A review of meta-heuristic methods for solving location allocation financial problems
Subject Areas : Application of Game Theory in FinanceMehdi Fazli 1 , Somayyeh Faraji Amoogin 2
1 - Islamic Azad University, Ardabil Branch, Ardabil, Iran
2 - Department of Mathematics, Islamic Azad University, Ardabil Branch, Ardabil, Iran
Keywords:
Abstract :
[1] Drexl, M. and M. Schneider, A survey of variants and extensions of the location-routing problem. European Journal of Operational Research, doi.org/10.1016/j.ejor.2014.08.030
[2] Prodhon, C. and C. Prins, A survey of recent research on location-routing problems. European Journal of Operational Research, doi.org/10.1016/j.ejor.2014.01.005
[3] Nagy, G. and S. Salhi, Location-routing: Issues, models and methods. European Journal of Operational Research, doi.org/10.1016/j.ejor.2006.04.004
[4] Vallada, E., R. Ruiz, and G. Minella, Minimising total tardiness in the m-machine flowshop problem: A review and evaluation of heuristics and metaheuristics. Computers & Operations Research, doi.org/10.1016/j.cor.2006.08.016
[5] Cook, D.J., et al., The relation between systematic reviews and practice guidelines. Annals of internal medicine, doi.org/10.7326/0003-4819-127-3-199708010-00006
[6] Aznoli, F. and N.J. Navimipour, Cloud services recommendation: Reviewing the recent advances and suggesting the future research directions. Journal of Network and Computer Applications, doi.org/10.1016/j.jnca.2016.10.009
[7] Kitchenham, B., Procedures for performing systematic reviews. Keele, UK, Keele University, 2004. 33(2004): p. 1-26
.
[8] Navimipour, N.J. and Y. Charband, Knowledge sharing mechanisms and techniques in project teams: literature review, classification, and current trends. Computers in Human Behavior, doi.org/10.1016/j.chb.2016.05.003
[9] Charband, Y. and N.J. Navimipour, Online knowledge sharing mechanisms: a systematic review of the state of the art literature and recommendations for future research. Information Systems Frontiers, 2016. 6(18): p. 1131-1151.
[10] Kupiainen, E., M.V. Mäntylä, and J. Itkonen, Using metrics in Agile and Lean Software Development–A systematic literature review of industrial studies. Information and Software Technology, doi.org/10.1016/j.infsof.2015.02.005
[11] Soltani, Z. and N.J. Navimipour, Customer relationship management mechanisms: A systematic review of the state of the art literature and recommendations for future research. Computers in Human, doi.org/10.1016/j.chb.2016.03.008
[12] Aznoli, F. and N.J. Navimipour, Deployment Strategies in the Wireless Sensor Networks: Systematic Literature Review, Classification, and Current Trends. Wireless Personal Communications, 2016: p. 1-28.
[13] Kitchenham, B., et al., Systematic literature reviews in software engineering–a systematic literature review. Information and software technology, doi.org/10.1016/j.infsof.2008.09.009
[14] Biolchini, J., et al., Systematic review in software engineering. System Engineering and Computer Science Department COPPE/UFRJ, Technical Report ES, 2005. 679(05): p. 45.
[15] Sharma, K. and P. Mediratta, Importance of keywords for retrieval of relevant articles in medline search. Indian journal of pharmacology, 2002. 34(5): p. 369-371.
[16] Keele, S., Guidelines for performing systematic literature reviews in software engineering, in Technical report, Ver. 2.3 EBSE Technical Report. EBSE. 2007, sn.
