Modelling and optimization of a tri-objective Transportation-Location-Routing Problem considering route reliability: using MOGWO, MOPSO, MOWCA and NSGA-II
الموضوعات :Fariba Maadanpour Safari 1 , Farhad Etebari 2 , Adel Pourghader Chobar 3
1 - Faculty of Mechanic and Industrial Engineering, Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - Faculty of Mechanic and Industrial Engineering, Department of Industrial Engineering, Qazvin Branch, Islamic
Azad University, Qazvin, Iran
3 - Faculty of Mechanic and Industrial Engineering, Department of Industrial Engineering, Qazvin Branch, Islamic
Azad University, Qazvin, Iran
الکلمات المفتاحية: reliability, Multi-objective particle swarm optimization, Transportation-Location-Routing, Multi-Objective Grey Wolf Optimizer, Multi-Objective Water Cycle Algorithm, Non-Dominated Sorting Genetic Algorithm- II,
ملخص المقالة :
In this research, a tri-objective mathematical model is proposed for the Transportation-Location-Routing problem. The model considers a three-echelon supply chain and aims to minimize total costs, maximize the minimum reliability of the traveled routes and establish a well-balanced set of routes. In order to solve the proposed model, four metaheuristic algorithms, including Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Water Cycle Algorithm (MOWCA), Multi-objective Particle Swarm Optimization (MOPSO) and Non-Dominated Sorting Genetic Algorithm- II (NSGA-II) are developed. The performance of the algorithms is evaluated by solving various test problems in small, medium, and large scale. Four performance measures, including Diversity, Hypervolume, Number of Non-dominated Solutions, and CPU-Time, are considered to evaluate the effectiveness of the algorithms. In the end, the superior algorithm is determined by Technique for Order of Preference by Similarity to Ideal Solution method.
Adrang, H., Bozorgi-Amiri, A., Khalili-Damghani, K., Tavakkoli-Moghaddam, R. (2020). Planning for Medical Emergency Transportation Vehicles during Natural Disasters. Journal of Optimization in Industrial Engineering, 13(2), 185-197.
Albareda-Sambola, M., Díaz, J. A., & Fernández, E. (2001). Heuristic approaches for solving a class of combined location-routing problem. In 4th Metaheuristics international conference, pp. 405-410.
Albareda-Sambola, M., Dı́az, J. A., & Fernández, E. (2005). A compact model and tight bounds for a combined location-routing problem. Computers & Operations Research, 32(3), 407-428.
Ambrosino, D., & Scutella, M. G. (2005). Distribution network design: New problems and related models. European journal of operational research, 165(3), 610-624.
Boccia, M., Crainic, T. G., Sforza, A., &Sterle, C. (2010, May). A metaheuristic for a two echelon location-routing problem. In International Symposium on Experimental Algorithms, pp. 288-301.
Brandão, J. (2020). A memory-based iterated local search algorithm for the multi-depot open vehicle routing problem. European Journal of Operational Research, 284(2), 559-571.
Caballero, R., González, M., Guerrero, F. M., Molina, J., &Paralera, C. (2007). Solving a multi-objective location routing problem with a metaheuristic based on tabu search. Application to a real case in Andalusia. European Journal of Operational Research, 177(3), 1751-1763.
Chaman-Motlagh, A. (2015). Superdefect photonic crystal filter optimization using grey wolf optimizer. IEEE Photonics Technology Letters, 27(22), 2355-2358.
Contardo, C., Hemmelmayr, V., &Crainic, T. G. (2012). Lower and upper bounds for the two-echelon capacitated location-routing problem. Computers & operations research, 39(12), 3185-3199.
Cornnejols, G., Fisher, M., &Nemhauser, G. (1977). Location of bank accounts of optimize float: An analytic study of exact and approximate algorithm. Management Science, 23, 789-810.
Emary, E., Zawbaa, H. M., &Hassanien, A. E. (2016). Binary grey wolf optimization approaches for feature selection. Neurocomputing, 172, 371-381.
Fazli-Khalaf, M., Khalilpourazari, S., &Mohammadi, M. (2017). Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design. Annals of Operations Research, 1-31.
Ghatreh Samani, M., & Hosseini-Motlagh, S. M. (2017). A hybrid algorithm for a two-echelon location-routing problem with simultaneous pickup and delivery under fuzzy demand. International Journal of Transportation Engineering, 5(1), 59-85.
