Providing an integrated multi-depot vehicle routing problem model with simultaneous pickup and delivery and package layout under uncertainty with fuzzy-robust box optimization method
DOI:
https://doi.org/10.31181/dmame622023640Keywords:
MDVRP, SPD, package layout, MOALO, FRBOAbstract
This paper modeled and solved an integrated multi-depot vehicle routing problem (MDVRP) with simultaneous pickup and delivery (SPD) with package layout under unpredictable pickup, delivery, and transfer costs. The model described in this paper is divided into two stages. In the first stage, the SCA algorithm is used to optimize the package dimensions (a collection of commodities consumers need). The NSGA II and MOALO algorithms are used in the second stage to optimize the three objective functions of 1 simultaneously) minimizing total costs, 2) minimizing co2 emissions, and 3) minimizing the maximum working hours of drivers based on the optimal dimensions (length, width, and height) obtained from solving the first stage model. Determining the quantity and ideal location of possible warehouses, the best route for trucks to take to deliver and collect customer items, and the distribution of customers to warehouses are the key goals of the second stage. Since the model is unclear, the problem's uncertainty parameters are controlled using a novel fuzzy-robust box optimization (FRBO) technique. This technique, which combines the advantages of fuzzy programming with robust box-based optimization, produces excellent results when used to optimize objective functions. The numerical calculations in the numerical example show that the total network costs and CO2 emissions increased in the second stage in the presented model with an increasing uncertainty rate. At the same time, the maximum working hours of drivers decreased due to the shortened communication route and the number of vehicles increasing. Finally, the MOALO algorithm was used to resolve a case study at Safir Broadcasting Company because of its excellent efficiency in resolving the created model, the findings of which revealed the presence of 13 potential effective solutions. The quantity of greenhouse gas emissions rose by 1.11%, the overall expenditures climbed by 1.72%, and the number of hours that drivers worked fell by 11.98% when the uncertainty rate was raised from 0.5 to 0.7, according to research on the FRBO.
Downloads
References
Ahmadizar, F., Zeynivand, M., & Arkat, J. (2015). Two-level vehicle routing with cross-docking in a three-echelon supply chain: A genetic algorithm approach. Applied Mathematical Modelling, 39, 7065–7081. https://doi.org/10.1016/j.apm.2015.03.005
Avci, M., & Topaloglu, S. (2016). A hybrid metaheuristic algorithm for heterogeneous vehicle routing problem with simultaneous pickup and delivery. Expert Systems with Applications, 53, 160–171. https://doi.org/10.1016/j.eswa.2016.01.038
Belgin, O., Karaoglan, I., & Altiparmak, F. (2018). Two-echelon vehicle routing problem with simultaneous pickup and delivery: Mathematical model and heuristic approach. Computers and Industrial Engineering, 115, 1–16. https://doi.org/10.1016/j.cie.2017.10.032
Brandão, J. (2018). Iterated local search algorithm with ejection chains for the open vehicle routing problem with time windows. Computers and Industrial Engineering, 120, 146–159. https://doi.org/10.1016/j.cie.2018.04.032
Casazza, M., Ceselli, A., & Wolfler Calvo, R. (2021). A route decomposition approach for the single commodity Split Pickup and Split Delivery Vehicle Routing Problem. European Journal of Operational Research, 289, 897–911. https://doi.org/10.1016/j.ejor.2019.07.015
Curtois, T., Landa-Silva, D., Qu, Y., & Laesanklang, W. (2018). Large neighbourhood search with adaptive guided ejection search for the pickup and delivery problem with time windows. EURO Journal on Transportation and Logistics, 7, 151–192. https://doi.org/10.1007/s13676-017-0115-6
Dambakk, C. (2019). The Maritime Pickup and Delivery Problem with Cost and Time Window Constraints: System Modeling and A* Based Solution. Master’s Thesis, Universitetet i Agder; University of Agder.