[17] Gao, S., et al., Ant colony optimization with clustering for solving the dynamic location routing problem. Applied Mathematics and Computation, doi.org/10.1016/j.amc.2016.03.035
[18] Bouamama, S., C. Blum, and J.-G. Fages, An algorithm based on ant colony optimization for the minimum connected dominating set problem. Applied Soft Computing, doi.org/10.1016/j.asoc.2019.04.028
[19] Herazo-Padilla, N., et al. Coupling ant colony optimization and discrete-event simulation to solve a stochastic location-routing problem. in Proceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World, dOI: 10.1109/WSC.2013.6721699
[20] Liu, S., H. Leng, and L. Han, Pheromone Model Selection in Ant Colony Optimization for the Travelling Salesman Problem. Chinese Journal of Electronics, doi.org/10.1049/cje.2017.01.019
[21] Yang, L., X. Sun, and T. Chi. An ant colony optimization algorithm and multi-agent system combined method to solve Single Source Capacitated Facility Location Problem. in Advanced Computational Intelligence (ICACI), doi. 10.1109/ICACI.2013.6748482
[22] Küçükoğlu, İ., R. Dewil, and D. Cattrysse, Hybrid simulated annealing and tabu search method for the electric travelling salesman problem with time windows and mixed charging rates. Expert Systems with Applications, doi.org/10.1016/j.eswa.2019.05.037
[23] Santosa, B. and I.G.N.A. Kresna, Simulated Annealing to solve single stage capacitated warehouse location problem. Procedia Manufacturing, doi.org/10.1016/j.promfg.2015.11.015
[24] Ghorbani, A. and M.R.A. Jokar, A hybrid imperialist competitive-simulated annealing algorithm for a multisource multi-product location-routing-inventory problem. Computers & Industrial Engineering, doi.org/10.1016/j.cie.2016.08.027
[25] Vincent, F.Y. and S.-W. Lin, Multi-start simulated annealing heuristic for the location routing problem with simultaneous pickup and delivery. Applied Soft Computing, , doi.org/10.1016/j.asoc.2014.06.024
[26] Vincent, F.Y. and S.-Y. Lin, A simulated annealing heuristic for the open location-routing problem. Computers & Operations Research, doi.org/10.1016/j.cor.2014.10.009
[27] Rao, B.V. and G.N. Kumar, Sensitivity analysis based optimal location and tuning of static VAR compensator using firefly algorithm. Indian Journal of Science and Technology, 2014. 7(8): p. 1201-1210.
[28] Prima, P. and A.M. Arymurthy. Optimization of school location-allocation using Firefly Algorithm. in Journal of Physics: Conference Series. 2019, doi 10.1088/1742-6596/1235/1/012002
[29] Sulaiman, M.H., et al. Optimal allocation and sizing of distributed generation in distribution system via firefly algorithm. in Power Engineering and Optimization Conference (PEDCO) Melaka, Malaysia, 2012 Ieee International, doi 10.1109/PEOCO.2012.6230840
[30] Nadhir, K., D. Chabane, and B. Tarek, Distributed generation location and size determination to reduce power losses of a distribution feeder by Firefly Algorithm. International journal of advanced science and technology, 2013. 56: p. 61-72.
[31] Babaie-Kafaki, S., R. Ghanbari, and N. Mahdavi-Amiri, Hybridizations of genetic algorithms and neighborhood search metaheuristics for fuzzy bus terminal location problems. Applied Soft Computing, doi.org/10.1016/j.asoc.2016.03.005
[32] Hiassat, A., A. Diabat, and I. Rahwan, A genetic algorithm approach for location-inventory-routing problem with perishable products. Journal of Manufacturing Systems, doi.org/10.1016/j.jmsy.2016.10.004
[33] Saif-Eddine, A.S., M.M. El-Beheiry, and A.K. El-Kharbotly, An improved genetic algorithm for optimizing total supply chain cost in inventory location routing problem. Ain Shams Engineering Journal, doi.org/10.1016/j.asej.2018.09.002
[34] Rybičková, A., A. Burketová, and D. Mocková. Solution to the location-routing problem using a genetic algorithm. in Smart Cities Symposium Prague (SCSP), doi: 10.1109/SCSP.2016.7501016
[35] Crossland, A., D. Jones, and N. Wade, Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing. International Journal of Electrical Power & Energy Systems, doi.org/10.1016/j.ijepes.2014.02.001
[36] Zhang, Z., et al., A fast two-stage hybrid meta-heuristic algorithm for robust corridor allocation problem. Advanced Engineering Informatics, doi.org/10.1016/j.aei.2022.101700
