A survey of meta-heuristic methods for optimization problems

Subject Areas : Evolutionary computing

Mehdi Fazli ¹ , somayyeh faraji ²

1 -
2 - 1Department of Mathematics, Islamic Azad University, Tabriz Branch, Tabriz, Iran

Received: 2023-09-06 Accepted : 2023-09-17 Published : 2023-10-10

Keywords: Operations research, Meta-heuristic, Hybridization, Memetic algorithm,

Abstract :

In this article, we will examine the problems related to routing and positioning with real variables and examine the related questions. These engineering, inventory and optimization decisions are made in a multi-layered supply chain system, including suppliers, warehouses and different buyers. We are looking for new ways to manage location and routing efficiently and effectively. In order to increase efficiency and achieve optimal results, exploratory and meta-heuristic methods have been used. In meta-heuristic techniques, a combination technique is usually used to increase performance. Therefore, this review article examines meta-heuristic methods and analysis of location problems using different quantities. It also examines the advantages and disadvantages of each method to optimally solve these problems in order to introduce practical and efficient methods.

References:


Amaya, J. E., Porras, C. C., & Leiva, A. J. F. (2015). Memetic and hybrid evolutionary algorithms. In Springer Handbook of Computational Intelligence (pp. 1047-1060): Springer.
Aznoli, F., & Navimipour, N. J. (2016). Deployment Strategies in the Wireless Sensor Networks: Systematic Literature Review, Classification, and Current Trends. Wireless Personal Communications, 1-28.
Aznoli, F., & Navimipour, N. J. (2017). Cloud services recommendation: Reviewing the recent advances and suggesting the future research directions. Journal of Network and Computer Applications, 77, 73-86.
Babaie-Kafaki, S., Ghanbari, R., & Mahdavi-Amiri, N. (2016). Hybridizations of genetic algorithms and neighborhood search metaheuristics for fuzzy bus terminal location problems. Applied Soft Computing, 46, 220-229.
Charband, Y., & Navimipour, N. J. (2016). Online knowledge sharing mechanisms: a systematic review of the state of the art literature and recommendations for future research. Information Systems Frontiers, 6(18), 1131-1151.
Crossland, A., Jones, D., & Wade, N. (2014). 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, 59, 103-110.
Drexl, M., & Schneider, M. (2015). A survey of variants and extensions of the location-routing problem. European Journal of Operational Research, 241(2), 283-308.
Ellis, R., & Petridis, M. (2009). Research and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems XVII: Springer Science & Business Media.
Fazli, M., F.M.Khiabani and B. Daneshian, Hybrid whale and genetic algorithms with fuzzy values to solve the location problem mmep, 763-768
Ghorbani, A., & Jokar, M. R. A. (2016). A hybrid imperialist competitive-simulated annealing algorithm for a multisource multi-product location-routing-inventory problem. Computers & Industrial Engineering, 101, 116-127.
Hiassat, A., Diabat, A., & Rahwan, I. (2017). A genetic algorithm approach for location-inventory-routing problem with perishable products. Journal of Manufacturing Systems, 42, 93-103.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004), 1-26.
Küçükoğlu, İ., Dewil, R., & Cattrysse, D. (2019). Hybrid simulated annealing and tabu search method for the electric travelling salesman problem with time windows and mixed charging rates. Expert Systems with Applications, 134, 279-303.
Kupiainen, E., Mäntylä, M. V., & Itkonen, J. (2015). Using metrics in Agile and Lean Software Development–A systematic literature review of industrial studies. Information and Software Technology, 62, 143-163.
Lai, D. S., Demirag, O. C., & Leung, J. M. (2016). A tabu search heuristic for the heterogeneous vehicle routing problem on a multigraph. Transportation Research Part E: Logistics and Transportation Review, 86, 32-52.
Li, X., Yue, C., Aneja, Y. P., Chen, S., & Cui, Y. (2018). An Iterated Tabu Search Metaheuristic for the Regenerator Location Problem. Applied Soft Computing, 70, 182-194.
Liu, S., Leng, H., & Han, L. (2017). Pheromone Model Selection in Ant Colony Optimization for the Travelling Salesman Problem. Chinese Journal of Electronics, 26(2), 223-229.
