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Volumn 40, Issue 12, 2013, Pages 4812-4819

A non dominated ranking Multi Objective Genetic Algorithm and electre method for unequal area facility layout problems

Author keywords

Electre method; Facility layout problems; Non dominated Ranking Genetic Algorithm; Slicing structure

Indexed keywords

COMPUTATIONAL COMPLEXITY; DECISION MAKING; EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; OPTIMAL SYSTEMS; PARETO PRINCIPLE; PLANT LAYOUT;

EID: 84885190532     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2013.02.026     Document Type: Article
Times cited : (69)

References (40)
  • 1
    • 84861193408 scopus 로고    scopus 로고
    • Multi objective genetic algorithm for the facility layout problem based upon slicing structure encoding
    • Aiello, G., LaScalia, M., & Enea, A. (2012). Multi Objective Genetic Algorithm for the facility layout problem based upon slicing structure encoding. Expert Systems with Applications, 39(12), 10352-10352.
    • (2012) Expert Systems with Applications , vol.39 , Issue.12 , pp. 10352-10352
    • Aiello, G.1    Lascalia, M.2    Enea, A.3
  • 2
    • 71049145727 scopus 로고    scopus 로고
    • Non-dominated ranked genetic algorithm for solving multi-objective optimization problems: Nrga
    • Al Jadaan, O., Rajamani, L., & Rao, R. (2008), Non-dominated ranked genetic algorithm for solving multi-objective optimization problems:Nrga, Journal of Theoretical and Applied Information Technology, 4(1), 60-67.
    • (2008) Journal of Theoretical and Applied Information Technology , vol.4 , Issue.1 , pp. 60-67
    • Al Jadaan, O.1    Rajamani, L.2    Rao, R.3
  • 3
    • 0035362129 scopus 로고    scopus 로고
    • Locating input and output points in facilities design - A comparison of constructive, evolutionary, and exact methods
    • DOI 10.1109/4235.930310, PII S1089778X01053723
    • Arapoglu, R. A., Norman, B. A., & Smith, A. E. (2001). Locating input and output points in facilities design -Acomparison ofconstructive, evolutionary, and exact methods. IEEE Transactions on Evolutionary Computation, 3, 192-203. (Pubitemid 32645069)
    • (2001) IEEE Transactions on Evolutionary Computation , vol.5 , Issue.3 , pp. 192-203
    • Arapoglu, R.A.1    Norman, B.A.2    Smith, A.E.3
  • 4
    • 0000970291 scopus 로고
    • Aheuristic algorithm and simulation approach to the relative location of facilities
    • Aromur, G. C., & Buffa, E. S. (1963). Aheuristic algorithm and simulation approach to the relative location of facilities. Management Science, 9, 294-309.
    • (1963) Management Science , vol.9 , pp. 294-309
    • Aromur, G.C.1    Buffa, E.S.2
  • 5
    • 59349093577 scopus 로고    scopus 로고
    • Evaluation ofsequential, multi-objective, and parallel interactive genetic algorithms for multi-objective optimization problems
    • Brintup, A. M., Takagi, H., Tiwari, A., & Ramsden, J. (2006). Evaluation ofsequential, multi-objective, and parallel interactive genetic algorithms for multi-objective optimization problems. Journal of Biological Physics and Chemistry, 6, 137-146.
    • (2006) Journal of Biological Physics and Chemistry , vol.6 , pp. 137-146
    • Brintup, A.M.1    Takagi, H.2    Tiwari, A.3    Ramsden, J.4
  • 6
    • 31744439068 scopus 로고    scopus 로고
    • Heuristic approach for solving the multiobjective facility layout problem
    • Chen, C. W., & Sha, D. Y. (2005). Heuristic approach for solving the multiobjective facility layout problem. International Journal of Production Research, 43(21), 4493-4493.
    • (2005) International Journal of Production Research , vol.43 , Issue.21 , pp. 4493-4493
    • Chen, C.W.1    Sha, D.Y.2
  • 8
    • 79955885772 scopus 로고    scopus 로고
    • Single row facility location problem using a permutation-based genetic algorithm
