Drug logistics network design based on the fleet routing by using the improved gray wolf optimizer algorithm

Subject Areas : Industrial Management

farzad mahmoodi ¹ , Farzad Pouyan far ²

1 - department of industrial management, faculty of management and accounting, islamic azad University, qazvin. iran.
2 - Department of Industrial Management, Faculty of Management and Accounting, Islamic Azad University, Qazvin Branch

Received: 2020-05-31 Accepted : 2020-11-07 Published : 2020-11-14

Keywords: shipping fleet routing, gray wolf algorithm, drug logistics, hazardous material transportation,

Abstract :

Transportation of pharmaceuticals as one of the most complex types of transportation has always been studied by researchers. This issue, which is a subset of a key issue called the transportation of hazardous substances, is one of the most integral and high-risk activities in the industrial activity cycle. Trying to find the optimal solution to this problem is one of the most useful topics in logistics. Accordingly, this study optimized the design of the drug logistics network. In this regard, the issue of vehicle routing (VRP) has been inspired. In this regard, the issue of vehicle routing (VRP) is inspired. To this end, first a conceptual model for this problem and a new mathematical model for routing drug transport vehicles with the role of path sensitivity and time window uncertainty are presented. In order to solve the problem, the Gray Wolf meta-heuristic algorithm has been used as a new and efficient algorithm. To evaluate the performance of the proposed algorithm, this algorithm is compared with the exact solution method and genetic algorithms and particle swarm and the results of the gray wolf algorithm show that this algorithm provides answers with the least possible error in a very short time.

References:

1. 
   
   1. Androutsopoulos, K. N., & Zografos, K. G. (2012). A bi-objective time-dependent vehicle routing and scheduling problem for hazardous materials distribution. EURO Journal on Transportation and Logistics, 1(1-2), 157-183.
   
   
   2. Ardjmand, E., Weckman, G., Park, N., Taherkhani, P., & Singh, M. (2015). Applying genetic algorithm to a new location and routing model of hazardous materials. International Journal of Production Research, 53(3), 916-928.
   
   
   3. Bektaş, T., & Laporte, G. (2011). The pollution-routing problem. Transportation Research Part B: Methodological, 45(8), 1232-1250.
   
   
   4. Bérubé, J. F., Gendreau, M., & Potvin, J. Y. (2009). An exact e-constraint method for bi-objective combinatorial optimization problems: Application to the Traveling Salesman Problem with Profits. European journal of operational research, 194(1), 39-50.
   
   
   5. Bouziyane, B., Dkhissi, B., & Cherkaoui, M. (2020). Multiobjective optimization in delivering pharmaceutical products with disrupted vehicle routing problem. International Journal of Industrial Engineering Computations, 11(2), 299-316.
   
   
   6. Chai, H., He, R., Ma, C., Dai, C., & Zhou, K. (2017). Path Planning and Vehicle Scheduling Optimization for Logistic Distribution of Hazardous Materials in Full Container Load. Discrete Dynamics in Nature and Society, 
   
   
   7. Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management science, 6(1), 80-91.
   
   
   8. Darbari, J. D., Agarwal, V., & Jha, P. C. (2015). Fuzzy optimization approach to supply chain distribution network for product value recovery. In Proceedings of Fourth International Conference on Soft Computing for Problem Solving (pp. 491-504). Springer, New Delhi.
   
   
   9. Das, A., Gupta, A. K., & Mazumder, T. N. (2012). A comprehensive risk assessment framework for offsite transportation of inflammable hazardous waste. Journal of hazardous materials, 227, 88-96.
   
   
   10. Du, J., Li, X., Yu, L., Dan, R., & Zhou, J. (2017). Multi-depot vehicle routing problem for hazardous materials transportation: a fuzzy bilevel programming. Information Sciences, 399, 201-218.
   
   
   11. Jabir, E, Panickera, V. V., & Sridharana, R. (2015). Multi-objective optimization model for a green vehicle routing problem. Procedia-Social and Behavioral Sciences, 189, 33-39.
   
   
   12. Govindan, K., Soleimani, H., & Kannan, D. (2015). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, 240(3), 603-626.
   
   
   13. Hu, H., Li, J., & Li, X. (2018). A credibilistic goal programming model for inventory routing problem with hazardous materials. Soft Computing, 22(17), 5803-5816.
       
       
       1. Ibarra-Rojas, O. J., Hernandez, L., & Ozuna, L. (2017). Accessibility Vehicle Routing Problem. Journal of cleaner production, 172, 1514-1528.
       
       
   
   
   14. Juwana, I., Muttil, N., & Perera, B. J. C. (2012). Indicator-based water sustainability assessment—A review. Science of the Total Environment, 438, 357-371.
   
   
   15. Kara, B. Y., & Verter, V. (2004). Designing a road network for hazardous materials transportation. Transportation Science, 38(2), 188-196.
   
