An island model genetic algorithm for unequal area facility layout problems

Expert Systems with Applications

Volumn 68, Issue , 2017, Pages 151-162

  Palomo Romero, Juan M ^a   Salas Morera, Lorenzo ^a   García Hernández, Laura ^a  

^a UNIVERSITY OF CÓRDOBA   (Spain)

Author keywords

Flexible bay structure; Island model; Parallel genetic algorithms; Unequal area facility layout problem

Indexed keywords

HEURISTIC METHODS; PLANT LAYOUT;

FLEXIBLE BAY STRUCTURES; ISLAND MODEL; ISLAND MODEL GENETIC ALGORITHM; META-HEURISTIC APPROACH; PARALLEL GENETIC ALGORITHMS; POPULATION DIVERSITY; PRE-MATURE CONVERGENCES; UNEQUAL AREA FACILITY LAYOUT PROBLEMS;

GENETIC ALGORITHMS;

EID: 84992432051     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2016.10.004     Document Type: Article

References (92)

1. Abotaleb, I., Nassar, K., Hosny, O., Layout optimization of construction site facilities with dynamic freeform geometric representations. Automation in Construction 66 (2016), 15–28 http://dx.doi.org/10.1016/j.autcon.2016.02.007.
2. Achterberg, T., Conflict analysis in mixed integer programming. Discrete Optimization 4:1 (2007), 4–20 http://dx.doi.org/10.1016/j.disopt.2006.10.006.
3. Ahmadi, A., Jokar, M.R.A., An efficient multiple-stage mathematical programming method for advanced single and multi-floor facility layout problems. Applied Mathematical Modelling 40:9–10 (2016), 5605–5620 http://dx.doi.org/10.1016/j.apm.2016.01.014.
4. Aiello, G., Enea, M., Fuzzy approach to the robust facility layout in uncertain production environments. International Journal of Production Research 39:18 (2001), 4089–4101.
5. Aiello, G., Scalia, G.L., Enea, M., A multi objective genetic algorithm for the facility layout problem based upon slicing structure encoding. Expert Systems with Applications 39:12 (2012), 10352–10358.
6. Aiello, G., Scalia, G.L., Enea, M., A non dominated ranking multi objective genetic algorithm and electre method for unequal area facility layout problems. Expert Systems with Applications 40:12 (2013), 4812–4819 http://dx.doi.org/10.1016/j.eswa.2013.02.026.
7. Al-Hakim, L., On solving facility layout problems using genetic algorithms. International Journal of Production Research 38:11 (2000), 2573–2582, 10.1080/00207540050031922.
8. Alagoz, O., Norman, B.A., Smith, A.E., Determining aisle structures for facility designs using a hierarchy of algorithms. IIE Transactions 40:11 (2008), 1019–1031, 10.1080/07408170802167621.
9. Alba, E., Troya, J.M., A survey of parallel distributed genetic algorithms. Complex. 4:4 (1999), 31–52, 10.1002/(SICI)1099-0526(199903/04)4:4<31::AID-CPLX5>3.3.CO;2-W.
10. Albayrak, M., Allahverdi, N., Development a new mutation operator to solve the traveling salesman problem by aid of genetic algorithms. Expert Systems with Applications 38:3 (2011), 1313–1320 http://dx.doi.org/10.1016/j.eswa.2010.07.006.
11. Alves, D.T.S., de Medeiros, J.L., de Queiroz F. Arajo, O., Optimal determination of chemical plant layout via minimization of risk to general public using monte carlo and simulated annealing techniques. Journal of Loss Prevention in the Process Industries 41 (2016), 202–214 http://dx.doi.org/10.1016/j.jlp.2016.03.026.
12. Armour, G.C., Buffa, E.S., A heuristic algorithm and simulation approach to relative location of facilities. Management Science 9 (1963), 294–309.
13. Bazaraa, M.S., Computerized layout design: A branch and bound approach. A I I E Transactions 7:4 (1975), 432–438, 10.1080/05695557508975028.
14. Bozer, Y.A., Meller, R.D., A reexamination of the distance-based facility layout problem. IIE Transactions 29:7 (1997), 549–560, 10.1080/07408179708966365.
15. Bozer, Y.A., Wang, C.-T., A graph-pair representation and mip-model-based heuristic for the unequal-area facility layout problem. European Journal of Operational Research 218:2 (2012), 382–391 http://dx.doi.org/10.1016/j.ejor.2011.10.052.