    • Datta, D., Amaral, A. R. S., & Figueira, J. R. (2011). Single row facility location problem using a permutation-based genetic algorithm. European Journal of Operational Research, 213, 388-388.
    • (2011) European Journal of Operational Research , vol.213 , pp. 388-388
    • Datta, D.1    Amaral, A.R.S.2    Figueira, J.R.3
  • 11
    • 0001953837 scopus 로고
    • Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization
    • San Mateo, CA
    • Fonseca C. M. & Fleming P. J. (1993). Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In Fifth international conference on genetic algorithms (pp. 416-423)San Mateo, CA.
    • (1993) Fifth International Conference on Genetic Algorithms , pp. 416-423
    • Fonseca, C.M.1    Fleming, P.J.2
  • 12
    • 0036530772 scopus 로고    scopus 로고
    • A fast and elitist multiobjective genetic algorithm: NSGA-II
    • DOI 10.1109/4235.996017, PII S1089778X02041012
    • Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). Afast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions onEvolutionary Computation, V6(2), 182-197. (Pubitemid 34555372)
    • (2002) IEEE Transactions on Evolutionary Computation , vol.6 , Issue.2 , pp. 182-197
    • Deb, K.1    Pratap, A.2    Agarwal, S.3    Meyarivan, T.4
  • 13
    • 0024622632 scopus 로고
    • New approaches for heuristic search: Abilateral linkage with artificial intelligence
    • Glover, F., & Greenberg, H. J. (1989). New approaches for heuristic search:Abilateral linkage with artificial intelligence. European Journal of Operational Research, 39, 119-130.
    • (1989) European Journal of Operational Research , vol.39 , pp. 119-130
    • Glover, F.1    Greenberg, H.J.2
  • 14
    • 0026977142 scopus 로고
    • Interactive layout heuristic based on hexagonal adjacency graphs
    • DOI 10.1016/0377-2217(92)90033-6
    • Goetschalckx, M. (1992). Aninteractive layout heuristic based onhexagonal adjacency graphs. European Journal ofOperational Research, 63, 304-321. (Pubitemid 23607249)
    • (1992) European Journal of Operational Research , vol.63 , Issue.2 , pp. 304-321
    • Goetschalckx Marc1
  • 15
    • 0004181390 scopus 로고
    • Search, optimization and machine learning. Addison-Wesley
    • Goldberg, D. E. (1989). Genetic algorithms. Search, optimization and machine learning. Addison-Wesley.
    • (1989) Genetic Algorithms
    • Goldberg, D.E.1
  • 17
    • 0026826080 scopus 로고
    • Experimental analysis ofsimulated annealing based algorithms for the layout problem
    • Heragu, S. S., & Alfa, A. S. (1992). Experimental analysis ofsimulated annealing based algorithms for the layout problem. European Journal of Operational Research, 57, 190-202.
    • (1992) European Journal of Operational Research , vol.57 , pp. 190-202
    • Heragu, S.S.1    Alfa, A.S.2
  • 21
    • 5644282920 scopus 로고    scopus 로고
    • Animproved genetic algorithm for multi- floor facility layout problems having inner structure walls and passages
    • Lee, K. Y., Roh, M. I., & Jeong, H. S. (2005). Animproved genetic algorithm for multi- floor facility layout problems having inner structure walls and passages. Computers & Operations Research, 32(4), 879-899.
    • (2005) Computers & Operations Research , vol.32 , Issue.4 , pp. 879-899
    • Lee, K.Y.1    Roh, M.I.2    Jeong, H.S.3
  • 22
    • 0030270848 scopus 로고    scopus 로고
    • Facility layout objective function and robust layouts
    • Meller, R. D., & Gau, K. Y. (1996). Facility layout objective function and robust layouts. International Journal of Production Research, 34(10), 2727-2727.
    • (1996) International Journal of Production Research , vol.34 , Issue.10 , pp. 2727-2727
    • Meller, R.D.1    Gau, K.Y.2
  • 25
    • 0032314483 scopus 로고    scopus 로고
    • Facilities layout design by genetic algorithms
    • PII S0360835298001508
    • Moghaddam, R. T., & Shayan, E. (1998). Facility layout design bygenetic algorithm. International Journal ofComputers and Industrial Engineering, 35(3-4), 527-530. (Pubitemid 128674770)
