A review of meta-heuristic methods for solving location allocation financial problems

محورهای موضوعی : Application of Game Theory in Finance

Mehdi 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

تاریخ دریافت : 1401/07/17 تاریخ پذیرش : 1402/04/08 تاریخ انتشار : 1402/04/10

کلید واژه: Location routing, Financial problems, meta-heuristic, Hybridization,

چکیده مقاله :

In this article, we will examine the financial issues related to multi-period routing and positioning and the related costs, and we will examine the related limitations. These decisions are made about location allocation, inventory and routing in a three-tier supply chain, including suppliers, warehouses and customers. We are looking for new ways to make location and routing decisions simultaneously and efficiently. In order to maximize the search space and achieve optimal results, exploratory and meta-heuristic methods have been used. The meta-heuristic technique is usually used to increase the performance of the hybrid technique. Therefore, this paper provides an overview of meta-heuristic methods and their combination to solve problems. It also examines the advantages and disadvantages of the proposed methods to solve these problems in order to provide more efficient methods.

منابع و مأخذ:

[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