16. van Camp, D.J., Carter, M.W., Vannelli, A., A nonlinear optimization approach for solving facility layout problems. European Journal of Operational Research 57:2 (1992), 174–189 Facility Layout. http://dx.doi.org/10.1016/0377-2217(92)90041-7.
17. Cantú-Paz, E., A survey of parallel genetic algorithms. Calculateurs Paralleles, 10, 1998.
18. Chen, Y., Jiang, Y., Wahab, M., Long, X., The facility layout problem in non-rectangular logistics parks with split lines. Expert Systems with Applications 42:21 (2015), 7768–7780 http://dx.doi.org/10.1016/j.eswa.2015.06.009.
19. Darwin, C., On the origin of the species by means of natural selection: Or, the preservation of favoured races in the struggle for life. 1859, John Murray.
20. Datta, D., Amaral, A.R., Figueira, J.R., Single row facility layout problem using a permutation-based genetic algorithm. European Journal of Operational Research 213:2 (2011), 388–394 http://dx.doi.org/10.1016/j.ejor.2011.03.034.
21. Dorigo, M., Stützle, T., Ant colony optimization. 2004, Bradford Company, Scituate, MA, USA.
22. Drira, A., Pierreval, H., Hajri-Gabouj, S., Facility layout problems: A survey. Annual Reviews in Control 31:2 (2007), 255–267.
23. Dunker, T., Radons, G., WestkÈñmper, E., Combining evolutionary computation and dynamic programming for solving a dynamic facility layout problem. European Journal of Operational Research 165:1 (2005), 55–69 http://dx.doi.org/10.1016/j.ejor.2003.01.002.
24. Ertay, T., Ruan, D., Tuzkaya, U.R., Integrating data envelopment analysis and analytic hierarchy for the facility layout design in manufacturing systems. Information Sciences 176:3 (2006), 237–262 http://dx.doi.org/10.1016/j.ins.2004.12.001.
25. García-Hernández, L., Arauzo-Azofra, A., Salas-Morera, L., Pierreval, H., Corchado, E., Recycling plants layout design by means of an interactive genetic algorithm. Intelligent Automation and Soft Computing 19:3 (2013), 457–468.
26. García-Hernández, L., Arauzo-Azofra, A., Salas-Morera, L., Pierreval, H., Corchado, E., Facility layout design using a multi-objective interactive genetic algorithm to support the dm. Expert Systems 32:1 (2015), 94–107, 10.1111/exsy.12064.
27. García-Hernández, L., Palomo-Romero, J.M., Salas-Morera, L., Arauzo-Azofra, A., Pierreval, H., A novel hybrid evolutionary approach for capturing decision maker knowledge into the unequal area facility layout problem. Expert Systems with Applications 42:10 (2015), 4697–4708 http://dx.doi.org/10.1016/j.eswa.2015.01.037.
28. García-Hernández, L., Pérez-Ortiz, M., Araúzo-Azofra, A., Salas-Morera, L., Hervás-Martínez, C., An evolutionary neural system for incorporating expert knowledge into the ua-flp. Neurocomputing 135 (2014), 69–78 http://dx.doi.org/10.1016/j.neucom.2013.01.068.
29. García-Hernández, L., Pierreval, H., Salas-Morera, L., Arauzo-Azofra, A., Handling qualitative aspects in unequal area facility layout problem: An interactive genetic algorithm. Applied Soft Computing 13:4 (2013), 1718–1727.
30. Goldberg, D.E., Genetic algorithms in search, optimization, and machine learning. 1989, Adisson-Wesley Professional.
31. Goldberg, D.E., Deb, K., A comparative analysis of selection schemes used in genetic algorithms. Foundations of genetic algorithms, 1991, Morgan Kaufmann, 69–93.
32. Gonçalves, J.F., Resende, M.G., A biased random-key genetic algorithm for the unequal area facility layout problem. European Journal of Operational Research 246:1 (2015), 86–107 http://dx.doi.org/10.1016/j.ejor.2015.04.029.
33. Guan, J., Lin, G., Hybridizing variable neighborhood search with ant colony optimization for solving the single row facility layout problem. European Journal of Operational Research 248:3 (2016), 899–909 http://dx.doi.org/10.1016/j.ejor.2015.08.014.
34. Heragu, S., Facilities design, third edition. 500 Tips, 2008, Taylor & Francis.
35. Holland, J.H., Adaptation in natural and artificial systems. 1992, MIT Press, Cambridge, MA, USA.