    • (1998) Computers and Industrial Engineering , vol.35 , Issue.3-4 , pp. 527-530
    • Moghaddain, R.T.1    Shayan, E.2
  • 27
    • 84860392554 scopus 로고    scopus 로고
    • Mathematical modeling and hybrid heuristic for unequal size facility layout problem
    • Nordin, N. N., Zainuddin, Z. M., Salim, S., & Ponnusamy, R. R. (2009). Mathematical modeling and hybrid heuristic for unequal size facility layout problem. Journal of Fundamental Sciences, 5(1), 87-89.
    • (2009) Journal of Fundamental Sciences , vol.5 , Issue.1 , pp. 87-89
    • Nordin, N.N.1    Zainuddin, Z.M.2    Salim, S.3    Ponnusamy, R.R.4
  • 29
    • 75149169172 scopus 로고    scopus 로고
    • Extensions tostats for practical applications of the facility layout problem
    • Scholz, D., Jaehn, F., & Junker, A. (2010). Extensions toSTaTS for practical applications of the facility layout problem. European Journal of Operational Research, 204, 463-472.
    • (2010) European Journal of Operational Research , vol.204 , pp. 463-472
    • Scholz, D.1    Jaehn, F.2    Junker, A.3
  • 31
    • 0000852513 scopus 로고
    • Multiobjective optimization using nondominated sorting in genetic algorithms
    • Srinivas, N., & Deb, K. (1995). Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2, 221-248.
    • (1995) Evolutionary Computation , vol.2 , pp. 221-248
    • Srinivas, N.1    Deb, K.2
  • 36
    • 13444304124 scopus 로고    scopus 로고
    • Asolution tothe unequal area facilities layout problem bygenetic algorithm
    • Wanga, M. J., Hub, M. H., & Kub, M. Y. (2005). Asolution tothe unequal area facilities layout problem bygenetic algorithm. Computers in Industry, 56, 207-220.
    • (2005) Computers in Industry , vol.56 , pp. 207-220
    • Wanga, M.J.1    Hub, M.H.2    Kub, M.Y.3
  • 37
    • 0037449044 scopus 로고    scopus 로고
    • Ahierarchical ahp/dea methodology for the facilities layout design problem
    • Yang, T., & Kuo, C. (2003). Ahierarchical AHP/DEA methodology for the facilities layout design problem. European Journal of Operational Research, 147, 128-136.
    • (2003) European Journal of Operational Research , vol.147 , pp. 128-136
    • Yang, T.1    Kuo, C.2
  • 38
    • 35148893275 scopus 로고    scopus 로고
    • A local genetic approach to multi-objective, facility layout problems with fixed aisles
    • DOI 10.1080/00207540600818179, PII 770289029
    • Ye, M., & Zhou, G. (2007). Alocal genetic approach tomultiobjective, facility layout problems with fixedaisles. International Journal of Production Research, 45, 5243-5264. (Pubitemid 47537050)
    • (2007) International Journal of Production Research , vol.45 , Issue.22 , pp. 5243-5264
    • Ye, M.1    Zhou, G.2
  • 39
    • 2942547409 scopus 로고    scopus 로고
    • SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization
    • K. C. Giannakoglou and others, eds, International Center for Numerical Methods in Engineering (CIMNE)
    • Zitzler, E., Laumanns, M., Thiele, L. (2001):SPEA2:Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. InK. C. Giannakoglou and others, eds, Evolutionary Methods for Design, Optimisation and Control with Application toIndustrial Problems (EUROGEN2001), (pp95-100)2002. International Center for Numerical Methods in Engineering (CIMNE).
    • (2001) Evolutionary Methods for Design, Optimisation and Control with Application ToIndustrial Problems (EUROGEN2001) , pp. 95-100
    • Zitzler, E.1    Laumanns, M.2    Thiele, L.3
  • 40
    • 0033318858 scopus 로고    scopus 로고
    • Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach
    • Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms:A comparative case study and the strength pareto approach. IEEE Transaction on Evolutionary Computation, 3, 257-257.
    • (1999) IEEE Transaction on Evolutionary Computation , vol.3 , pp. 257-257
    • Zitzler, E.1    Thiele, L.2


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