[37] Lv, C., et al., A fuzzy correlation based heuristic for Dual-mode integrated Location routing problem. Computers & Operations Research, doi.org/10.1016/j.cor.2022.105923
[38] Mokhtarzadeh, M., et al., A hybrid of clustering and meta-heuristic algorithms to solve a p-mobile hub location–allocation problem with the depreciation cost of hub facilities. Engineering Applications of Artificial Intelligence, doi.org/10.1016/j.engappai.2020.104121
[39] Fazli, M., F.M.Khiabani and B. Daneshian, Hybrid whale and genetic algorithms with fuzzy values to solve the location problem mmep, 763-768
[40] Saffari, A., S.H. Zahiri, and M. Khishe, Fuzzy whale optimisation algorithm: a new hybrid approach for automatic sonar target recognition. Journal of Experimental & Theoretical Artificial Intelligence, doi.org/10.1080/0952813X.2021.1960639
[41] Saif-Eddine, A. S., El-Beheiry, M. M., & El-Kharbotly, A. K. (2019). An improved genetic algorithm for optimizing total supply chain cost in inventory location routing problem. Ain Shams Engineering Journal, doi.org/10.1016/j.asej.2018.09.002
[42] Li, X., et al., An Iterated Tabu Search Metaheuristic for the Regenerator Location Problem. Applied Soft Computing, doi.org/10.1016/j.asoc.2018.05.019
[43] Díaz, J.A., et al., GRASP and hybrid GRASP-Tabu heuristics to solve a maximal covering location problem with customer preference ordering. Expert Systems with Applications, doi.org/10.1016/j.eswa.2017.04.002
[44] Ho, S.C., An iterated tabu search heuristic for the single source capacitated facility location problem. Applied Soft Computing, doi.org/10.1016/j.asoc.2014.11.004
[45] Lai, D.S., O.C. Demirag, and J.M. Leung, A tabu search heuristic for the heterogeneous vehicle routing problem on a multigraph. Transportation Research Part E: Logistics and Transportation Review, doi.org/10.1016/j.tre.2015.12.001
[46] Silvestrin, P.V. and M. Ritt, An iterated tabu search for the multi-compartment vehicle routing problem. Computers & Operations Research, doi.org/10.1016/j.cor.2016.12.023
[47] Sun, M., Solving the uncapacitated facility location problem using tabu search. Computers & Operations Research, doi.org/10.1016/j.cor.2005.07.014
[48] Polak, I. and M. Boryczka, Tabu Search in revealing the internal state of RC4+ cipher. Applied Soft Computing, doi.org/10.1016/j.asoc.2019.01.039
[49] Nguyen, V.-P., C. Prins, and C. Prodhon, A multi-start iterated local search with tabu list and path relinking for the two-echelon location-routing problem. Engineering Applications of Artificial Intelligence, doi.org/10.1016/j.engappai.2011.09.012
[50] Xie, J., et al. A restricted neighbourhood tabu search for storage location assignment problem. in Evolutionary Computation (CEC), 2015 IEEE Congress on, doi: 10.1109/CEC.2015.7257237
[51] Ge, F., et al. Chaotic ant swarm for graph coloring. in Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on. 2010, doi. 10.1109/ICICISYS.2010.5658530
[52] Brock, T.C., et al., The Consumer Reports study of psychotherapy: Invalid is invalid. 1996. doi.org/10.1037/0003-066X.51.10.1083
[53] Abbasian, R., M. Mouhoub, and A. Jula, Solving Graph Coloring Problems Using Cultural Algorithms. FLAIRS Conference, 2011.
[54] Dorrigiv, M. and H.Y. Markib. Algorithms for the graph coloring problem based on swarm intelligence. in Artificial Intelligence and Signal Processing (AISP), 2012 16th CSI International Symposium on, doi.10.1109/AISP.2012.6313794
[55] Yang, X.-S. Firefly algorithms for multimodal optimization. in International symposium on stochastic algorithms, doi.org/10.1007/978-3-642-04944-6_14
[56] Ellis, R. and M. Petridis, Research and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems XVII. 2009: Springer Science & Business Media.
[57] Bramer, M. and M. Petridis, Research and Development in Intelligent Systems XXIX: Incorporating Applications and Innovations in Intelligent Systems XX Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. 2012: Springer Science & Business Media.
[58] Schorle, H., et al., Transcription factor AP-2 essential for cranial closure and craniofacial development. Nature, 1996. 381(6579): p. 235.