36. Ijjina, E.P., Chalavadi, K.M., Human action recognition using genetic algorithms and convolutional neural networks. Pattern Recognition 59 (2016), 199–212 http://dx.doi.org/10.1016/j.patcog.2016.01.012.
37. Ingber, L., Simulated annealing: Practice versus theory. Mathematical and Computer Modelling 18:11 (1993), 29–57 http://dx.doi.org/10.1016/0895-7177(93)90204-C.
38. Islier, A.A., A genetic algorithm approach for multiple criteria facility layout design. International Journal of Production Research 36:6 (1998), 1549–1569, 10.1080/002075498193165.
39. Kim, J.Y., Kim, Y.D., Graph theoretic heuristics for unequal-sized facility layout problems. Omega 23:4 (1995), 391–401.
40. Kochhar, J.S., Foster, B.T., Heragu, S.S., Hope: A genetic algorithm for the unequal area facility layout problem. Computers & Operations Research 25:7–8 (1998), 583–594 http://dx.doi.org/10.1016/S0305-0548(97)00100-7.
41. Komarudin, Wong, K.Y., Applying ant system for solving unequal area facility layout problems. European Journal of Operational Research 202:3 (2010), 730–746.
42. Konak, A., Kulturel-Konak, S., Norman, B., Smith, A., A new mixed integer programming formulation for facility layout design using flexible bays. Operations Research Letters 34 (2006), 660–672.
43. Koopmans, T., Beckmann, M.J., Assignment Problems and the Location of Economic Activities. Cowles Foundation Discussion Papers 4, 1955, Cowles Foundation for Research in Economics, Yale University.
44. Kulturel-Konak, S., A linear programming embedded probabilistic tabu search for the unequal-area facility layout problem with flexible bays. European Journal of Operational Research 223:3 (2012), 614–625 http://dx.doi.org/10.1016/j.ejor.2012.07.019.
45. Kulturel-Konak, S., Konak, A., A new relaxed flexible bay structure representation and particle swarm optimization for the unequal area facility layout problem. Engineering Optimization 43:12 (2011), 1263–1287, 10.1080/0305215X.2010.548864.
46. Kulturel-Konak, S., Konak, A., Unequal area flexible bay facility layout using ant colony optimisation. International Journal of Production Research 49:7 (2011), 1877–1902, 10.1080/00207541003614371.
47. Kundakc, N., Kulak, O., Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem. Computers & Industrial Engineering 96 (2016), 31–51.
48. Kurdi, M., An effective new island model genetic algorithm for job shop scheduling problem. Computers & Operations Research 67 (2016), 132–142 http://dx.doi.org/10.1016/j.cor.2015.10.005.
49. Kwong, S., Ng, A., Man, K., Improving local search in genetic algorithms for numerical global optimization using modified grid-point search technique. Genetic algorithms in engineering systems: Innovations and applications, 1995. galesia. first international conference on (conf. publ. no. 414), 1995, 419–423, 10.1049/cp:19951085.
50. Kker, R., A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization. Information Sciences 222:0 (2013), 528–543 http://dx.doi.org/10.1016/j.ins.2012.07.051.
51. Lee, Y.H., Lee, M.H., A shape-based block layout approach to facility layout problems using hybrid genetic algorithm. Computers & Industrial Engineering 42:2–4 (2002), 237–248 http://dx.doi.org/10.1016/S0360-8352(02)00018-9.
52. Li, X., Parrott, L., An improved genetic algorithm for spatial optimization of multi-objective and multi-site land use allocation. Computers, Environment and Urban Systems 59 (2016), 184–194 http://dx.doi.org/10.1016/j.compenvurbsys.2016.07.002.
53. Liu, Q., Meller, R.D., A sequence-pair representation and mip-model-based heuristic for the facility layout problem with rectangular departments. IIE Transactions 39:4 (2007), 377–394.
54. Mak, K., Wong, Y., Chan, F., A genetic algorithm for facility layout problems. Computer Integrated Manufacturing Systems 11:1–2 (1998), 113–127 http://dx.doi.org/10.1016/S0951-5240(98)00018-4.
