Enhanced Multi-Objective Green Vehicle Routing with a New Fuzzy Speed-Driven Fuel Consumption Model
DOI:
https://doi.org/10.30871/jaic.v10i1.11794Keywords:
Logistics, Fuzzy Theory, Metaheuristic, Multi-Objective Problem, Green Vehicle Routing ProblemAbstract
Today, decision-makers begun to prioritize the concept of green logistics, which is based on strategies aimed to promote more environmentally sustainable practices during vehicle routing. Among key factors influencing fuel consumption in such problems, vehicle speed plays a crucial role. This article adapts the Comprehensive Modal Emission Model (CMEM) for fuel consumption by treating vehicle speed as a fuzzy variable. This enhanced version, referred as Fuzzy-CMEM, enables the formulation of a more realistic fuzzy multi-objective Green Vehicle Routing Problem (GVRP). The proposed methodology follows four main steps. First, we formulate the problem considering the vehicle speed as a fuzzy variable. Second the initial fuzzy problem is defuzzified using the interval approximation approach. Third, a sequential approach is adopted where the sweep heuristic is used to construct feasible routes, and the BicriterionAnt metaheuristic is employed to generate optimal Pareto-front solutions of the resulting deterministic problem. Finally, a numerical simulation is addressed, followed by a comparative analysis of results and discussion.
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[1] G. B. Dantzig and J. H. Ramser, The Truck Dispatching Problem, Management Science, vol. 6, no. 1, pp. 80–91, 1959. DOI: 10.1287/mnsc.6.1.80.
[2] E.K. Nawej, J.L. Makubikwa, and J.D.B. Kampempe, New Hybrid Algorithm Based on BicriterionAnt for Solving Multi-objective Green Vehicle Routing Problem, American Journal of Operations Research, vol. 13, pp. 33–52, 2023. DOI: 10.4236/ajor.2023.133003.
[3] F. Dişkaya and S. E. Dinçer, A sectoral application for green vehicle routing problem optimization with capacity constrained and heterogeneous fleet, EKOIST Journal of Econometrics and Statistics, no. 40, pp. 183–198, 2024. DOI: 10.26650/ekoist.2024.40.1451034.
[4] T. Bektaş and G. Laporte, The Pollution-Routing Problem, Transportation Research Part B, vol. 45, no. 8, pp. 1232–1250, 2011.
[5] I. Kara, B. Y. Kara, and M. K. Yetis, Energy Minimizing Vehicle Routing Problem, in Combinatorial Optimization and Applications. Berlin, Germany: Springer, 2007, pp. 62–71.
[6] Z. H. Zhang, L. Wei, and A. Lim, An evolutionary local search for the capacitated vehicle routing problem minimizing fuel consumption under three-dimensional loading constraints, Transportation Research Part B, vol. 82, pp. 20–35, 2015.
[7] E. Marrekchi, W. Besbes, D. Dhouib, and E. Demir, A review of recent advances in the operations research literature on the green routing problem and its variants, Annals of Operations Research, vol. 304, pp. 529–574, 2021. DOI: 10.1007/s10479-021-04046-8.
[8] M. P. Trépanier and L. C. Coelho, Facteurs et méthodes de calcul d’émissions de gaz à effet de serre, CIRRELT Report, 2017.
[9] A. J. Hickman, Methodology for calculating transport emissions and energy consumption, European Cold Starts, vol. 1, pp. 69–73, 1999.
[10] M. Figliozzi, Vehicle Routing Problem for Emissions Minimization, Transportation Research Record, no. 2197, pp. 1–7, 2010.
[11] H. Fan, X. Ren, Y. Zhang, Z. Zhen, and H. Fan, A Chaotic Genetic Algorithm with Variable Neighborhood Search for Solving Time-Dependent Green VRPTW with Fuzzy Demand, Symmetry, vol. 14, 2115, 2022.
[12] M. Barth, T. Younglove, and G. Scora, Development of a Heavy Duty Diesel Modal Emissions and Fuel Consumption Model, PATH Report, UC Berkeley, 2005.
[13] M. B. Younglove and G. Scora, Development of a Heavy-Duty Diesel Modal Emissions and Fuel Consumption Model, California PATH, UC Berkeley, 2005.
[14] M. Barth and K. Boriboonsomsin, Real-world CO₂ impacts of traffic congestion, Transportation Research Record, vol. 2058, no. 1, pp. 163–171, 2008.
[15] O. Jabali, T. Van Woensel, and A. G. De Kok, Analysis of travel times and CO₂ emissions in time dependent vehicle routing, Production and Operations Management, vol. 21, no. 6, pp. 1060–1074, 2012.
[16] L. A. Zadeh, Fuzzy Sets, Information Control, vol. 8, pp. 338–353, 1965.
[17] L. A. Zadeh, The Concept of a Linguistic Variable and its Application to Approximate Reasoning-I, Information Sciences, vol. 8, pp. 199–249, 1975.
[18] S. Iredi, D. Merkle, and M. Middendorf, Bi-Criterion Optimization with Multi Colony Ant Algorithms, Lecture Notes in Computer Science. Berlin, Germany: Springer, 1993.
