A Novel Pareto-Based Meta-Heuristic Algorithm to Optimize Multi-Facility Location-Allocation Problem
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.
<p>[1] Current, J., Daskin, M., Schilling, D. "Discrete network location
models," in: Z. Drezner, H.W. Hamacher (Eds.), Facility Location:
Applications and Theory, Springer, Heidelberg, (2002).
[2] Francis, R. L., Megginis, L. F., White, J. A. "Facility layout and
location: An analytical approach" (2nd ed.). Englewood Cliffs, NJ:
Prentice-Hall, (1992).
[3] Marianov, V., ReVelle, C. "Siting emergency services in Facility
Location: A Survey of Applications and Methods." Springer Series in
Operations Research, (1995).
[4] Boffey, B., Galvao, R., and Espejo, L. "A review of congestion models
in the location of facilities with immobile servers." European Journal of
Operational Research (2007); 178: 643–662.
[5] Cooper, R.B. “Introduction to queuing theory”, 2nd Edition. New York:
Elsevier North Holland, (1980).
[6] Porter, A., Roper, A. Mason, T., Rossini, F., & Banks, J. “Forecasting
and Management of Technology”. Wiley, New York, (1991).
[7] Shavandi, H., Mahlooji, H. "A fuzzy queuing location model with
congested systems; A genetic algorithm." Applied Mathematics and
Computation 2006; 181: 440 -456.
[8] Wang Q, Batta R, Rump C. "Algorithms for a facility location problem
with stochastic customer demand and immobile servers." Annals of
Operations Research 2002; 111:17–34.
[9] Berman O, Drezner Z. "The multiple server location problem." Journal
of the Operational Research Society 2007; 58: 91–9.
[10] Berman O, Krass D, Wang J. "Locating service facilities to reduce lost
demand." IIE Transactions 2006; 38: 933–46.
[11] Pasandideh, S.H.R., Niaki, S.D.A. "Genetic application in a facility
location problem with random demand within queuing framework."
Journal of Intelligent Manufacturing 2010: 21: 234-546.
[12] Hajipour, V., Pasandideh, S.H.R., “A New Multi Objective Model for
Location-Allocation Problem within Queuing Framework”, World
Academy of Science, Engineering and Technology, 78, International
Conference on Industrial and Mechanical Engineering, Amesterdam
2011, 1665-1673.
[13] Pasandideh, S.H.R, Niaki, S.T.A., Hajipour, V., “A Multi-objective
Facility Location Model with Batch Arrivals: Two Parametric-Tunic
Meta-heuristic Algorithms”, Journal of Intelligence and Manufacturing,
DOI 10.1007/s10845-011-0592-7.
[14] L. J. Fogel, A.J. Owens, M.J. Walsh, Artificial Intelligence through
Simulated Evolution, John Wiley, Chichester, UK, 1966.
[15] J. R. Koza, Genetic programming: a paradigm for genetically breeding
populations of computer programs to solve problems, Rep. No. STANCS-
90-1314, Stanford University, CA, 1990.
[16] J. H. Holland, Adaptation in Natural and Artificial Systems, University
of Michigan Press, Ann Arbor, MI, 1975.
[17] D. E. Goldberg, Genetic Algorithms in Search, Optimization and
Machine Learning, Addison Wesley, Boston, MA, 1989.
[18] R. Poli, J. Kennedy, T. Blackwell, Particle swarm optimization An
overview, Swarm Intell, 1, 33–57, 2007.
[19] R. Oftadeh, M. J. Mahjoob, M. Shariatpanahi, A novel meta-heuristic
optimization algorithm inspired by group hunting of animals: Hunting
search, Computers and Mathematics with Applications 60 (2010) 2087–
2098.
[20] S. Kirkpatrick, C. Gelatt, M. Vecchi, Optimization by simulated
annealing, Science 220 (4598) (1983) 671–680.
[21] Z.W. Geem, J.H. Kim, G.V. Loganathan, A new heuristic optimization
algorithm: harmony search, Simulation 76 (2) (2001) 60–68.
[22] Al Jaddan O, Rajamani L, Rao CR. Nondominated ranked genetic
algorithm for solving constrained multi-objective optimization problems.
Journal of Theoretical and Applied Information Technology 2009; 5:
640-651.
[23] Deb, K. "Multi-objective optimization using evolutionary algorithms."
Chichester, UK: Wiley (2001).
[24] Radcliffe, N. J. "Forma analysis and random respectful recombination."
In Proceedings of the fourth international conference on genetic
algorithms (pp. 222–229). Morgan Kaufmann, San Mateo, CA (1991).
[25] E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: improving the strength
Pareto Evolutionary Algorithm. Evolutionary Methods for Design,
Optimization and Control with Applications to industrial Problems,
Greece, 2001, pp. 95-100.</p>
<p>[1] Current, J., Daskin, M., Schilling, D. "Discrete network location
models," in: Z. Drezner, H.W. Hamacher (Eds.), Facility Location:
Applications and Theory, Springer, Heidelberg, (2002).
