Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Article . 2013
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Article . 2013
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Article . 2013
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

A Novel Pareto-Based Meta-Heuristic Algorithm To Optimize Multi-Facility Location-Allocation Problem

Authors: Hajipour, Vahid; Noshafagh, Samira V.; Tavakkoli-Moghaddam, Reza;

A Novel Pareto-Based Meta-Heuristic Algorithm To Optimize Multi-Facility Location-Allocation Problem

Abstract

{"references": ["", "Current, J., Daskin, M., Schilling, D. \"Discrete network location\nmodels,\" in: Z. Drezner, H.W. Hamacher (Eds.), Facility Location:\nApplications and Theory, Springer, Heidelberg, (2002).", "Francis, R. L., Megginis, L. F., White, J. A. \"Facility layout and\nlocation: An analytical approach\" (2nd ed.). Englewood Cliffs, NJ:\nPrentice-Hall, (1992).", "Marianov, V., ReVelle, C. \"Siting emergency services in Facility\nLocation: A Survey of Applications and Methods.\" Springer Series in\nOperations Research, (1995).", "Boffey, B., Galvao, R., and Espejo, L. \"A review of congestion models\nin the location of facilities with immobile servers.\" European Journal of\nOperational Research (2007); 178: 643\u2013662.", "Cooper, R.B. \"Introduction to queuing theory\", 2nd Edition. New York:\nElsevier North Holland, (1980).", "Porter, A., Roper, A. Mason, T., Rossini, F., & Banks, J. \"Forecasting\nand Management of Technology\". Wiley, New York, (1991).", "Shavandi, H., Mahlooji, H. \"A fuzzy queuing location model with\ncongested systems; A genetic algorithm.\" Applied Mathematics and\nComputation 2006; 181: 440 -456.", "Wang Q, Batta R, Rump C. \"Algorithms for a facility location problem\nwith stochastic customer demand and immobile servers.\" Annals of\nOperations Research 2002; 111:17\u201334.", "Berman O, Drezner Z. \"The multiple server location problem.\" Journal\nof the Operational Research Society 2007; 58: 91\u20139.\n[10] Berman O, Krass D, Wang J. \"Locating service facilities to reduce lost\ndemand.\" IIE Transactions 2006; 38: 933\u201346.\n[11] Pasandideh, S.H.R., Niaki, S.D.A. \"Genetic application in a facility\nlocation problem with random demand within queuing framework.\"\nJournal of Intelligent Manufacturing 2010: 21: 234-546.\n[12] Hajipour, V., Pasandideh, S.H.R., \"A New Multi Objective Model for\nLocation-Allocation Problem within Queuing Framework\", World\nAcademy of Science, Engineering and Technology, 78, International\nConference on Industrial and Mechanical Engineering, Amesterdam\n2011, 1665-1673.\n[13] Pasandideh, S.H.R, Niaki, S.T.A., Hajipour, V., \"A Multi-objective\nFacility Location Model with Batch Arrivals: Two Parametric-Tunic\nMeta-heuristic Algorithms\", Journal of Intelligence and Manufacturing,\nDOI 10.1007/s10845-011-0592-7.\n[14] L. J. Fogel, A.J. Owens, M.J. Walsh, Artificial Intelligence through\nSimulated Evolution, John Wiley, Chichester, UK, 1966.\n[15] J. R. Koza, Genetic programming: a paradigm for genetically breeding\npopulations of computer programs to solve problems, Rep. No. STANCS-\n90-1314, Stanford University, CA, 1990.\n[16] J. H. Holland, Adaptation in Natural and Artificial Systems, University\nof Michigan Press, Ann Arbor, MI, 1975.\n[17] D. E. Goldberg, Genetic Algorithms in Search, Optimization and\nMachine Learning, Addison Wesley, Boston, MA, 1989.\n[18] R. Poli, J. Kennedy, T. Blackwell, Particle swarm optimization An\noverview, Swarm Intell, 1, 33\u201357, 2007.\n[19] R. Oftadeh, M. J. Mahjoob, M. Shariatpanahi, A novel meta-heuristic\noptimization algorithm inspired by group hunting of animals: Hunting\nsearch, Computers and Mathematics with Applications 60 (2010) 2087\u2013\n2098.\n[20] S. Kirkpatrick, C. Gelatt, M. Vecchi, Optimization by simulated\nannealing, Science 220 (4598) (1983) 671\u2013680.\n[21] Z.W. Geem, J.H. Kim, G.V. Loganathan, A new heuristic optimization\nalgorithm: harmony search, Simulation 76 (2) (2001) 60\u201368.\n[22] Al Jaddan O, Rajamani L, Rao CR. Nondominated ranked genetic\nalgorithm for solving constrained multi-objective optimization problems.\nJournal of Theoretical and Applied Information Technology 2009; 5:\n640-651.\n[23] Deb, K. \"Multi-objective optimization using evolutionary algorithms.\"\nChichester, UK: Wiley (2001).\n[24] Radcliffe, N. J. \"Forma analysis and random respectful recombination.\"\nIn Proceedings of the fourth international conference on genetic\nalgorithms (pp. 222\u2013229). Morgan Kaufmann, San Mateo, CA (1991).\n[25] E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: improving the strength\nPareto Evolutionary Algorithm. Evolutionary Methods for Design,\nOptimization and Control with Applications to industrial Problems,\nGreece, 2001, pp. 95-100."]}

This article proposes a novel Pareto-based multiobjective meta-heuristic algorithm named non-dominated ranking genetic algorithm (NRGA) to solve multi-facility location-allocation problem. In NRGA, a fitness value representing rank is assigned to each individual of the population. Moreover, two features ranked based roulette wheel selection including select the fronts and choose solutions from the fronts, are utilized. The proposed solving methodology is validated using several examples taken from the specialized literature. The performance of our approach shows that NRGA algorithm is able to generate true and well distributed Pareto optimal solutions.

Related Organizations
Keywords

Non-dominated ranking genetic algorithm, Pareto solutions, Multi-facility location-allocation problem.

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
    OpenAIRE UsageCounts
    Usage byUsageCounts
    visibility views 2
    download downloads 6
  • 2
    views
    6
    downloads
    Powered byOpenAIRE UsageCounts
Powered by OpenAIRE graph
Found an issue? Give us feedback
visibility
download
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
0
Average
Average
Average
2
6
Green