Abstract
Arranging non-identical machines into a limited area of manufacturing shop floor is an essential part of plant design. Material handling distance is one of the key performance indexes of internal logistic activities within manufacturing companies. It leads to the efficient productivity and related costs. Machine layout design is known as facility layout problem and classified into non-deterministic polynomial-time hard problem. The objective of this paper was to compare the performance of Bat Algorithm (BA), Genetic Algorithm (GA) and Shuffled Frog Leaping Algorithm (SFLA) for designing machine layouts in a multiple-row environment with the aim to minimise the total material handling distance. An automated machine layout design tool has been coded in modular style using a general purpose programming language called Tcl/Tk. The computational experiment was designed and conducted using four MLD benchmark datasets adopted from literature. It was found that the proposed algorithms performed well in different aspects.
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References
Pongcharoen, P., Hicks, C., Braiden, P.M., Stewardson, D.J.: Determining optimum Genetic Algorithm parameters for scheduling the manufacturing and assembly of complex products. International Journal of Production Economics 78, 311–322 (2002)
Pongcharoen, P., Chainate, W., Pongcharoen, S.: Improving Artificial Immune System Performance: Inductive Bias and Alternative Mutations. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 220–231. Springer, Heidelberg (2008)
Kirkpatrick, S., Gelatt, C.D., Cevvhi, M.P.: Optimization by Simulated Annealing. Science New Series 220, 671–680 (1983)
Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: Harmony search. Simulation 76, 60–68 (2001)
Glover, F.: Tabu search: Part I. ORCA Journal on Computing 1, 190–206 (1989)
Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company, Inc. (1989)
Holland, J.H.: Outline for a logical theory of adaptive systems. Journal of ACM 3, 297–314 (1962)
Dorigo, M.: Optimization, Learning and Nature Algorithms. PhD, Politecnico di Milano, Italy (1992)
Eusuff, M.M., Lansey, K.E.: Optimization of water distribution network design using the Shuffled Frog Leaping Algorithm. Journal of Water Resources Planning and Management-Asce 129, 210–225 (2003)
Yang, X.-S.: Nature-Inspired Metaheuristic Algorithm. Luniver Press (2008)
Yang, X.-S.: Nature-Inspired Metaheuristic Algorithm, 2nd edn. Luniver Press (2010)
Tompkins, J.A., White, J.A., Bozer, Y.A., Tanchoco, J.M.A.: Facilities Planning, 4th edn. John Wiley & Sons, Inc. (2010)
Loiola, E.M., de Abreu, N.M.M., Boaventura-Netto, P.O., Hahn, P., Querdo, T.: A survey for the quadratic assignment problem. European Journal of Operational Research 176, 657–690 (2007)
Wang, G., Guo, L.: A novel hybrid bat algorithm with harmony search for global numerical optimization. Journal of Applied Mathematics 2013 (2013)
Musikapun, P., Pongcharoen, P.: Solving multi-stage multi-machine multi-product scheduling problem using Bat Algorithm. In: 2nd International Conference on Management and Artificial Intelligence, Thailand (2012)
Gandomi, A.H., Yang, X.S., Alavi, A.H., Talatahari, S.: Bat algorithm for constrained optimization tasks. Neural Computing and Applications, 1–17 (2012)
Yang, X.S.: Bat algorithm for multi-objective optimisation. International Journal of Bio-Inspired Computation 3, 267–274 (2011)
Yang, X.S., Gandomi, A.H.: Bat algorithm: A novel approach for global engineering optimization. Engineering Computations (Swansea, Wales) 29, 464–483 (2012)
Vitayasak, S.: Facility layout problem: a 10-year review and reserach perspectives. Naresuan Unversity Engineering Journal 5, 46–62 (2010) (in Thai)
Drira, A., Pierreval, H., Hajri-Gabouj, S.: Facility layout problems: A survey. Annual Reviews in Control 31, 255–267 (2007)
Osman, I.H., Laporte, G.: Metaheuristics: a bibliography. Annual of Operations Research 63 (1996)
Gen, M., Cheng, R.: Genetic Algorithms and Engineering Design. John Wiley & Sons, Inc. (1997)
Pan, Q.-K., Wang, L., Gao, L., Li, J.: An effective shuffled frog-leaping algorithm for lot-streaming flow shop scheduling problem. International Journal of Advanced Manufacturing Technology (2010)
Ousterhout, J.K.: Tcl and Tk tookit, 2nd edn. Addison Wesley (2010)
Nearchou, A.C.: Meta-heuristics from nature for the loop layout design problem. International Journal of Production Economics 101, 312–328 (2006)
Vitayasak, S.: Multiple-row rotatable machine layout using Genetic Algorithm. Research report, Naresuan Univeristy, Phitsanulok, Thailand (2011) (in Thai)
Vitayasak, S., Pongcharoen, P.: Interaction of crossover and mutation operations for designing non-rotatable machine layout. In: Operations Research Network Conference, Bangkok, Thailand (2011)
Leechai, N., Iamtan, T., Pongcharoen, P.: Comparison on Rank-based Ant System and Shuffled Frog Leaping for design multiple row machine layout. SWU Engineering Journal 4, 102–115 (2009)
Singpraya, S., Dedklen, S., Vitayasak, S., Pongcharoen, P.: Adaptive Genetic Algorithm for designing non-identical rectangular machine layout. In: Operations Research Network Conference, Thailand (2012)
Iamtan, T., Pongcharoen, P.: Swap and adjustment techniques in Shuffled Frog Leaping algorithm for solving machine layout. Thai VCML Journal 3, 25–36 (2010)
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Dapa, K., Loreungthup, P., Vitayasak, S., Pongcharoen, P. (2013). Bat Algorithm, Genetic Algorithm and Shuffled Frog Leaping Algorithm for Designing Machine Layout. In: Ramanna, S., Lingras, P., Sombattheera, C., Krishna, A. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2013. Lecture Notes in Computer Science(), vol 8271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44949-9_6
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DOI: https://doi.org/10.1007/978-3-642-44949-9_6
Publisher Name: Springer, Berlin, Heidelberg
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