A Multi Echelon Location-Routing-Inventory Model for a Supply Chain Network: NSGA II and Multi-Objective Whale Optimization Algorithm

Document Type : SI: SD of ISC

Authors

1 Department of Industrial Engineering-Ershad University of Damavand, Tehran, Iran

2 Research of Industrial Engineering, Islamic Azad University of Karaj Branch, Karaj, Iran

3 Department of Business Decisions and Analytics

4 Research of Industrial Engineering, Islamic Azad University of Karaj

Abstract

In this study, we aim to explore the modeling and solution approach for a multi-objective location-routing-inventory problem. The focus is on planned transportation with the goal of minimizing total costs and reducing the maximum working hours of drivers. To achieve these objectives, we need to consider the routing of vehicles between customers and distribution centers, as well as the optimal allocation of product transfer flow between the production center and customers. Therefore, the proposed model incorporates location, routing-inventory, and allocation simultaneously. To solve the two-objective model, we employed the Epsilon-constraint method for small-sized problems. For large-sized problems, we utilized the NSGA-II and MOWOA meta-heuristic algorithms with a new chromosome. The computational results indicate that in order to reduce the maximum working hours of drivers, it is necessary to increase the number of vehicles and minimize travel distances. However, this leads to higher costs due to vehicle utilization and the need for constructing distribution centers closer to customers, which in turn increases construction costs. Finally, based on the analysis, the NSGA-II algorithm outperformed the MOWOA algorithm with a weighted value of 0.983 compared to 0.016, making it the selected algorithm.