55. Matai, R., Solving multi objective facility layout problem by modified simulated annealing. Applied Mathematics and Computation 261 (2015), 302–311 http://dx.doi.org/10.1016/j.amc.2015.03.107.
56. Meller, R., Gau, K., The facility layout problem: Recent and emerging trends and perspectives. Journal of Manufacturing Systems 15 (1996), 351–366.
57. Meller, R.D., Narayanan, V., Vance, P.H., Optimal facility layout design1. Operations Research Letters 23:3–5 (1998), 117–127 http://dx.doi.org/10.1016/S0167-6377(98)00024-8.
58. Miller, B.L., Goldberg, D.E., Genetic algorithms, selection schemes, and the varying effects of noise. Evolutionary Computation 4:2 (1996), 113–131, 10.1162/evco.1996.4.2.113.
59. Mhlenbein, H., Evolution in time and space - the parallel genetic algorithm. Foundations of genetic algorithms, 1991, Morgan Kaufmann, 316–337.
60. Paes, F.G., Pessoa, A.A., Vidal, T., A hybrid genetic algorithm with decomposition phases for the unequal area facility layout problem. European Journal of Operational Research, 2016 http://dx.doi.org/10.1016/j.ejor.2016.07.022.
61. Palubeckis, G., Fast local search for single row facility layout. European Journal of Operational Research 246:3 (2015), 800–814 http://dx.doi.org/10.1016/j.ejor.2015.05.055.
62. Palubeckis, G., Fast simulated annealing for single-row equidistant facility layout. Applied Mathematics and Computation 263 (2015), 287–301 http://dx.doi.org/10.1016/j.amc.2015.04.073.
63. Pereira, C.M., Sacco, W.F., A parallel genetic algorithm with niching technique applied to a nuclear reactor core design optimization problem. Progress in Nuclear Energy 50:7 (2008), 740–746 International Nuclear Atlantic Conference 2005. http://dx.doi.org/10.1016/j.pnucene.2007.12.007.
64. Pierreval, H., Caux, C., Paris, J., Viguier, F., Evolutionary approaches to the design and organization of manufacturing systems. Computers & Industrial Engineering 44:3 (2003), 339–364 http://dx.doi.org/10.1016/S0360-8352(02)00195-X.
65. Pourvaziri, H., Naderi, B., A hybrid multi-population genetic algorithm for the dynamic facility layout problem. Applied Soft Computing 24:0 (2014), 457–469 http://dx.doi.org/10.1016/j.asoc.2014.06.051.
66. Ripon, K.S.N., Glette, K., Khan, K.N., Hovin, M., Torresen, J., Adaptive variable neighborhood search for solving multi-objective facility layout problems with unequal area facilities. Swarm and Evolutionary Computation 8:0 (2013), 1–12 http://dx.doi.org/10.1016/j.swevo.2012.07.003.
67. Sadrzadeh, A., A genetic algorithm with the heuristic procedure to solve the multi-line layout problem. Computers and Industrial Engineering 62:4 (2012), 1055–1064, 10.1016/j.cie.2011.12.033.
68. Sahin, R., A simulated annealing algorithm for solving the bi-objective facility layout problem. Expert Systems with Applications 38:4 (2011), 4460–4465 http://dx.doi.org/10.1016/j.eswa.2010.09.117.
69. Scholz, D., Petrick, A., Domschke, W., Stats: A slicing tree and tabu search based heuristic for the unequal area facility layout problem. European Journal of Operational Research 197:1 (2009), 166–178.
70. Seppanen, J., Moore, J.M., Facilities planning with graph theory. Management Science 17:4 (1970), B242–B253.
71. Shin, H., Joo, C., Koo, J., Optimal rehabilitation model for water pipeline systems with genetic algorithm. Procedia Engineering 154 (2016), 384–390 12th International Conference on Hydroinformatics (HIC 2016) - Smart Water for the Future. http://dx.doi.org/10.1016/j.proeng.2016.07.497.
72. Sikaroudi, A.M.E., Shahanaghi, K., Facility layout by collision detection and force exertion heuristics. Journal of Manufacturing Systems 41 (2016), 21–30 http://dx.doi.org/10.1016/j.jmsy.2016.07.001.
73. Singh, S., Sharma, R., A review of different approaches to the facility layout problems. The International Journal of Advanced Manufacturing Technology 30 (2006), 425–433.
