreuil, B., & Ratliff, H. D. (1988). Optimizing the location of input/output stations within facilities layout. Engineering Cost and Production Economics, 14, 177–187.

    Article  Google Scholar 

  • Moslemipour, G., & Lee, T. (2012). Intelligent design of a dynamic machine layout in uncertain environment of flexible manufacturing systems. Journal of Intelligent Manufacturing, 23, 1849–1860.

    Article  Google Scholar 

  • Murata, H., Fujiyoshi, K., & Kajitani, Y. (1996). VLSI module placement based on rectangle-packing by the sequence-pair. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 15, 1518–1524.

    Article  Google Scholar 

  • Norman, B. A., Arapoglu, R. A., & Smith, A. E. (2001). Integrated facilities design using a contour distance metric. IIE Transactions, 33, 337–344.

    Google Scholar 

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

    Article  Google Scholar 

  • Peer, S. K., & Sharma, D. K. (2008). Human-computer interaction design with multigoal facilities layout model. Computers and Mathematics with Applications, 56, 2164–2174.

    Article  Google Scholar 

  • Rajasekharan, M., Peters, B. A., & Yang, T. (1998). A genetic algorithm for facility layout design in flexible manufacturing systems. International Journal of Production Research, 36, 95–110.

    Article  Google Scholar 

  • Raman, D., Nagalingam, V., & Gurd, W. (2009a). A genetic algorithm and queuing theory based methodology for facilities layout problem. International Journal of Production Research, 47, 5611–5635.

    Article  Google Scholar 

  • Raman, D., Nagalingam, S. V., & Lin, G. C. I. (2009b). Towards measuring the effectiveness of a facilities layout. Robotics and Computer-Integrated Manufacturing, 25, 191–203.

    Article  Google Scholar 

  • Sadrzadeh, A. (2012). A genetic algorithm with the heuristic procedure to solve multi-line layout problem. Computers & Industrial Engineering, 62, 1055–1064.

    Article  Google Scholar 

  • Samarghandi, H., & Eshghi, K. (2010). An efficient tabu algorithm for the single row facility layout problem. European Journal of Operational Research, 205, 98–105.

    Article  Google Scholar 

  • Schockaert, S., Smart, P. D., & Twaroch, F. A. (2011). Generating approximate region boundaries from heterogeneous spatial information: An evolutionary approach. Information Sciences, 181, 257–283.

    Article  Google Scholar 

  • Scholz, D., Jaehn, F., & Junker, A. (2010). Extensions to STaTS for practical applications of the facility layout problem. European Journal of Operational Research, 204, 463–472.

    Article  Google Scholar 

  • Sedehi, M. S., & Farahani, R. Z. (2009). An integrated approach to determine the block layout, AGV flow path and the location of pick-up/delivery points in single-loop systems. International Journal of Production Research, 47, 3041–3061.

    Article  Google Scholar 

  • Sherali, H. D., Fraticelli, B. M. P., & Meller, R. D. (2003). Enhanced model formulations for optimal facility layout. Operations Research, 51, 629–644.

    Article  Google Scholar 

  • Solimanpur, M., Vrat, P., & Shankar, R. (2004). Ant Colony optimization algorithm to the inter-cell layout problem in cellular manufacturing. European Journal of Operational Research, 157, 592–606.

    Article  Google Scholar 

  • Solimanpur, M., Vrat, P., & Shankar, R. (2005). Ant algorithm for the single row layout problem in flexible manufacturing systems. Computers and Operations Research, 32, 583–598.

    Article  Google Scholar 

  • Tang, X., Wong, D. F., & Tian, R. (2000). Fast evaluation of sequence pair in block placement by longest common subsequence computation. In Proceedings from the design, automation and test in Europe, Paris, pp. 106–111.

  • Tate, D. M., & Smith, E. A. (1995). Unequal-area facility by genetic search. IIE Transactions, 27, 465–472.

    Article  Google Scholar 

  • Tavakkoli-Moghaddam, R., & Shayan, E. (1998). Facilities layout design by genetic algorithms. Computers and Industrial Engineering, 35, 527–530.

    Article  Google Scholar 

  • Tompkins, J. A., White, J. A., Bozer, Y. A., & Tanchoco, J. M. A. (2003). Facilities Planning (3rd ed.). New York: Wiley.

    Google Scholar 

  • Tuzkaya, G., Gülsün, Tuzkaya, R., Onut, S., & Bildik, E. (2013). A comparative analysis of meta-heuristic approaches for facility layout design problem: A case study for an elevator manufacturer. Journal of Intelligent Manufacturing, 24, 357–372.

    Article  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 

  • Welgama, P. S., & Gibson, P. R. (1993). A construction algorithm for the machine layout problem with fixed pick-up and drop-off points. International Journal of Production Research, 11, 2575–2590.

  • Wu, Y., & Appleton, E. (2002). The optimization of block layout and aisle structure by a genetic algorithm. Computers & Industrial Engineering, 41, 371–387.

    Article  Google Scholar 

  • Xu, G., & Papageorgiou, L. G. (2009). Process plant layout using an improvement-type algorithm. Chemical Engineering Research and Design, 87, 780–788.

    Article  Google Scholar 

  • Yang, L., Deuse, J., & Jiang, P. (2013). Multi-objective optimization of facility planning for energy intensive companies. Journal of Intelligent Manufacturing, 24, 1095–1109.

    Article  Google Scholar 

  • Yang, T., Peters, B. A., & Tu, M. (2005). Layout design for flexible manufacturing systems considering single-loop directional flow patterns. European Journal of Operational Research, 164, 440–455.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, National College of Engineering, Maruthakulam, Tamil Nadu, India

    I. Jerin Leno

  2. Department of Mechanical Engineering, Kalasalingam University, Krishnankoil, Tamil Nadu, India

    S. Saravana Sankar

  3. Advanced Engineering Platform and School of Engineering, Monash University Malaysia, Sunway Campus, 47500, Bandar Sunway, Malaysia

    S. G. Ponnambalam

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  1. I. Jerin Leno
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  2. S. Saravana Sankar
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  3. S. G. Ponnambalam
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Correspondence to I. Jerin Leno.

Appendix

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Algorithm 1 Elitist strategy hybrid genetic algorithm—ESHGA

figure a

Algorithm 2 Local search using SA algorithm

figure b

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Jerin Leno, I., Saravana Sankar, S. & Ponnambalam, S.G. MIP model and elitist strategy hybrid GA–SA algorithm for layout design. J Intell Manuf 29, 369–387 (2018). https://doi.org/10.1007/s10845-015-1113-x

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