Systematic Literature Review of GPS-based Multi-Objective Environmentally Friendly Shortest Path with a Proposed Lexicographic Framework

Thania Nur Salsabila, Diah Chaerani, Herlina Napitupulu

Abstract


Environmentally friendly path planning has become an important topic in transportation research as concerns about carbon emissions continue to grow. This study presents a Systematic Literature Review (SLR) and bibliometric analysis to identify the state of the art in green shortest path optimization. Using the PRISMA guideline, the study analyzes 20 articles selected from Scopus, ScienceDirect, and Dimensions databases published between 2021 and 2025. Results indicate that the field is dominated by metaheuristic and AI-based approaches, while deterministic methods with explicit objective prioritization remain underutilized. Bibliometric visualization identifies traffic congestion and carbon emission policies as major research clusters, yet few studies integrate these with real-time GPS data in developing countries. Based on these findings, this paper proposes a conceptual framework for a GPS-based Lexicographic Multi-Objective Optimization model. The proposed framework prioritizes carbon emission minimization as the primary objective, followed by travel time, offering a transparent decision-making tool for sustainable urban transportation.

Keywords


Green Shortest Path; Multi-Objective Shortest Path Problem; Lexicographic Method; Systematic Literature Review; Bibliometric Analysis.

Full Text:

PDF

References


[1] Singiresu S. Rao. Engineering Optimization: Theory and Practice. 5th. Hoboken, NJ: John Wiley & Sons, 2020.

[2] H. Napitupulu, E. Carnia, N. Anggriani, and A. K. Supriatna. “Centrality measures in transportation networks for Unpad campus route”. In: Journal of Physics: Conference Series 1722.1 (2021), p. 012063. doi: 10.1088/1742-6596/1722/1/012063.

[3] John Current and Michael Marsh. “Multiobjective transportation network design and routing problems: Taxonomy and annotation”. In: European Journal of Operational Research 65.1 (1993), pp. 4–19. doi: 10.1016/0377-2217(93)90140-I.

[4] C. C. Azis, D. Chaerani, and E. Rusyaman. “Analysis of multi-objective linear robust optimization model with lexicographical method”. In: Media Statistika 17.1 (2024), pp. 57–68. doi: 10.14710/medstat.17.1.57-68.

[5] D. Chaerani, S. R. Adawiyah, and E. Lesmana. “Robust Optimization Model for Bi-objective Emergency Medical Service Design Problem with Demand Uncertainty”. In: Jurnal Teknik Industri 20.2 (2018), pp. 95–104. doi: 10.9744/jti.20.2.95-104.

[6] K. Thakur, S. Maity, P. Nielsen, T. Pal, and M. Maiti. “A 3D multiobjective multi-item eco-routing problem for refrigerated fresh products delivery using NSGA-II with hybrid chromosome”. In: Computers & Industrial Engineering 198 (2024), p. 110644. doi: 10.1016/j.cie.2024.110644.

[7] A. Mahmoodi, L. Hashemi, J. Laliberte, and S. M. Sajadi. “Optimizing energy and CO2 efficiency in last-mile delivery using hybrid fleet models”. In: Sustainable Futures 10 (2025), p. 101089. doi: 10.1016/j.sftr.2025.101089.

[8] H. Sun, M. He, Y. Gai, and J. Cao. “Optimization of Fresh Food Logistics Routes for Heterogeneous Fleets in Segmented Transshipment Mode”. In: Mathematics 12.23 (2024), p. 3831. doi: 10.3390/math12233831.

[9] A. Sabet and B. Farooq. “Exploring the combined effects of major fuel technologies, eco-routing, and eco-driving for sustainable traffic decarbonization in downtown Toronto”. In: Transportation Research Part A: Policy and Practice 192 (2025), p. 104385. doi: 10.1016/j.tra.2025.104385.

[10] S. M. Nasution, E. Husni, K. Kuspriyanto, and R. Yusuf. “Heterogeneous Traffic Condition Dataset Collection for Creating Road Capacity Value”. In: Big Data and Cognitive Computing 7.1 (2023), p. 40. doi: 10.3390/bdcc7010040.

[11] Institute for Essential Services Reform (IESR). Wilayah Perkotaan di Pulau Jawa Menjadi Kontributor Tertinggi Emisi Karbon Individu. 2025. https://iesr.or.id/wilayah-perkotaan-di-pulau-jawa-menjadi-kontributor-tertinggi-emisi-karbon-individu/.