74. Sitkoff, N., Implementing a genetic algorithm on a parallel custom computing machine. Proceedings of the ieee symposium on FPGA's for custom computing machines FCCM ’95, 1995, IEEE Computer Society, Washington, DC, USA 180–.
75. Solar, M., Parada, V., Urrutia, R., A parallel genetic algorithm to solve the set-covering problem. Computers & Operations Research 29:9 (2002), 1221–1235 http://dx.doi.org/10.1016/S0305-0548(01)00026-0.
76. Solimanpur, M., Jafari, A., Optimal solution for the two-dimensional facility layout problem using a branch-and-bound algorithm. Computers and Industrial Engineering 55:3 (2008), 606–619, 10.1016/j.cie.2008.01.018.
77. Starkweather, T., Mcdaniel, S., Whitley, D., Mathias, K., Whitley, D., Dept, M.E., A comparison of genetic sequencing operators. Proceedings of the fourth international conference on genetic algorithms, 1991, Morgan Kaufmann, 69–76.
78. Starkweather, T., Whitley, D., Mathias, K., Optimization using distributed genetic algorithms. Schwefel, H.-P., MÈñnner, R., (eds.) Parallel problem solving from nature Lecture Notes in Computer Science, 496, 1991, Springer Berlin Heidelberg, 176–185, 10.1007/BFb0029750 http://dx.doi.org/10.1007/BFb0029750.
79. Tam, K.Y., Genetic algorithms, function optimization, and facility layout design. European Journal of Operational Research 63:2 (1992), 322–346 Strategic Planning of Facilities. http://dx.doi.org/10.1016/0377-2217(92)90034-7.
80. Tam, K.Y., Solving facility layout problems with geometric constraints using parallel genetic algorithms: Experimentation and findings. International Journal of Production Research 36:12 (1998), 3253–3272, 10.1080/002075498192058.
81. Tanese, R., Distributed genetic algorithms. Proceedings of the 3rd international conference on genetic algorithms, 1989, Morgan Kaufmann Publishers Inc, San Francisco, CA, USA, 434–439.
82. Tate, D.M., Smith, A.E., Unequal area facility layout using genetic search. IIE Transactions 27 (1995), 465–472.
83. Tompkins, J., White, J., Bozer, Y., Tanchoco, J., Facilities planning. 4th, 2010, Wiley, New York.
84. Tong, X., SECOT: A sequential construction technique for facility design. 1991, Doctoral Dissertation, University of Pittsburg.
85. Ulutas, B., Islier, A.A., Dynamic facility layout problem in footwear industry. Journal of Manufacturing Systems 36 (2015), 55–61 http://dx.doi.org/10.1016/j.jmsy.2015.03.004.
86. Ulutas, B.H., Kulturel-Konak, S., An artificial immune system based algorithm to solve unequal area facility layout problem. Expert Systems with Applications 39:5 (2012), 5384–5395 http://dx.doi.org/10.1016/j.eswa.2011.11.046.
87. Umarani, R., Selvi, V., Comparative analysis of ant colony and particle swarm optimization techniques. International Journal of Computer Applications 5:4 (2010), 1–6 Published By Foundation of Computer Science.
88. Vitayasak, S., Pongcharoen, P., Hicks, C., A tool for solving stochastic dynamic facility layout problems with stochastic demand using either a genetic algorithm or modified backtracking search algorithm. International Journal of Production Economics, 2016 http://dx.doi.org/10.1016/j.ijpe.2016.03.019.
89. Wang, M.-J., Hu, M.H., Ku, M.-Y., A solution to the unequal area facilities layout problem by genetic algorithm. Computers and Industrial 56:2 (2005), 207–220, 10.1016/j.compind.2004.06.003.
90. Wang, P., Kwok, D., Optimal design of {PID} process controllers based on genetic algorithms. Control Engineering Practice 2:4 (1994), 641–648 http://dx.doi.org/10.1016/0967-0661(94)90008-6.
91. Whitley, D., Rana, S., Heckendorn, R.B., The island model genetic algorithm: On separability, population size and convergence. Journal of Computing and Information Technology 7 (1998), 33–47.
92. Wong, K.-Y., Komarudin, Solving facility layout problems using flexible bay structure representation and ant system algorithm. Expert Systems with Applications 37:7 (2010), 5523–5527 http://dx.doi.org/10.1016/j.eswa.2009.12.080.