Journal of Transportation and Logistics

JTL

SUBMITINFORMATION
EnglishTürkçe

Research Article


DOI :10.26650/JTL.2023.1101161   IUP :10.26650/JTL.2023.1101161    Full Text (PDF)

Fleet Size and Mix Vehicle Routing Problem (FSMVRP), Adapted Large Neighbourhood Search Heuristic Optimization ProposalWith a Plant-capacity and Multi-day Planning Algorithm: A Livestock Feed Industry Case Study

Alperen Ekrem Çelikdin

The vehicle routing problem (VRP) is of great importance for feed factories that do not work with the dealership system. This is especially important in the Central Anatolian region, where customers’ number of animals is low. Data used in the study came from the order data of a feed mill which operates in Turkey. Before selecting the most suitable VRP software vendor, the logistics manager of the plant was urged to analyse the results with the scope of percent fleet capacity used, service level (on-time deliveries), and total transportation cost incurred. As a requirement of the enterprise strategy, a multi-day planning algorithm was developed to level the daily production capacity of the factory while maintaining minimum transportation costs and maximum service level. It has been determined that better results are achieved with the developed multi-day planning algorithm for both methods of Simulated Annealing (SA), Genetic Algorithm (GA), and our Adapted Large Neighbourhood Search (ALNS) heuristic. The data set of the real-life problem that was used was applied to those three methods, and 0.45%, 0.81%, and 1.39% improvements were achieved using the methods, respectively.

Keywords: Vehicle routing problem, fleet size mix with time windows, multi-day planning, feed distribution, adapted large neighbourhood search