Dantzig, G. B., & Ramser, J. H. (1959). The Truck Dispatching Problem. Management Science, 6, 80–91. https://doi.org/10.1287/mnsc.6.1.80
Dell’Amico, M., Furini, F., & Iori, M. (2020). A branch-and-price algorithm for the temporal bin packing problem. Computers and Operations Research, 114, 104825. https://doi.org/10.1016/j.cor.2019.104825
do C. Martins, L., Hirsch, P., & Juan, A. A. (2021). Agile optimization of a two-echelon vehicle routing problem with pickup and delivery. International Transactions in Operational Research, 28, 201–221. https://doi.org/10.1111/itor.12796
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. https://doi.org/10.1016/j.ins.2017.02.011
Fu, Y., & Banerjee, A. (2020). Heuristic/meta-heuristic methods for restricted bin packing problem. Journal of Heuristics, 26, 637–662. https://doi.org/10.1007/s10732-020-09444-y
Hemmati Golsefidi, A., & Akbari Jokar, M. R. (2020). A robust optimization approach for the production-inventory-routing problem with simultaneous pickup and delivery. Computers and Industrial Engineering, 143, 106388. https://doi.org/10.1016/j.cie.2020.106388
Ghahremani-Nahr, J., Najafi, S. E., & Nozari, H. (2022). A Combined Transportation Model for the Fruit and Vegetable Supply Chain Network. Journal of Optimization in Industrial Engineering, 15, 131–145. https://doi.org/10.22094/joie.2022.1948231.1925
Hadian, H., Golmohammadi, A. M., Hemmati, A., & Mashkani, O. (2019). A multi-depot location routing problem to reduce the differences between the vehicl' traveled distances; A comparative study of heuristics. Uncertain Supply Chain Management, 7, 17–32. https://doi.org/10.5267/j.uscm.2018.6.001
Kalayci, C. B., & Kaya, C. (2016). An ant colony system empowered variable neighborhood search algorithm for the vehicle routing problem with simultaneous pickup and delivery. Expert Systems with Applications, 66, 163–175. https://doi.org/10.1016/j.eswa.2016.09.017
Kara, İ., Kara, B. Y., & Yetis, M. K. (2007). Energy Minimizing Vehicle Routing Problem BT - Combinatorial Optimization and Applications. International Conference on Combinatorial Optimization and Applications, 4616, 62–71.
Kartal, Z., Hasgul, S., & Ernst, A. T. (2017). Single allocation p-hub median location and routing problem with simultaneouspickup and delivery. Transportation Research Part E: Logistics and Transportation Review, 108, 141–159. https://doi.org/10.1016/j.tre.2017.10.004
Koç, Ç., Laporte, G., & Tükenmez, İ. (2020). A review of vehicle routing with simultaneous pickup and delivery. Computers and Operations Research, 122, 104987. https://doi.org/10.1016/j.cor.2020.104987
Ky Phuc, P. N., & Phuong Thao, N. Le. (2021). Ant Colony Optimization for Multiple Pickup and Multiple Delivery Vehicle Routing Problem with Time Window and Heterogeneous Fleets. Logistics, 5, 28. https://doi.org/10.3390/logistics5020028
Lagos, C., Guerrero, G., Cabrera, E., Moltedo-Perfetti, Andr. S., Johnson, F., & Paredes, F. (2018). An improved particle swarm optimization algorithm for the VRP with simultaneous pickup and delivery and time windows. IEEE Latin America Transactions, 16, 1732–1740. https://doi.org/10.1109/TLA.2018.8444393
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 Journal, 81, 105–460. https://doi.org/10.1016/j.asoc.2019.04.030
Ma, Y., Li, Z., Yan, F., & Feng, C. (2019). A hybrid priority-based genetic algorithm for simultaneous pickup and delivery problems in reverse logistics with time windows and multiple decision-makers. Soft Computing, 23, 6697–6714. https://doi.org/10.1007/s00500-019-03754-5
Mirjalili, S. (2015). The ant lion optimizer. Advances in Engineering Software, 83, 80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010
Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133.
Wang, M., & Lu, G. (2021). A Modified Sine Cosine Algorithm for Solving Optimization Problems. IEEE Access, 9, 27434–27450. https://doi.org/10.1109/ACCESS.2021.3058128
Nadizadeh, A., & Kafash, B. (2019). Fuzzy capacitated location-routing problem with simultaneous pickup and delivery demands. Transportation Letters, 11, 1–19. https://doi.org/10.1080/19427867.2016.1270798
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, 266, 819–839. https://doi.org/10.1016/j.ejor.2017.10.046
Sadati, M. E. H., Aksen, D., & Aras, N. (2020). The r‐interdiction selective multi‐depot vehicle routing problem. International Transactions in Operational Research, 27, 835-866.
Scheithauer, G. (2018). Introduction to Cutting and Packing Optimization. Springer, Cham., 263, 14. http://link.springer.com/10.1007/978-3-319-64403-5
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, 25, 1–45. https://doi.org/10.1007/s10732-018-9382-0
Ulmer, M. W., Thomas, B. W., Campbell, A. M., & Woyak, N. (2021). The restaurant meal delivery problem: Dynamic pickup and delivery with deadlines and random ready times. Transportation Science, 55, 75–100. https://doi.org/10.1287/TRSC.2020.1000
Zahedi, M. R., & Nahr, J. G. (2020). Designing a hub covering location problem under uncertainty conditions. Decision Science Letters, 9, 477–500. https://doi.org/10.5267/j.dsl.2020.2.002
Zhang, S., Zhang, W., Gajpal, Y., & Appadoo, S. S. (2019). Ant Colony Algorithm for Routing Alternate Fuel Vehicles in Multi-depot Vehicle Routing Problem. Springer, Singapore. In Decision Science in Action., 251–260. https://doi.org/10.1007/978-981-13-0860-4_19
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Decision Making: Applications in Management and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.