Keywords


Ahmadini, A. A. H., Modibbo, U. M., Shaikh, A. A., & Ali, I. (2021). Multi-objective optimization modelling of sustainable green supply chain in inventory and production management. Alexandria Engineering Journal, Vol. 60(6), pp. 5129-5146.
Alinaghian, M., & Shokouhi, N. (2018). Multi-depot multi-compartment vehicle routing problem, solved by a hybrid adaptive large neighborhood search. Omega, Vol. 76, pp. 85-99.
AlArjani, A., Modibbo, U. M., Ali, I., & Sarkar, B. (2021). A new framework for the sustainable development goals of Saudi Arabia. Journal of King Saud University-Science, 33(6), 101477.
Amiri, A., Amin, S. H., & Zolfagharinia, H. (2023). A bi-objective green vehicle routing problem with a mixed fleet of conventional and electric trucks: Considering charging power and density of stations. Expert Systems with Applications, Vol. 213, 119228.
Arani, M., Chan, Y., Liu, X., & Momenitabar, M. (2021). A lateral resupply blood supply chain network design under uncertainties. Applied Mathematical Modelling, Vol. 93, pp. 165-187.
Biuki, M., Kazemi, A., & Alinezhad, A. (2020). An integrated location-routing-inventory model for sustainable design of a perishable products supply chain network. Journal of Cleaner Production, Vol. 260, 120842.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, Vol. 6(2), pp. 182-197.
Dell’Amico, M., Furini, F., & Iori, M. (2020). A branch-and-price algorithm for the temporal bin packing problem. Computers & operations research, Vol. 114, 104825.
Fahmy, S. A., Zaki, A. M., & Gaber, Y. H. (2023). Optimal locations and flow allocations for aggregation hubs in supply chain networks of perishable products. Socio-Economic Planning Sciences, Vol. 86, 101500.
Fu, Y., & Banerjee, A. (2020). Heuristic/meta-heuristic methods for restricted bin packing problem. Journal of heuristics, Vol. 26, pp. 637-662.
Ghahremani-Nahr, J., Kian, R., & Sabet, E. (2019). A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm. Expert systems with applications, Vol. 116, pp. 454-471.
Ghasemi, P., Goodarzian, F., Abraham, A., & Khanchehzarrin, S. (2022). A possibilistic-robust-fuzzy programming model for designing a game theory based blood supply chain network. Applied Mathematical Modelling, Vol. 112, pp. 282-303.
Ghasemi, P., Goodarzian, F., Muñuzuri, J., & Abraham, A. (2022). A cooperative game theory approach for location-routing-inventory decisions in humanitarian relief chain incorporating stochastic planning. Applied Mathematical Modelling, Vol. 104, pp. 750-781.
Goli, A., Tirkolaee, E. B., & Weber, G. W. (2020). A perishable product sustainable supply chain network design problem with lead time and customer satisfaction using a hybrid whale-genetic algorithm. Logistics operations and management for recycling and reuse, pp. 99-124.
Goli, A., Tirkolaee, E. B., Mahdavi, I., & Zamani, M. (2019). Solving a University Exam Scheduling Problem Using Genetic and Firefly Algorithms, International Conference of Industrial Engineering and Operation Management.
Govindan, K., Salehian, F., Kian, H., Hosseini, S. T., & Mina, H. (2023). A location-inventory-routing problem to design a circular closed-loop supply chain network with carbon tax policy for achieving circular economy: An augmented epsilon-constraint approach. International Journal of Production Economics, 108771.
Guimarães, T. A., Coelho, L. C., Schenekemberg, C. M., & Scarpin, C. T. (2019). The two-echelon multi-depot inventory-routing problem. Computers & Operations Research, Vol. 101, pp. 220-233.
Hadian, H., Golmohammadi, A., Hemmati, A., & Mashkani, O. (2019). A multi-depot location routing problem to reduce the differences between the vehicles’ traveled distances; a comparative study of heuristics. Uncertain supply chain management, Vol. 7(1), pp. 17-32.
Khan, M. F., Modibbo, U. M., Ahmad, N., & Ali, I. (2022). Nonlinear optimization in bi-level selective maintenance allocation problem. Journal of King Saud University-Science, Vol. 34(4), 101933.
Li, J., Li, T., Yu, Y., Zhang, Z., Pardalos, P. M., Zhang, Y., & Ma, Y. (2019). Discrete firefly algorithm with compound neighborhoods for asymmetric multi-depot vehicle routing problem in the maintenance of farm machinery. Applied soft computing, Vol. 81, 105460.
Li, P., Wen, M., Zu, T., & Kang, R. (2023). A Joint Location–Allocation–Inventory Spare Part Optimization Model for Base-Level Support System with Uncertain Demands. Axioms, Vol. 12(1), 46.
Lin, M. D., Liu, P. Y., Kuo, J. H., & Lin, Y. H. (2022). A multiobjective stochastic location-allocation model for scooter battery swapping stations. Sustainable Energy Technologies and Assessments, Vol. 52, 102079.
Ma, Y., Liu, B., Zhang, K., & Yang, Y. (2022). Incorporating multi-criteria suitability evaluation into multi-objective location–allocation optimization comparison for earthquake emergency shelters. Geomatics, Natural Hazards and Risk, Vol. 13(1), pp. 2333-2355.
Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, Vol. 95, pp. 51-67.
Mohammadi, S., Darestani, S. A., Vahdani, B., & Alinezhad, A. (2020). A robust neutrosophic fuzzy-based approach to integrate reliable facility location and routing decisions for disaster relief under fairness and aftershocks concerns. Computers & Industrial Engineering, Vol. 148, 106734.
Momenitabar, M., Ebrahimi, Z. D., Abdollahi, A., Helmi, W., Bengtson, K., & Ghasemi, P. (2023). An integrated machine learning and quantitative optimization method for designing sustainable bioethanol supply chain networks. Decision Analytics Journal, 100236.
Nasiri, M. M., Mousavi, H., & Nosrati-Abarghooee, S. (2023). A green location-inventory-routing optimization model with simultaneous pickup and delivery under disruption risks. Decision Analytics Journal, 100161.
Polyakovskiy, S., & M’Hallah, R. (2018). A hybrid feasibility constraints-guided search to the two-dimensional bin packing problem with due dates. European journal of operational research, Vol. 266(3), pp. 819-839.
Pourmohammadi, P., Tavakkoli-Moghaddam, R., Rahimi, Y., & Triki, C. (2023). Solving a hub location-routing problem with a queue system under social responsibility by a fuzzy meta-heuristic algorithm. Annals of Operations Research, Vol. 324(1-2), pp. 1099-1128.
Rahbari, M., Arshadi Khamseh, A., Sadati-Keneti, Y., & Jafari, M. J. (2022). A risk-based green location-inventory-routing problem for hazardous materials: NSGA II, MOSA, and multi-objective black widow optimization. Environment, Development and Sustainability, Vol. 24(2), pp. 2804-2840.
Sadati, M. E. H., Aksen, D., & Aras, N. (2020). The r‐interdiction selective multi‐depot vehicle routing problem. International transactions in operational research, Vol. 27(2), pp. 835-866.
Safaei, S., Ghasemi, P., Goodarzian, F., & Momenitabar, M. (2022). Designing a new multi-echelon multi-period closed-loop supply chain network by forecasting demand using time series model: a genetic algorithm. Environmental Science and Pollution Research, Vol. 29(53), pp. 79754-79768.
Sar, K., & Ghadimi, P. (2023). A Systematic Literature Review of the Vehicle Routing Problem in Reverse Logistics Operations. Computers & Industrial Engineering, 109011.
Seraji, H., Tavakkoli-Moghaddam, R., & Soltani, R. (2019). A two-stage mathematical model for evacuation planning and relief logistics in a response phase. Journal of Industrial and Systems Engineering, Vol. 12(1), pp. 129-146.
Seraji, H., Tavakkoli-Moghaddam, R., Asian, S., & Kaur, H. (2022). An integrative location-allocation model for humanitarian logistics with distributive injustice and dissatisfaction under uncertainty. Annals of Operations Research, Vol. 319(1), pp. 211-257.
Sethanan, K., & Pitakaso, R. (2016). Differential evolution algorithms for scheduling raw milk transportation. Computers and Electronics in Agriculture, Vol. 121, pp. 245-259.
Spencer, K. Y., Tsvetkov, P. V., & Jarrell, J. J. (2019). A greedy memetic algorithm for a multiobjective dynamic bin packing problem for storing cooling objects. Journal of heuristics, Vol. 25, pp. 1-45.
Wei, X., Qiu, H., Wang, D., Duan, J., Wang, Y., & Cheng, T. C. E. (2020). An integrated location-routing problem with post-disaster relief distribution. Computers & Industrial Engineering, Vol. 147, 106632.
Zhang, S., Zhang, W., Gajpal, Y., & Appadoo, S. S. (2019). Ant colony algorithm for routing alternate fuel vehicles in multi-depot vehicle routing problem. Decision Science in Action: Theory and Applications of Modern Decision Analytic Optimization, pp. 251-260.