[59] Goldberg, D., Genetic algorithms in optimization, search and machine learning. Reading: Addison-Wesley, 1989.
[60] Goldberg, D.E., Genetic algorithms in search, optimization, and machine learning, 1989. Reading: Addison-Wesley, 1989.
[61] Lim, D., et al., Efficient hierarchical parallel genetic algorithms using grid computing. Future Generation Computer Systems, doi.org/10.1016/j.future.2006.10.008
[62] Cui, J., T.C. Fogarty, and J.G. Gammack, Searching databases using parallel genetic algorithms on a transputer computing surface. Future Generation Computer Systems, doi.org/10.1016/0167-739X(93)90024-J
[63] Sena, G.A., D. Megherbi, and G. Isern, Implementation of a parallel genetic algorithm on a cluster of workstations: traveling salesman problem, a case study. Future Generation Computer Systems, doi.org/10.1016/S0167-739X(99)00134
[64] Liu, Z., et al., Evolving neural network using real coded genetic algorithm (GA) for multispectral image classification. Future Generation Computer Systems, doi.org/10.1016/j.future.2003.11.024
[65] Glover, F., Tabu search—part I. ORSA Journal on computing, 1989. 1(3): p. 190-206.
[66] Khachaturyan, A., S. Semenovsovskaya, and B. Vainshtein, The thermodynamic approach to the structure analysis of crystals. Acta Crystallographica Section A: Crystal Physics, Diffraction, Theoretical and General Crystallography, doi.org/10.1107/S0567739481001630
[67] Glover, F., Future paths for integer programming and links to artificial intelligence. Computers & operations research, doi.org/10.1016/0305-0548(86)90048-1
[68] Glover, F., Artificial intelligence, heuristic frameworks and tabu search. Managerial and Decision Economics, doi.org/10.1002/mde.4090110512
[69] Glover, F., Tabu search—part II. ORSA Journal on computing, 1990. 2(1): p. 4-32.
[70] Jin, Y., J.-K. Hao, and J.-P. Hamiez, A memetic algorithm for the minimum sum coloring problem. Computers & Operations Research, doi.org/10.1016/j.cor.2013.09.019
[71] Amaya, J.E., C.C. Porras, and A.J.F. Leiva, Memetic and hybrid evolutionary algorithms, in Springer Handbook of Computational Intelligence. 2015, Springer. p. 1047-1060.
[72]Neri, F., C. Cotta, and P. Moscato, Handbook of Memetic Algorithms, Vol. 379 of Studies in Computational Intelligence. 2011, Springer.
[73] Galinier, P. and J.-K. Hao, Hybrid evolutionary algorithms for graph coloring. Journal of combinatorial optimization, 1999. 3(4): p. 379-397.
[74] Lü, Z. and J.-K. Hao, A memetic algorithm for graph coloring. European Journal of Operational Research, doi.org/10.1016/j.ejor.2009.07.016
[75]Malaguti, E., M. Monaci, and P. Toth, A metaheuristic approach for the vertex coloring problem. 77. Porumbel, D.C., J.-K. Hao, and P. Kuntz, An evolutionary approach with diversity guarantee and well-informed grouping recombination for graph coloring. Computers & Operations Research, 2010. 37(10): p. 1822-1832.
[76] Shi, Y. and R.C. Eberhart. Empirical study of particle swarm optimization. in Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, doi: 10.1109/CEC.1999.785511
[77] Shi, Y. and R. Eberhart. A modified particle swarm optimizer. in Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, doi: 10.1109/ICEC.1998.699146
[78] Kennedy, J. The particle swarm: social adaptation of knowledge. in Evolutionary Computation, 1997., IEEE International Conference on, doi.10.1109/ICEC.1997.592326
[79] Poli, R., Analysis of the publications on the applications of particle swarm optimisation. Journal of Artificial Evolution and Applications, doi:10.1155/2008/685175
[80] Poli, R., An analysis of publications on particle swarm optimization applications. Essex, UK: Department of Computer Science, University of Essex, 2007.
[81] Bonyadi, M.R., et al., Particle swarm optimization for single objective continuous space problems: a review. Evolutionary Computation, doi: 10.1162/EVCO_r_00180