   
   16. Kazantzi, V., Kazantzis, N., & Gerogiannis, V. C. (2011). Risk informed optimization of a hazardous material multi-periodic transportation model. Journal of Loss Prevention in the Process Industries, 24(6), 767-773.
   
   
   17. Kazemian, I., & Aref, S. (2017). A green perspective on capacitated time-dependent vehicle routing problem with time windows. International Journal of Supply Chain and Inventory Management, 2(1), 20-38.
   
   
   18. Leung, S. C., Zhou, X., Zhang, D., & Zheng, J. (2011). Extended guided tabu search and a new packing algorithm for the two-dimensional loading vehicle routing problem. Computers & Operations Research, 38(1), 205-215.
   
   
   19. List, G. F., Mirchandani, P. B., Turnquist, M. A., & Zografos, K. G. (1991). Modeling and analysis for hazardous materials transportation: Risk analysis, routing/scheduling and facility location. Transportation Science, 25(2), 100-114.
   
   
   20. Madankumar, S., & Rajendran, C. (2018). Mathematical models for green vehicle routing problems with pickup and delivery: A case of semiconductor supply chain. Computers & Operations Research, 89, 183-192.
   
   
   21. Nowruzi Narges, Reza Tavakoli Moghadam, Mohsen Sadegh Amel Nik, Sadegh Khaefi.( 2012). New Mathematical Modeling The Problem of Locating Facilities and Routing the Connection Means and Solving It with Competition Algorithm, Integrated Colonialism, Specialized Journal of Industrial Engineering, No. 1.137 - Spring and Summer, Page 1.
   
   
   22. Pokharel, S., & Mutha, A. (2009). Perspectives in reverse logistics: a review. Resources, Conservation and Recycling, 53(4), 175-182.
   
   
   23. Sargordan Fard Arani, Vahid; Kia, Reza; Ghaffari, Mahdi 1394. Modeling the problem of vehicle routing by considering multiple warehouses, simultaneous delivery and loading, hard and soft time window, cost and depreciation depending on the amount of load in the vehicle and the type of route, International Conference on New Research in Management and Industrial Engineering, Tehran, Iran. (In Persian).
   
   
   24. Sbihi, A., & Eglese, R. W. (2007). The relationship between vehicle routing and scheduling and green logistics-a literature survey.
   
   
   25. Sobhani, Ramin; Ali Paydar and Hassan Zoghi.( 2016) Safety Management Model for Drivers and Operators of Dangerous Materials Loading in Road Transport Case Study of Iran Oil Company Refinery Transport System, Third National Conference on Recent Innovations in Civil Engineering, Architecture and Urban Planning, Tehran, Nikan Institute of Higher Education(In Persian).
   
   
   26. Soleimani, H., Chaharlang, Y., & Ghaderi, H. (2017). Collection and distribution of returned-remanufactured products in a vehicle routing problem with pickup and delivery considering sustainable and green criteria. Journal of Cleaner Production, 172, 960-970. (In Persian).
   
   
   27. Tarantilis, C. D., & Kiranoudis, C. T. (2001). Using the vehicle routing problem for the transportation of hazardous materials. Operational Research, 1(1), 67.
   
   
   28. Toro, E. M., Franco, J. F., Echeverri, M. G., & Guimarães, F. G. (2017). A multi-objective model for the green capacitated location-routing problem considering environmental impact. Computers & Industrial Engineering, 110, 114-125.
   
   
   29. Turkensteen, M., & Hasle, G. (2017). Combining pickups and deliveries in vehicle routing–An assessment of carbon emission effects. Transportation Research Part C: Emerging Technologies, 80, 117-132.
   
   
   30. Wang, Y., Zhang, S., Assogba, K., Fan, J., Xu, M., & Wang, Y. (2018). Economic and environmental evaluations in the two-echelon collaborative multiple centers vehicle routing optimization. Journal of Cleaner Production, 197, 443-461.
   
   
   31. Xiao, Y., & Konak, A. (2016). The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion. Transportation Research Part E: Logistics and Transportation Review, 88, 146-166.
   
   
   32. Xiao, Y., Zhao, Q., Kaku, I., & Xu, Y. (2012). Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Computers & operations research, 39(7), 1419-1431.
   
   
   33. Yuan, W., Wang, J., Li, J., Yan, B., & Wu, J. (2017, September). Two-stage heuristic algorithm for a new model of hazardous material multi-depot vehicle routing problem. In UK Workshop on Computational Intelligence (pp. 362-366). Springer, Cham.
   
   
   34. Zhang, H., Zhang, Q., Ma, L., Zhang, Z., & Liu, Y. (2019). A hybrid ant colony optimization algorithm for a multi-objective vehicle routing problem with flexible time windows. Information Sciences, 490, 166-190.
   
   
   35. Zografos, K. G., & Androutsopoulos, K. N. (2004). A heuristic algorithm for solving hazardous materials distribution problems. European Journal of Operational Research, 152(2), 507-519.