Haeri, A., Hosseini‐Motlagh, S. M., Ghatreh Samani, M. R., & Rezaei, M. (2020). A mixed resilient‐efficient approach toward blood supply chain network design. International Transactions in Operational Research, 27(4), 1962-2001.
Haeri, A., Motlagh, S. M. H., Samani, M. R. G., & Rezaei, M. S. (2020). A bi-level programming approach for improving relief logistics operations: A real case in Kermanshah earthquake. Computers & Industrial Engineering, 106532.
Hidayatul, Y. S., Djunaidy, A., & Muklason, A. (2019, July). Solving Multi-objective Vehicle Routing Problem Using Hyper-heuristic Method by Considering Balance of Route Distances. In 2019 International Conference on Information and Communications Technology (ICOIACT) (pp. 937-942). IEEE.
Hosseini-Motlagh, S. M., Qamsari, A. N., & Samani, M. R. G. A ROBUST POSSIBILISTIC APPROACH FOR MULTI-DEPOT INVENTORY ROUTING PROBLEM. International Journal of Industrial Engineering: Theory, Applications and Practice, 27(2).
Hosseini-Motlagh, S. M., Samani, M. R. G., & Saadi, F. A. (2020). A novel hybrid approach for synchronized development of sustainability and resiliency in the wheat network. Computers and Electronics in Agriculture, 168, 105095.
Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making (pp. 58-191). Springer, Berlin, Heidelberg.
Jarboui, B., Derbel, H., Hanafi, S., &Mladenović, N. (2013). Variable neighborhood search for location routing. Computers & Operations Research, 40(1), 47-57.
Kamboj, V. K., Bath, S. K., & Dhillon, J. S. (2016). Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer. Neural Computing and Applications, 27(5), 1301-1316.
Karp, R. M. (1972). Reducibility among combinatorial problems. In complexity of computer computations (pp. 85-103). Springer, Boston, MA.
Khalilpourazari, S., & Mohammadi, M. (2018). A new exact algorithm for solving single machine scheduling problems with learning effects and deteriorating jobs. arXiv preprint arXiv:1809.03795.
Khalilpourazari, S., & Pasandideh, S. H. R. (2019). Sine–cosine crow search algorithm: theory and applications. Neural Computing and Applications, 1-18.
Khalilpourazari, S., &Mohammadi, M. (2016). Optimization of closed-loop Supply chain network design: a Water Cycle Algorithm approach. In Industrial Engineering (ICIE), 2016 12th International Conference on (pp. 41-45). IEEE.
Khalilpourazari, S., Mirzazadeh, A., Weber, G. W., & Pasandideh, S. H. R. (2019). A robust fuzzy approach for constrained multi-product economic production quantity with imperfect items and rework process. Optimization.
Khalilpourazari, S., Naderi, B., & Khalilpourazary, S. (2020). Multi-Objective Stochastic Fractal Search: a powerful algorithm for solving complex multi-objective optimization problems. Soft Computing, 24(4), 3037-3066.
Khalilpourazari, S., Pasandideh, S. H. R., & Niaki, S. T. A. (2019). Optimizing a multi-item economic order quantity problem with imperfect items, inspection errors, and backorders. Soft Computing, 23(22), 11671-11698.
Khalilpourazari, S., Soltanzadeh, S., Weber, G. W., & Roy, S. K. (2020). Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study. Annals of Operations Research, 289(1), 123-152.
Khalilpourazari, S., Teimoori, S., Mirzazadeh, A., Pasandideh, S. H. R., & Ghanbar Tehrani, N. (2019). Robust Fuzzy chance constraint programming for multi-item EOQ model with random disruption and partial backordering under uncertainty. Journal of Industrial and Production Engineering, 36(5), 276-285.
Komaki, G. M., &Kayvanfar, V. (2015). Grey Wolf Optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time. Journal of Computational Science, 8, 109-120.
Lashine, S. H., Fattouh, M., & Issa, A. (2006). Location/allocation and routing decisions in supply chain network design. Journal of Modelling in Management, 1(2), 173-183.
Lin, C. K. Y., & Kwok, R. C. W. (2006). Multi-objective metaheuristics for a location-routing problem with multiple use of vehicles on real data and simulated data. European Journal of Operational Research, 175(3), 1833-1849.
