Optimized Approach to Electric Vehicle Routing Problem with Time Windows Using Grasshopper Optimization Algorithm

Adifa Yasin Aksyarafah; Nughthoh Arfawi Kurdhi

doi:10.37905/jjom.v7i1.30664

Optimized Approach to Electric Vehicle Routing Problem with Time Windows Using Grasshopper Optimization Algorithm

Adifa Yasin Aksyarafah, Nughthoh Arfawi Kurdhi

Abstract

The Electric Vehicle Routing Problem with Time Windows (EVRPTW) is a complex logistics issue that involves optimizing delivery routes for electric vehicles while adhering to strict time limits, managing limited battery capacity, and addressing recharging needs. In this research, we introduce an optimized method to tackle the EVRPTW using the Grasshopper Optimization Algorithm (GOA), a metaheuristic inspired by the swarming behavior of grasshoppers. We utilize the Solomon dataset, a recognized benchmark in logistics and vehicle routing, to assess the effectiveness of our proposed algorithm. Our focus is on minimizing the total distance traveled while ensuring timely deliveries and effectively managing battery logistics and recharging. Comparative analysis indicates that the GOA surpasses traditional methods in route efficiency, reducing travel distances, and enhancing logistical operations. This study highlights the potential of GOA as a valuable tool for overcoming the challenges associated with electric vehicle logistics, paving the way for more sustainable and efficient transportation systems.

Keywords

Electric vehicle routing problem; Time window; Grasshopper optimization algorithm; Metaheuristic

Full Text:

PDF

References

M. F. N. Maghfiroh, A. H. Pandyaswargo, and H. Onoda, “Current readiness status of electric vehicles in indonesia: Multistakeholder perceptions,” Sustain., vol. 13, no. 23, pp. 1–25, 2021, doi: 10.3390/su132313177.

B. P. Resosudarmo, D. A. Nurdianto, and A. A. Yusuf, “Greenhouse Gas Emission in Indonesia: The Significance of Fossil Fuel Combustion,” Reg. Dev. Energy Environ. Indones., pp. 146–159, 2009.

A. Ajanovic and R. Haas, “Electric vehicles : solution or new problem ?,” Environ. Dev. Sustain., vol. 20, no. s1, pp. 7–22, 2018, doi: 10.1007/s10668-018-0190-3.

I. Gunawan, A. A. N. P. Redi, A. A. Santosa, M. F. N. Maghfiroh, A. H. Pandyaswargo, and A. C. Kurniawan, “Determinants of Customer Intentions to Use Electric Vehicle in Indonesia: An Integrated Model Analysis,” Sustain., vol. 14, no. 4, pp. 1–22, 2022, doi: 10.3390/su14041972.

N. Touati-Moungla and V. Jost, “Combinatorial optimization for electric vehicles management,” Renew. Energy Power Qual. J., vol. 1, no. 9, pp. 942–947, 2011, doi: 10.24084/repqj09.504.

C. Sudjoko, N. A. Sasongko, I. Utami, and A. Maghfuri, “Utilization of electric vehicles as an energy alternative to reduce carbon emissions,” IOP Conf. Ser. Earth Environ. Sci., vol. 926, no. 1, 2021, doi: 10.1088/1755-1315/926/1/012094.

M. S. Hossain, L. Kumar, M. M. Islam, and J. Selvaraj, “A Comprehensive Review on the Integration of Electric Vehicles for Sustainable Development,” J. Adv. Transp., vol. 2022, 2022, doi: 10.1155/2022/3868388.

I. Kucukoglu, R. Dewil, and D. Cattrysse, “The electric vehicle routing problem and its variations: A literature review,” Comput. Ind. Eng., vol. 161, no. July, p. 107650, 2021, doi: 10.1016/j.cie.2021.107650.

M. E. Yuniza, I. W. B. E. Pratama, and R. C. Ramadhaniati, “Indonesia’s incentive policies on electric vehicles: The questionable effort from the government,” Int. J. Energy Econ. Policy, vol. 11, no. 5, pp. 434–440, 2021, doi: 10.32479/ijeep.11453.

H. Yu and A. L. Stuart, “Impacts of compact growth and electric vehicles on future air quality and urban exposures may be mixed,” Sci. Total Environ., vol. 576, pp. 148–158, 2017, doi: 10.1016/j.scitotenv.2016.10.079.

Y. Xiao, Y. Zhang, I. Kaku, R. Kang, and X. Pan, “Electric vehicle routing problem: A systematic review and a new comprehensive model with nonlinear energy recharging and consumption,” Renew. Sustain. Energy Rev., vol. 151, no. October 2020, p. 111567, 2021, doi: 10.1016/j.rser.2021.111567.

V. C. Vinod and A. H. S, “Nature inspired meta heuristic algorithms for optimization problems,” Computing, vol. 104, no. 2, pp. 251–269, 2022, doi: 10.1007/s00607-021-00955-5.

K. Rajwar, K. Deep, and S. Das, An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges, vol. 56, no. 11. Springer Netherlands, 2023. doi: 10.1007/s10462-023-10470-y.

R. Elshaer and H. Awad, “A taxonomic review of metaheuristic algorithms for solving the vehicle routing problem and its variants,” Comput. Ind. Eng., vol. 140, no. November 2018, p. 106242, 2020, doi: 10.1016/j.cie.2019.106242.

D. Woller, V. Kozák, and M. Kulich, “The GRASP Metaheuristic for the Electric Vehicle Routing Problem,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12619 LNCS, pp. 189–205, 2021, doi: 10.1007/978-3-030-70740-8_12.

S. Saremi, S. Mirjalili, and A. Lewis, “Grasshopper Optimisation Algorithm: Theory and application,” Adv. Eng. Softw., vol. 105, pp. 30–47, 2017, doi: 10.1016/j.advengsoft.2017.01.004.

B. Çatay, “A new saving-based ant algorithm for the Vehicle Routing Problem with simultaneous Pickup and Delivery,” Expert Syst. Appl., vol. 37, no. 10, pp. 6809–6817, 2010, doi: 10.1016/j.eswa.2010.03.045.

M. M. Solomon, “Algorithms for the Vehicle Routing and Scheduling Problems With Time Window Constraints.,” Oper. Res., vol. 35, no. 2, pp. 254–265, 1987, doi: 10.1287/opre.35.2.254.

DOI: https://doi.org/10.37905/jjom.v7i1.30664