[19] E. Jabir, V. Panicker, and R. Sridharan, Modeling ants analysis of a green vehicle routing problem, in Proc. AIMS Int. Conf. Management, Kozhikode, India, 2015.
[20] Y. W. Liu and Z. R. Zhao, Multi-objective green multimodal transport path optimization considering carbon emissions, Computer Simulation, vol. 39, no.5, pp. 145–149, 2022.
[21] S. R. Kancharla and G. Ramadurai, Incorporating driving cycle based fuel consumption estimation in green vehicle routing problems, Sustainable Cities and Society, vol. 40, pp. 214–221, 2018.
[22] P. Gupta, K. Govindan, M. K. Mehlawat, and A. Khaitan, Multiobjective capacitated green vehicle routing problem with fuzzy time-distances and demands split into bags, International Journal of Production Research, vol. 60, no.8, pp. 2369–2385, 2022. DOI: 10.1080/00207543.2021.1888392.
[23] E. Demir, T. Bektaş, and G. Laporte, An adaptive large neighborhood search heuristic for the pollution routing problem, European Journal of Operational Research, vol. 223, pp. 346–359, 2012.
[24] Y. J. Kwon, Y. J. Choi, and D. H. Lee, Heterogeneous fixed fleet vehicle routing considering carbon emission, Transportation Research Part D, vol. 2, pp. 81–89, 2016.
[25] C. S. Liu, G. Kou, X. C. Zhou, Y. Peng, H. F. Sheng, and F. E. Alsaadi, Time-dependent vehicle routing problem with time windows of city logistics with a congestion avoidance approach, Knowledge-Based Systems, vol. 188, 104813, 2020.
[26] C. Ye, F. Liu, Y. K. Ou, and Z. Xu, Optimization of Vehicle Paths considering Carbon Emissions in a Time-Varying Road Network, Journal of Advanced Transportation, 2022. DOI: 10.1155/2022/9656262.
[27] M. Turkensteen, The accuracy of carbon emission and fuel consumption computations in green vehicle routing, European Journal of Operational Research, vol. 262, no. 2, pp. 647–659, 2017.
[28] N. El Fassi et al., A fuzzy logic-based approach for eco-driving assessment using real-world driving data, Transportation Research Part D, 2018.
[29] S. M. S. Hussain and A. L. Allaoui, A fuzzy logic approach for eco-driving control strategies, Transportation Research Part D, 2021.
[30] T. T. Nguyen et al., Fuzzy multi-objective model for energy-efficient routing of electric vehicles, Sustainable Cities and Society, 2021.
[31] E. Demir, T. Bektaş, and G. Laporte, A comparative analysis of several vehicle emission models for road freight transportation, Transportation Research Part D, vol. 16, pp. 347–357, 2011.
[32] A. Palmer, The development of an integrated routing and carbon dioxide emissions model for goods vehicles, Ph.D. dissertation, Cranfield Univ., School of Management, 2007.
[33] D. Dubois and H. Prade, Fuzzy Sets and Systems: Theory and Applications, New York, NY, USA: Academic Press, 1980.
[34] Y. Mangongo Tinda and J. D. Kampempe Busili, Some Approaches for Fuzzy Multi-objective Programming Problems, Journal of Advances in Applied Mathematics, vol.6, no.1, 2021. DOI: 10.22606/jaam.2021.61002.
[35] E. H. Mamdani and S. Assilian, An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller, International Journal of Man-Machine Studies, vol.7, pp.1–13, 1975. DOI: 10.1016/S0020-7373(75)80002-2.
[36] C. Aarthi, M. V. C. Rao, and M. S. Arumugan, Two new and useful defuzzification methods based on root mean square value, Soft Computing, vol. 10, no. 11, pp. 1047–1059, 2006.
[37] E. E. Rosyida, Sugianto, and I. B. Efendi, Capacitated Vehicle Routing Problem (CVRP) with Sweep and Nearest Neighbor Algorithm, Sinergi International Journal of Logistics, vol.2, no.1, pp.17–29, 2024.
[38] G. Laporte, The Vehicle Routing Problem: An Overview of Exact and Approximate Algorithms. European Journal of Operational Research, 59(3), 345–358, 1992.
[39] G. Scora and M. Barth, Comprehensive Modal Emissions Model (CMEM): User’s Guide, Univ. of California Riverside, 2006.
[40] Setareh, M.; Seyyed-Mahdi, H.M.; Saeed,Y.; Abbas,J., Fuzzy Green Vehicle Routing Problem with Simultaneous Pickup – Delivery and Time windows, RAIRO Operations Research, 2017, 51(2017) 1151–1176.
[41] P. Augerat, VRP-REP: the vehicle routing problem repository, Institut National Polytechnique de Grenoble, France. 1995. CVRPLIB - Plotted Instances
[42] E.K. Nawej, Y.T. Mangongo, P.K. Kafunda and J.D.B. Kampempe, A New Fuzzy Model of Multi-Objective Green Vehicle Routing Problem under Imprecise Driving Weather Conditions, LOGI -Scientific Journal on Transport and Logistics, 2025.
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Copyright (c) 2026 Emile Kayij Nawej, Yves Tinda Mangongo, Pierre Katalay Kafunda, Justin-Dupar Busili Kampempe
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