[12] A. W. Deendarlianto, T. Widodo, I. Handika, I. C. Setiawan, and A. Lindasista. “Modelling of Indonesian road transport energy sector in order to fulfill the national energy and oil reduction targets”. In: Renewable Energy 146.2 (2020), pp. 504–518. doi: 10.1016/j.renene.2019.06.169.

[13] Kementerian Lingkungan Hidup dan Kehutanan Republik Indonesia. Long-Term Strategy for Low Carbon and Climate Resilience 2050. 2021. https://unfccc.int/sites/default/files/resource/Indonesia_LTS-LCCR_2021.pdf.

[14] B. Kitchenham and S. Charters. Guidelines for Performing Systematic Literature Reviews in Software Engineering. Technical Report EBSE 2007-001. Joint Report. Keele, UK: Keele University and Durham University, 2007.

[15] D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman. “Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement”. In: Annals of Internal Medicine 151.4 (Aug. 2009). Epub 2009 Jul 20, pp. 264–269. doi: 10.7326/0003-4819-151-4-200908180-00135.

[16] A. Pritchard. “Statistical bibliography or bibliometrics”. In: Journal of Documentation 25.4 (1969), pp. 348–349.

[17] N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim. “How to conduct a bibliometric analysis: An overview and guidelines”. In: Journal of Business Research 133 (2021), pp. 285–296. doi: 10.1016/j.jbusres.2021.04.070.

[18] N. J. Van Eck and L. Waltman. “Software survey: VOSviewer, a computer program for bibliometric mapping”. In: Scientometrics 84.2 (2010), pp. 523–538. doi: 10.1007/s11192-009-0146-3.

[19] Marsudi. Teori Graf. Malang: UB Press, 2016.

[20] A Hickman, D Hassel, R Joumard, Z Samaras, and S Sorenson. Methodology for calculating transport emissions and energy consumption. Project Report SE/491/98. Crowthorne, Berkshire, UK: Transport Research Laboratory (TRL), 1999.

[21] E. Demir, T. Bektas, and G. Laporte. “A Review of Recent Research on Green Road Freight Transportation”. In: European Journal of Operational Research 229.3 (2013), pp. 775–793. doi: 10.1016/j.ejor.2013.12.033.

[22] Alexander Schrijver. Combinatorial Optimization. Springer, 2017.

[23] I. Zupic and T. Ćater. “Bibliometric Methods in Management and Organization”. In: Organizational Research Methods 18.3 (2015), pp. 429–472. doi: 10.1177/1094428114562629.

[24] M. E. Falagas, E. I. Pitsouni, G. A. Malietzis, and G. Pappas. “Comparison of PubMed, Scopus, Web of Science, and Google Scholar: Strengths and Weaknesses”. In: The FASEB Journal 22.2 (2008), pp. 338–342. doi: 10.1096/fj.07-9492LSF.

[25] V. Pradeep, R. Khemmar, and F. Jendoubi. “Visual Eco-Routing (VER): XGBoost Based Eco-Route Selection from Road Scenes and Vehicle Emissions”. In: IEEE Access 12 (2024), pp. 9669–9681. doi: 10.1109/ACCESS.2024.3353036.

[26] Adel Elgammal. “Integrated Path Planning and Speed Control for Electric Vehicles Using MOPSO-Based Optimization”. In: International Journal of Innovative Science and Research Technology 10.5 (2025). doi: 10.38124/ijisrt/25may1547.

[27] C. Zhu and X. Zhu. “Multi-Objective Path-Decision Model of Multimodal Transport Considering Uncertain Conditions and Carbon Emission Policies”. In: Symmetry 14.2 (2022), p. 221. doi: 10.3390/sym14020221.

[28] X. Yang and H. Jiang. “Research on Urban Cold Chain Logistics Path Optimization Considering Multi-Center and Time-Varying Road Networks”. In: IEEE Access 12 (2024), pp. 71331–71348. doi: 10.1109/ACCESS.2024.3402833.

[29] L. Li, H. Liang, J. Wang, J. Yang, and Y. Li. “Online Routing for Autonomous Vehicle Cruise Systems with Fuel Constraints”. In: Journal of Intelligent & Robotic Systems 104.4 (2022), p. 68. doi: 10.1007/s10846-021-01530-y.

[30] D. Saxena, N. Singh, K. Gupta, A. Verma, V. Mishra, J. Kumar, and A. K. Singh. “An Intelligent Multi-Depot Vehicle Routing and Management Model for Smart Cities”. In: IEEE Transactions on Intelligent Transportation Systems 26.6 (2025), pp. 7740–7754. doi: 10.1109/TITS.2025.3557826.