PDF View

References

  • Ai, T. J., & Kachitvichyanukul, V. (2009). Particle swarm optimization and two solution representations for solving the capacitated vehicle routing problem. Computers and Industrial Engineering, 56(1). doi:10.1016/j.cie.2008.06.012 google scholar
  • Baker, B. M., & Ayechew, M. A. (2003). A genetic algorithm for the vehicle routing problem. Computers and Operations Research, 30(5).S0305-0548(02)00051-5 google scholar
  • Chen, A., Gu, X., & Gao, Z. (2020). Two-Phase Algorithm to Multiple Depots Vehicle Routing Problem with Soft Time Windows. IOP Conference Series: Earth and Environmental Science, 587(1). doi:10.1088/1755-1315/587/1/012033 google scholar
  • Citation, S. (2001). Nutrient Requirements of Dairy Cattle. Nutrient Requirements of Dairy Cattle. doi:10.17226/9825 google scholar
  • Cordeau, J. F., Gendreau, M., & Laporte, G. (1997). A tabu search heuristic for periodic and multi-depot vehicle routing problems. Networks, 30(2). doi:10.1002/(SICI)1097-0037(199709)30:2<105::AID-NET5>3.0.CO;2-G google scholar
  • Danna, E., & Pape, C. Le. (n.d.). Chapter 4 B RANCH-AND -PRICEHEURISTICS: ACASES TUDYONTHEVEHICLEROUTI N G PROBLEM WI T H TIME WI N D O W S, (1998), 1998-1999. google scholar
  • Dantzig, G. B., and Ramser, J. H. (1959). Dantzig1959.Pdf. Management Science. google scholar
  • Demir, E., Bektaş, T.,& Laporte, G. (2012). An adaptive large neighborhood search heuristic for the Pollution-Routing Problem. European Journal of Operational Research, 223(2), 346-359. doi:10.1016/j.ejor.2012.06.044 google scholar
  • Erdoğan, G. (2017). An open source Spreadsheet Solver for Vehicle Routing Problems. Computers and Operations Research, 84, 62-72. doi:10.1016/j.cor.2017.02.022 google scholar
  • Golden, B., Assad, A., Levy, L., & Gheysens, F. (1984). The fleet size and mix vehicle routing problem. Computers and Operations Research, 11(1), 49-66. doi:10.1016/0305-0548(84)90007-8 google scholar
  • Hoff, A., Andersson, H., Christiansen, M., Hasle, G., & L0kketangen, A. (2010). Industrial aspects and literatüre survey: Fleet composition and routing. Computers and Operations Research, 37(12), 2041-2061. doi:10.1016/j.cor.2010.03.015 google scholar
  • Holland, J. H. (1975). Adaptation in natüral and artificial systems: an introdüctory analysis with applications to biology, control, and artificial intelligence. Ann Arbor University of Michigan Press 1975. google scholar
  • Irnich, S., & Villeneuve, D. (2006). The shortest-path problem with resource constraints and k-cycle elimination for k > 3. INFORMS Journal on Computing, 18(3), 391-406. doi:10.1287/joc.1040.0117 google scholar
  • Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simülated annealing. Science, 220(4598). doi:10.1126/science.220.4598.671 google scholar
  • Küo, Y. (2010). Using simülated annealing to minimize füel consümption for the time-dependent vehicle roüting problem. Computers and Industrial Engineering, 59(1), 157-165. doi:10.1016/j.cie.2010.03.012 google scholar
  • Laporte, G. (1992). The traveling salesman problem: An overview of exact and approximate algorithms. European Journal of Operational Research, 59(2), 231-247. doi:10.1016/0377-2217(92)90138-Y google scholar
  • Liü, C.-M. (2005). The Mültidimensional and Hierarchical Strüctüre of Perceived Qüality and Cüstomer Satisfaction. International Journal of Management, 22(3). google scholar
  • Liü, F. H., & Shen, S. Y. (1999). The fleet size and mix vehicle roüting problem with time windows. Journal of the Operational Research Society, 50(7), 721-732. doi:10.1057/palgrave.jors.2600763 google scholar
  • Miller, C. E., Zemlin, R. A., & Tücker, A. W. (1960). Integer Programming Formülation of Traveling Salesman Problems. Journal of the ACM (JACM), 7(4). doi:10.1145/321043.321046 google scholar
  • Mohammed, M. A., Ahmad, M. S., & Mostafa, S. A. (2012). Using genetic algorithm in implementing capacitated vehicle roüting problem. 2012 International Conference on Computer and Information Science, ICCIS 2012 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2012 - Conference Proceedings, 1, 257-262. doi:10.1109/ICCISci.2012.6297250 google scholar
  • Nazif, H., & Lee, L. S. (2010). Optimized crossover genetic algorithm for vehicle roüting problem with time windows. American Joürnal of Applied Sciences, 7(1), 95-101. doi:10.3844/ajassp.2010.95.101 google scholar
  • Osman, I. H. (1993). Metastrategy simülated annealing and tabü search algorithms for the vehicle roüting problem. Annals of Operations Research, 41(4). doi:10.1007/BF02023004 google scholar
  • Pisinger, D., & Ropke, S. (2007). A general heüristic for vehicle roüting problems. Compüters and Operations Research, 34(8). doi:10.1016/j.cor.2005.09.012 google scholar
  • Prins, C. (2004). A simple and effective evolütionary algorithm for the vehicle roüting problem. Computers and Operations Research, 31(12). doi:10.1016/S0305-0548(03)00158-8 google scholar
  • Rattanamanee, T., Nanthavanij, S., & Dumrongsiri, A. (2020). Heuristic procedure for multi-workday vehiclerouting problem withpre-assigned workers and manüal ünloading. International Journal of Industrial Engineering: Theory Applications and Practice, 27(3). google scholar
  • Reimann, M., Doerner, K., & Hartl, R. F. (2004). D-ants: Savings based ants divide and conquer the vehicle routing problem. Computers and Operations Research, 31(4). doi:10.1016/S0305-0548(03)00014-5 google scholar
  • Rodriguez-Martm, I., Salazar-Gonzalez, J. J., & Yaman, H. (2019). The periodic vehicle routing problem with driver consistency. European Journal of Operational Research, 273(2), 575-584. doi:10.1016/j.ejor.2018.08.032 google scholar
  • Salhi, S., Sari, M., Saidi, D., & Touati, N. (1992). Adaptation of some vehicle fleet mix heuristics. Omega, 20(5-6), 653-660. doi:10.1016/0305-0483(92)90009-V google scholar
  • Shaw, P. (1998). Using constraint programming and local search methods to solve vehicle routing problems. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1520, 417-431. doi:10.1007/3-540-49481-230 google scholar
  • Sungur, I., Ordonez, F., & Dessouky, M. (2008). A robust optimization approach for the capacitated vehicle routing problem with demand uncertainty. IIE Transactions (Institute of Industrial Engineers), 40(5), 509-523. doi:10.1080/07408170701745378 google scholar
  • Tellez, O., Vercraene, S., Lehuede, F., Peton, O., Tellez, O., Vercraene, S.,... Monteiro, T. (2017). The fleet size and mix dial-a-ride problem with reconfigurable vehicle capacity To cite this version: HAL Id: hal-01619103 The fleet size and mix dial-a-ride problem with reconfigurable vehicle capacity. google scholar
  • Zhang, J., Lam, W. H. K., & Chen, B. Y. (2016). On-time delivery probabilistic models for the vehicle routing problem with stochastic demands and time windows. European Journal of Operational Research, 249(1), 144-154. doi:10.1016/j.ejor.2015.08.050 google scholar
  • Customers, orders, vehicles, results and data for reproduction; https://drive.google.com/drive/folders/1zUuSoq5OOEhIiADGr0KKtTFmwWO1OaX-?usp=sharingInformation has been reached from https://www.openstreetmap.org/ google scholar

Citations

Copy and paste a formatted citation or use one of the options to export in your chosen format