Lv, C., Zhang, C., Ren, Y., & Meng, L. (2022). A fuzzy correlation based heuristic for Dual-mode integrated Location routing problem. Computers & Operations Research, 146, 105923.
Mokhtarzadeh, M., Tavakkoli-Moghaddam, R., Triki, C., & Rahimi, Y. (2021). 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, 98, 104121.
Nagy, G., & Salhi, S. (2007). Location-routing: Issues, models and methods. European Journal of Operational Research, 177(2), 649-672.
Navimipour, N. J., & Charband, Y. (2016). Knowledge sharing mechanisms and techniques in project teams: literature review, classification, and current trends. Computers in Human Behavior, 62, 730-742.
Peng, Y., & Chen, Z.-X. (2009). Two-phase particle swarm optimization for multi-depot location-routing problem. Paper presented at the New Trends in Information and Service Science, 2009. NISS'09. International Conference on.
Peng, Y., Cheng, J.-f., & Jiang, R.-x. (2019). Inversion of UEP signatures induced by ships based on PSO method. Defence Technology.
Perl, J., & Daskin, M. S. (1985). A warehouse location-routing problem. Transportation Research Part B: Methodological, 19(5), 381-396.
Polak, I., & Boryczka, M. (2019). Tabu Search in revealing the internal state of RC4+ cipher. Applied Soft Computing, 77, 509-519.
Prima, P., & Arymurthy, A. M. (2019). Optimization of school location-allocation using Firefly Algorithm. Paper presented at the Journal of Physics: Conference Series.
Prodhon, C., & Prins, C. (2014). A survey of recent research on location-routing problems. European Journal of Operational Research, 238(1), 1-17.
Rabie, H. M., El-Khodary, I. A., & Tharwat, A. A. (2014). Particle Swarm Optimization algorithm for the continuous p-median location problems. Paper presented at the Computer Engineering Conference (ICENCO), 2014 10th International.
Rao, B. V., & Kumar, G. N. (2014). Sensitivity analysis based optimal location and tuning of static VAR compensator using firefly algorithm. Indian Journal of Science and Technology, 7(8), 1201-1210.
Rybičková, A., Burketová, A., & Mocková, D. (2016). Solution to the location-routing problem using a genetic algorithm. Paper presented at the Smart Cities Symposium Prague (SCSP), 2016.
Saffari, A., Zahiri, S. H., & Khishe, M. (2022). Fuzzy whale optimisation algorithm: a new hybrid approach for automatic sonar target recognition. Journal of Experimental & Theoretical Artificial Intelligence, 1-17.
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, 10(1), 63-76.
Salhi, S., & Rand, G. K. (1989). The effect of ignoring routes when locating depots. European journal of operational research, 39(2), 150-156.
Silvestrin, P. V., & Ritt, M. (2017). An iterated tabu search for the multi-compartment vehicle routing problem. Computers & Operations Research, 81, 192-202.
Soltani, Z., & Navimipour, N. J. (2016). Customer relationship management mechanisms: A systematic review of the state of the art literature and recommendations for future research. Computers in Human Behavior, 61, 667-688.
Sulaiman, M. H., Mustafa, M. W., Azmi, A., Aliman, O., & Rahim, S. A. (2012). Optimal allocation and sizing of distributed generation in distribution system via firefly algorithm. Paper presented at the Power Engineering and Optimization Conference (PEDCO) Melaka, Malaysia, 2012 Ieee International.
Vallada, E., Ruiz, R., & Minella, G. (2008). Minimising total tardiness in the m-machine flowshop problem: A review and evaluation of heuristics and metaheuristics. Computers & Operations Research, 35(4), 1350-1373.
Wasner, M., & Zäpfel, G. (2004). An integrated multi-depot hub-location vehicle routing model for network planning of parcel service. International Journal of Production Economics, 90(3), 403-419.
Yang, L., Sun, X., & Chi, T. (2013). An ant colony optimization algorithm and multi-agent system combined method to solve Single Source Capacitated Facility Location Problem. Paper presented at the Advanced Computational Intelligence (ICACI), 2013 Sixth International Conference on.
Yu, J., Chen, Y., Wu, J., Liu, R., Xu, H., Yao, D., & Fu, J. (2014). Particle swarm optimization based spatial location allocation of urban parks—A case study in Baoshan District, Shanghai, China. Paper presented at the Agro-geoinformatics (Agro-geoinformatics 2014), Third International Conference on.
Zhang, Z., Gong, J., Liu, J., & Chen, F. (2022). A fast two-stage hybrid meta-heuristic algorithm for robust corridor allocation problem. Advanced Engineering Informatics, 53, 101700.