[31] L. Yang, C. Zhang, and X. Wu. “Multi-Objective Path Optimization of Highway-Railway Multimodal Transport Considering Carbon Emissions”. In: Applied Sciences 13.8 (2023), p. 4731. doi: 10.3390/app13084731.

[32] J. Wu, Q. Luo, and Y. Zhou. “Modified Dung Beetle Optimizer with Multi-Strategy for Uncertain Multi-Modal Transport Path Problem”. In: Journal of Computational Design and Engineering 11.4 (2024), pp. 40–72. doi: 10.1093/jcde/qwae058.

[33] F. Cheng and S. Jia. “Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions”. In: Applied Sciences 14.21 (2024), p. 9956. doi: 10.3390/app14219956.

[34] Y. Benmessaoud, L. Cherrat, and M. Ezziyyani. “Real-Time Self-Adaptive Traffic Management System for Optimal Vehicular Navigation in Modern Cities”. In: Computers 12.4 (2023), p. 80. doi: 10.3390/computers12040080.

[35] C. Qi. “Multi-Objective Optimization-Based Algorithm for Selecting the Optimal Path of Rural Multi-Temperature Zone Cold Chain Dynamic Logistics Intermodal Transportation”. In: International Journal of Computational Intelligence Systems 17.1 (2024), p. 224. doi: 10.1007/s44196-024-00616-3.

[36] Qian Linyi. “Research on path selection system based on green transportation”. In: E3S Web of Conferences. Ed. by EILCD. Vol. 275. EDP Sciences, 2021, p. 02043. doi: 10.1051/e3sconf/202127502043. https://doi.org/10.1051/e3sconf/202127502043.

[37] N. Yin. “Research on Green Logistics Distribution Path Based on Genetic Algorithm”. In: Procedia Computer Science 261 (2025), pp. 1036–1042. doi: 10.1016/j.procs.2025.04.682.

[38] C. Ren, L. Lu, J. Teng, C. Yin, J. Li, H. Ji, and F. Fu. “Logistics Distribution Path Optimization Considering Carbon Emissions and Multifuel-Type Vehicles”. In: Journal of Advanced Transportation 2025 (2025), p. 6668589. doi: 10.1155/ATR/6668589.

[39] Y. Zhang, G. Shi, and J. Liu. “Dynamic Energy-Efficient Path Planning of Unmanned Surface Vehicle Under Time-Varying Current and Wind”. In: Journal of Marine Science and Engineering 10.6 (2022), p. 759. doi: 10.3390/jmse10060759.

[40] X. Hu, K. Hu, D. Tao, Y. Zhong, and Y. Han. “GIS-Data-Driven Efficient and Safe Path Planning for Autonomous Ships in Maritime Transportation”. In: Electronics 12.10 (2023), p. 2206. doi: 10.3390/electronics12102206.

[41] X. Lu, J. Wang, C. W. Yuen, and Q. Liu. “Multi-Objective Intercity Carpooling Route Optimization Considering Carbon Emission”. In: Sustainability 15.3 (2023), p. 2261. doi: 10.3390/su15032261.

[42] A. A. Ibrahim, D. Leite, and C. De Bacco. “Sustainable Optimal Transport in Multilayer Networks”. In: Physical Review E 105.6 (2022), p. 06432. doi: 10.1103/PhysRevE.105.064302.

[43] W. Xu, Q. Liu, M. Chen, and H. Zeng. “Ride the Tide of Traffic Conditions: Opportunistic Driving Improves Energy Efficiency of Timely Truck Transportation”. In: IEEE Transactions on Intelligent Transportation Systems 24.5 (2023), pp. 4777–4793. doi: 10.1109/TITS.2023.3244757.

[44] Y. Kisialiou, A. Rialland, and V. Gribkovskaia. “Ship Model-Based Route Optimisation for Decision Support in Deep Sea Shipping”. In: Journal of Physics: Conference Series. Vol. 2867. 1. IOP Publishing, 2024, p. 012012. doi: 10.1088/1742-6596/2867/1/012012.




DOI: https://doi.org/10.18860/cauchy.v11i1.40489

Refbacks

  • There are currently no refbacks.


Copyright (c) 2026 Thania Nur Salsabila, Diah Chaerani, Herlina Napitupulu

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Editorial Office
Mathematics Department,
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Gajayana Street 50 Malang, East Java, Indonesia 65144
Faximile (+62) 341 558933
e-mail: cauchy@uin-malang.ac.id

Creative Commons License
CAUCHY: Jurnal Matematika Murni dan Aplikasi is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.