[2] Francis, R. L., Megginis, L. F., White, J. A. "Facility layout and
location: An analytical approach" (2nd ed.). Englewood Cliffs, NJ:
Prentice-Hall, (1992).
[3] Marianov, V., ReVelle, C. "Siting emergency services in Facility
Location: A Survey of Applications and Methods." Springer Series in
Operations Research, (1995).
[4] Boffey, B., Galvao, R., and Espejo, L. "A review of congestion models
in the location of facilities with immobile servers." European Journal of
Operational Research (2007); 178: 643–662.
[5] Cooper, R.B. “Introduction to queuing theory”, 2nd Edition. New York:
Elsevier North Holland, (1980).
[6] Porter, A., Roper, A. Mason, T., Rossini, F., & Banks, J. “Forecasting
and Management of Technology”. Wiley, New York, (1991).
[7] Shavandi, H., Mahlooji, H. "A fuzzy queuing location model with
congested systems; A genetic algorithm." Applied Mathematics and
Computation 2006; 181: 440 -456.
[8] Wang Q, Batta R, Rump C. "Algorithms for a facility location problem
with stochastic customer demand and immobile servers." Annals of
Operations Research 2002; 111:17–34.
[9] Berman O, Drezner Z. "The multiple server location problem." Journal
of the Operational Research Society 2007; 58: 91–9.
[10] Berman O, Krass D, Wang J. "Locating service facilities to reduce lost
demand." IIE Transactions 2006; 38: 933–46.
[11] Pasandideh, S.H.R., Niaki, S.D.A. "Genetic application in a facility
location problem with random demand within queuing framework."
Journal of Intelligent Manufacturing 2010: 21: 234-546.
[12] Hajipour, V., Pasandideh, S.H.R., “A New Multi Objective Model for
Location-Allocation Problem within Queuing Framework”, World
Academy of Science, Engineering and Technology, 78, International
Conference on Industrial and Mechanical Engineering, Amesterdam
2011, 1665-1673.
[13] Pasandideh, S.H.R, Niaki, S.T.A., Hajipour, V., “A Multi-objective
Facility Location Model with Batch Arrivals: Two Parametric-Tunic
Meta-heuristic Algorithms”, Journal of Intelligence and Manufacturing,
DOI 10.1007/s10845-011-0592-7.
[14] L. J. Fogel, A.J. Owens, M.J. Walsh, Artificial Intelligence through
Simulated Evolution, John Wiley, Chichester, UK, 1966.
[15] J. R. Koza, Genetic programming: a paradigm for genetically breeding
populations of computer programs to solve problems, Rep. No. STANCS-
90-1314, Stanford University, CA, 1990.
[16] J. H. Holland, Adaptation in Natural and Artificial Systems, University
of Michigan Press, Ann Arbor, MI, 1975.
[17] D. E. Goldberg, Genetic Algorithms in Search, Optimization and
Machine Learning, Addison Wesley, Boston, MA, 1989.
[18] R. Poli, J. Kennedy, T. Blackwell, Particle swarm optimization An
overview, Swarm Intell, 1, 33–57, 2007.
[19] R. Oftadeh, M. J. Mahjoob, M. Shariatpanahi, A novel meta-heuristic
optimization algorithm inspired by group hunting of animals: Hunting
search, Computers and Mathematics with Applications 60 (2010) 2087–
2098.
[20] S. Kirkpatrick, C. Gelatt, M. Vecchi, Optimization by simulated
annealing, Science 220 (4598) (1983) 671–680.
[21] Z.W. Geem, J.H. Kim, G.V. Loganathan, A new heuristic optimization
algorithm: harmony search, Simulation 76 (2) (2001) 60–68.
[22] Al Jaddan O, Rajamani L, Rao CR. Nondominated ranked genetic
algorithm for solving constrained multi-objective optimization problems.
Journal of Theoretical and Applied Information Technology 2009; 5:
640-651.
[23] Deb, K. "Multi-objective optimization using evolutionary algorithms."
Chichester, UK: Wiley (2001).
[24] Radcliffe, N. J. "Forma analysis and random respectful recombination."
In Proceedings of the fourth international conference on genetic
algorithms (pp. 222–229). Morgan Kaufmann, San Mateo, CA (1991).
[25] E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: improving the strength
Pareto Evolutionary Algorithm. Evolutionary Methods for Design,
Optimization and Control with Applications to industrial Problems,
Greece, 2001, pp. 95-100.</p>
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:54522", author = "Vahid Hajipour and Samira V. Noshafagh and Reza Tavakkoli-Moghaddam", title = "A Novel Pareto-Based Meta-Heuristic Algorithm to Optimize Multi-Facility Location-Allocation Problem", abstract = "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.
", keywords = "Non-dominated ranking genetic algorithm, Pareto
solutions, Multi-facility location-allocation problem.", volume = "7", number = "7", pages = "1483-4", }
