Application of  Particle Optimization and Genetic Algorithms (GA) Method in Fleet Transportation Systems of two-wheeled automotive industry

Hery Sumardiyanto; Jacky  Chin

doi:10.11111/ujost.v2i1.115

Authors

Hery Sumardiyanto Industrial Engineering Department, Mercu Buana University, Jakarta
Jacky Chin Industrial Engineering Department, Mercu Buana University, Jakarta

DOI:

https://doi.org/10.11111/ujost.v2i1.115

Keywords:

Fleet Transport Systems, Milkrun, VRP, PSO, GA

Abstract

One of the activities in the two-wheeled automotive industry is the process of sending components or parts from suppliers to motorcycle assembly plants. Delivery of parts using the fleet of each supplier. Along with the increase in market demand, the demand for components to suppliers is also increasing. This has an impact on increasing the number and frequency of fleets entering the factory area, besides that there is also an increase in transportation costs. This study aims to make the efficiency of the number, frequency and cost of fleet transportation by using the Milkrun approach and VRP (Vehicle Routing Problem). To get optimal results, the PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) methods are used by utilizing the Matlab 2022 software. This research involved 40 suppliers, Logistic Partners and motorcycle assembly factories. The results of this study showed better results, namely before the research the number of fleets was 40 units and the frequency of 40 shipments with a total shipping cost of IDR 2,769,330, while after the research with the PSO optimization method the fleet number was 5 units and the frequency of 8 shipments with a total shipping cost of IDR 2,679,113. For GA optimization, the number of fleets is 4 units and the frequency is 8 times of delivery with a total shipping cost of IDR 2,000,058.

References

li, M., & Farida, B. N. I. (2021). Completion of FCVRP using Hybrid Particle Swarm Optimization Algorithm. Jurnal Teknik Industri, 22(1), 98–112. https://doi.org/10.22219/JTIUMM.Vol22.No1.98-112

Allaoui, M., Ahiod, B., & El Yafrani, M. (2018). A hybrid crow search algorithm for solving the DNA fragment assembly problem. Expert Systems with Applications, 102, 44–56. https://doi.org/10.1016/j.eswa.2018.02.018

Cui, R., Dong, X., & Lin, Y. (2019). Models for aircraft maintenance routing problem with consideration of remaining time and robustness. Computers & Industrial Engineering, 137, 106045. https://doi.org/10.1016/j.cie.2019.106045

Dewi, S. K., & Utama, D. M. (2021). A New Hybrid Whale Optimization Algorithm for Green Vehicle Routing Problem. Systems Science and Control Engineering, 9(1), 61–72. https://doi.org/10.1080/21642583.2020.1863276

Eltoukhy, A. E. E., Chan, F. T. S., Chung, S. H., & Niu, B. (2018). A model with a solution algorithm for the operational aircraft maintenance routing problem. Computers and Industrial Engineering, 120, 346–359. https://doi.org/10.1016/j.cie.2018.05.002

Eydi, A., & Alavi, H. (2019). Vehicle Routing Problem in Reverse Logistics with Split Demands of Customers and Fuel Consumption Optimization. Arabian Journal for Science and Engineering, 44(3), 2641–2651. https://doi.org/10.1007/s13369-018-3311-2

Gad, A. G. (2022). Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review. Archives of Computational Methods in Engineering, 29(5), 2531–2561. https://doi.org/10.1007/s11831-021-09694-4

Gao, Z., & Lu, H. (2021). Logistics Route Optimization Based on Improved Particle Swarm Optimization. Journal of Physics: Conference Series, 1995, 1–6. https://doi.org/10.1088/1742-6596/1995/1/012044

Karami, D. (2022). Supply Chain Network Design Using Particle Swarm Optimization (PSO) Algorithm. International Journal of Industrial Engineering and Operational Research, 4(1), 1–8.

Karouani, Y., & Elgarej, M. (2022). Milk-run collection monitoring system using the internet of things based on swarm intelligence. International Journal of Information Systems and Supply Chain Management, 15(3), 1–17. https://doi.org/10.4018/IJISSCM.290018

Moghdani, R., Salimifard, K., Demir, E., & Benyettou, A. (2021). The green vehicle routing problem: A systematic literature review. Journal of Cleaner Production, 279, 123691. https://doi.org/10.1016/j.jclepro.2020.123691

Normasari, N. M. E., Yu, V. F., Bachtiyar, C., & Sukoyo. (2019). A Simulated Annealing Heuristic for the Capacitated Green Vehicle Routing Problem. Mathematical Problems in Engineering, 2019, 1–18. https://doi.org/10.1155/2019/2358258

Norouzi, N., Amalnick, M. S., & Moghaddam, R. T. (2017). Modified particle swarm optimization in a time-dependent vehicle routing problem: minimizing fuel consumption. Optimization Letters, 11(1), 121–134. https://doi.org/10.1007/s11590-015-0996-y

Poonthalir, G., & Nadarajan, R. (2018). A Fuel Efficient Green Vehicle Routing Problem with varying speed constraint (F-GVRP). Expert Systems with Applications, 100, 131–144. https://doi.org/10.1016/j.eswa.2018.01.052

Rahman, A., & Asih, H. M. (2020). Optimizing shipping routes to minimize cost using particle swarm optimization. International Journal of Industrial Optimization, 1(1), 53. https://doi.org/10.12928/ijio.v1i1.1605

Ramadhani, B. N. I. F., & Garside, A. K. (2021). Particle Swarm Optimization Algorithm to Solve Vehicle Routing Problem with Fuel Consumption Minimization. Jurnal Optimasi Sistem Industri, 20(1), 1–10. https://doi.org/10.25077/josi.v20.n1.p1-10.2021

Ranjbaran, F., Husseinzadeh Kashan, A., & Kazemi, A. (2020). Mathematical formulation and heuristic algorithms for optimisation of auto-part milk-run logistics network considering forward and reverse flow of pallets. International Journal of Production Research, 58(6), 1741–1775. https://doi.org/10.1080/00207543.2019.1617449