EXPORT



APA

Çelikdin, A.E. (2023). Fleet Size and Mix Vehicle Routing Problem (FSMVRP), Adapted Large Neighbourhood Search Heuristic Optimization ProposalWith a Plant-capacity and Multi-day Planning Algorithm: A Livestock Feed Industry Case Study. Journal of Transportation and Logistics, 8(1), 1-12. https://doi.org/10.26650/JTL.2023.1101161


AMA

Çelikdin A E. Fleet Size and Mix Vehicle Routing Problem (FSMVRP), Adapted Large Neighbourhood Search Heuristic Optimization ProposalWith a Plant-capacity and Multi-day Planning Algorithm: A Livestock Feed Industry Case Study. Journal of Transportation and Logistics. 2023;8(1):1-12. https://doi.org/10.26650/JTL.2023.1101161


ABNT

Çelikdin, A.E. Fleet Size and Mix Vehicle Routing Problem (FSMVRP), Adapted Large Neighbourhood Search Heuristic Optimization ProposalWith a Plant-capacity and Multi-day Planning Algorithm: A Livestock Feed Industry Case Study. Journal of Transportation and Logistics, [Publisher Location], v. 8, n. 1, p. 1-12, 2023.


Chicago: Author-Date Style

Çelikdin, Alperen Ekrem,. 2023. “Fleet Size and Mix Vehicle Routing Problem (FSMVRP), Adapted Large Neighbourhood Search Heuristic Optimization ProposalWith a Plant-capacity and Multi-day Planning Algorithm: A Livestock Feed Industry Case Study.” Journal of Transportation and Logistics 8, no. 1: 1-12. https://doi.org/10.26650/JTL.2023.1101161


Chicago: Humanities Style

Çelikdin, Alperen Ekrem,. “Fleet Size and Mix Vehicle Routing Problem (FSMVRP), Adapted Large Neighbourhood Search Heuristic Optimization ProposalWith a Plant-capacity and Multi-day Planning Algorithm: A Livestock Feed Industry Case Study.” Journal of Transportation and Logistics 8, no. 1 (May. 2024): 1-12. https://doi.org/10.26650/JTL.2023.1101161


Harvard: Australian Style

Çelikdin, AE 2023, 'Fleet Size and Mix Vehicle Routing Problem (FSMVRP), Adapted Large Neighbourhood Search Heuristic Optimization ProposalWith a Plant-capacity and Multi-day Planning Algorithm: A Livestock Feed Industry Case Study', Journal of Transportation and Logistics, vol. 8, no. 1, pp. 1-12, viewed 17 May. 2024, https://doi.org/10.26650/JTL.2023.1101161


Harvard: Author-Date Style

Çelikdin, A.E. (2023) ‘Fleet Size and Mix Vehicle Routing Problem (FSMVRP), Adapted Large Neighbourhood Search Heuristic Optimization ProposalWith a Plant-capacity and Multi-day Planning Algorithm: A Livestock Feed Industry Case Study’, Journal of Transportation and Logistics, 8(1), pp. 1-12. https://doi.org/10.26650/JTL.2023.1101161 (17 May. 2024).


MLA

Çelikdin, Alperen Ekrem,. “Fleet Size and Mix Vehicle Routing Problem (FSMVRP), Adapted Large Neighbourhood Search Heuristic Optimization ProposalWith a Plant-capacity and Multi-day Planning Algorithm: A Livestock Feed Industry Case Study.” Journal of Transportation and Logistics, vol. 8, no. 1, 2023, pp. 1-12. [Database Container], https://doi.org/10.26650/JTL.2023.1101161


Vancouver

Çelikdin AE. Fleet Size and Mix Vehicle Routing Problem (FSMVRP), Adapted Large Neighbourhood Search Heuristic Optimization ProposalWith a Plant-capacity and Multi-day Planning Algorithm: A Livestock Feed Industry Case Study. Journal of Transportation and Logistics [Internet]. 17 May. 2024 [cited 17 May. 2024];8(1):1-12. Available from: https://doi.org/10.26650/JTL.2023.1101161 doi: 10.26650/JTL.2023.1101161


ISNAD

Çelikdin, AlperenEkrem. “Fleet Size and Mix Vehicle Routing Problem (FSMVRP), Adapted Large Neighbourhood Search Heuristic Optimization ProposalWith a Plant-capacity and Multi-day Planning Algorithm: A Livestock Feed Industry Case Study”. Journal of Transportation and Logistics 8/1 (May. 2024): 1-12. https://doi.org/10.26650/JTL.2023.1101161



TIMELINE


Submitted10.04.2022
Accepted30.01.2023
Published Online10.08.2023

LICENCE


Attribution-NonCommercial (CC BY-NC)

This license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms.


SHARE




Istanbul University Press aims to contribute to the dissemination of ever growing scientific knowledge through publication of high quality scientific journals and books in accordance with the international publishing standards and ethics. Istanbul University Press follows an open access, non-commercial, scholarly publishing.