Marinakis, Y., &Marinaki, M. (2008). A particle swarm optimization algorithm with path relinking for the location routing problem. Journal of Mathematical Modelling and Algorithms, 7(1), 59-78.
Martínez-Salazar, I. A., Molina, J., Ángel-Bello, F., Gómez, T., & Caballero, R. (2014). Solving a bi-objective transportation location routing problem by metaheuristic algorithms. European Journal of Operational Research, 234(1), 25-36.
Manavizadeh, N., Farrokhi-Asl, H., W.T. Lim, S. (2020). A New Mathematical Model for the Green Vehicle Routing Problem by Considering a Bi-Fuel Mixed Vehicle Fleet. Journal of Optimization in Industrial Engineering, 13(2), 165-183.
Melechovský, J., Prins, C., & Calvo, R. W. (2005). A metaheuristic to solve a location-routing problem with non-linear costs. Journal of Heuristics, 11(5-6), 375-391.
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. D. S. (2016). Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Systems with Applications, 47, 106-119.
Mohammadi, M., & Khalilpourazari, S. (2017, February). Minimizing makespan in a single machine scheduling problem with deteriorating jobs and learning effects. In Proceedings of the 6th International Conference on Software and Computer Applications (pp. 310-315).
Pasandideh, S. H. R., & Khalilpourazari, S. (2018). Sine cosine crow search algorithm: a powerful hybrid Meta heuristic for global optimization. arXiv preprint arXiv:1801.08485.
Peng, y., & Bai, L. (2006, October). An integrated optimization problem in logistics and the PSO solution. In Service Systems and Service
Management, 2006 International Conference on (Vol. 2, pp. 965-970). IEEE.
Precup, R. E., David, R. C., &Petriu, E. M. (2017). Grey wolf optimizer algorithm-based tuning of fuzzy control systems with reduced parametric sensitivity. IEEE Transactions on Industrial Electronics, 64(1), 527-534.
Prins, C., Prodhon, C., & Calvo, R. W. (2006). Solving the capacitated location-routing problem by a GRASP complemented by a learning process and a path relinking. 4OR, 4(3), 221-238.
Samani, M. R. G., Hosseini-Motlagh, S. M., & Ghannadpour, S. F. (2019). A multilateral perspective towards blood network design in an uncertain environment: Methodology and implementation. Computers & Industrial Engineering, 130, 450-471.
Sharma, Y., &Saikia, L. C. (2015). Automatic generation control of a multi-area ST–Thermal power system using Grey Wolf Optimizer algorithm based classical controllers. International Journal of Electrical Power & Energy Systems, 73, 853-862.
Song, X., Tang, L., Zhao, S., Zhang, X., Li, L., Huang, J., & Cai, W. (2015). Grey Wolf Optimizer for parameter estimation in surface waves. Soil Dynamics and Earthquake Engineering, 75, 147-157.
Sulaiman, M. H., Mustaffa, Z., Mohamed, M. R., &Aliman, O. (2015). Using the gray wolf optimizer for solving optimal reactive power dispatch problem. Applied Soft Computing, 32, 286-292.
Ting, C. J., & Chen, C. H. (2013). A multiple ant colony optimization algorithm for the capacitated location routing problem. International Journal of Production Economics, 141(1), 34-44.
Tuzun, D., & Burke, L. I. (1999). A two-phase tabu search approach to the location routing problem. European journal of operational research, 116(1), 87-99.
Wang, X., Sun, X., & Fang, Y. (2005, October). A two-phase hybrid heuristic search approach to the location-routing problem. In Systems, Man and Cybernetics, 2005 IEEE International Conference on (Vol. 4, pp. 3338-3343). IEEE.
Wu, T. H., Low, C., & Bai, J. W. (2002). Heuristic solutions to multi-depot location-routing problems. Computers & Operations Research, 29(10), 1393-1415.
Zitzler, E. (1999). Evolutionary algorithms for multi-objective optimization: Methods and applications (Vol. 63): Citeseer.
Zitzler, E., & Thiele, L. (1998, September). Multi-objective optimization using evolutionary algorithms—a comparative case study. In international conference on parallel problem solving from nature (pp. 292-301). Springer, Berlin, Heidelberg.
Zitzler, E., & Thiele, L. (1999). Multi-objective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE transactions on Evolutionary Computation, 3(4), 257-271.
Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. TIK-report, 103.
