Multi-Objective Modeling of Green Vehicle Routing Problem Using a Hybrid Extreme Learning Machine (ELM) and Genetic Programming (GP)

Document Type : Research/ Original/ Regular Article

Authors

1 Faculty of Industrial and Systems Engineering, Amirkabir University of Technology (Polytechnic)

2 Faculty of Industrial Engineering, Islamic Azad University Science and Research Branch

3 Iranian Research Institute for Information Science & Technology (IRANDOC)

4 Islamic Azad University Science and Research Branch

5 Faculty of Industrial Engineering, Pars University

Abstract

Transportation plays a significant role in the gross domestic product and oil consumption of every nation. In our country, a combination of recent sanctions and underdeveloped rail, air, and sea transportation systems has led to an increased reliance on road transport. Unfortunately, road transport contributes significantly to the emission of greenhouse gases, particularly carbon dioxide. Nevertheless, transportation is a vital aspect of logistics, and addressing pollution in vehicle routing stands as a paramount concern within this realm.This paper introduces a model aimed at optimizing fuel consumption costs, considering various factors such as vehicle load, speed, pollution, as well as parameters like fuel and engine efficiency, incline, traffic density, wind speed and direction, air temperature, asphalt quality, and driver remuneration. Additionally, this mathematical linear mixed-integer model incorporates probabilistic demand and a distribution system involving both delivery and pickup processes, all geared towards cost minimization.By employing this model, organizations can achieve more precise cost estimates, enhanced analysis, and improved planning. Given the NP-hard nature of the problem, its resolution involves the amalgamation of two meta-heuristic algorithms: Extreme Learning Machine (ELM) and Genetic Programming (GP). Experimental results indicate that the developed hybrid algorithm offers highly accurate estimations in a remarkably short time span when compared with similar algorithms.

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References

[1] Z. Rahimi Rise, et al. “Scenario-based analysis about   COVID-19 outbreak in Iran using systematic dynamics modeling-with a focus on the transportation system”. Journal of Transportation Research, vol. 17(2), pp. 33-48, 2020. https://doi.org/10.1080/17477778.2021.2015260
[2] M. M. Ershadi, and H. S. Shemirani, “A multi-objective optimization model for logistic planning in the crisis response phase”. Journal of Humanitarian Logistics and Supply Chain Management, vol. 12(1), pp. 30-53, 2022. https://doi.org/10.1108/JHLSCM-11-2020-0108
[3] Duan, Gang, and Kaibin Zhang, "Optimization on hybrid energy vessel routing and energy management for floating marine debris cleanup." Transportation Research Part C: Emerging Technologies, vol 138, pp. 103649, 2022. https://doi.org/10.1016/j.trc.2022.103649
[4] A. Martin, et al. “Utilizing waste heat gasoline engine in the design and fabrication of a fin and tube evaporator for the Organic Rankine Cycle (ORC)”. SINERGI, vlo. 27(2), pp. 171-178, 2023. https://doi.org/10.22441/sinergi.2023.2.004
[5] A. Derikvand, , et al. “Indoor Air Quality in the Most Crowded Public Places of Tehran: An Inhalation Health Risk Assessment”. Atmosphere, vol. 14(7), pp. 1080, 2023. https://doi.org/10.3390/atmos14071080
[6] D. Scott, et al. “A review of the IPCC Sixth Assessment and implications for tourism development and sectoral climate action”. Journal of Sustainable Tourism, vol. 32, pp. 1-18, 2023. https://doi.org/10.1080/09669582.2023.2195597
[7] Z. Zhang, et al. “Review on the impacts of cooperative automated driving on transportation and environment”. Transportation Research Part D: Transport and Environment, vol. 115, pp. 103607, 2023. https://doi.org/10.1016/j.trd.2023.103607
[8] Y. Wu, et al. “Integrating operations research into green logistics: A review”. Frontiers of Engineering Management, vol. 36, pp.1-17, 2023. https://doi.org/10.1007/s42524-023-0265-1
[9] S. Bashar, et al. “Adoption of green supply chain management in developing countries: role of consumer cooperation, eco-design, and green marketing”. Environmental Science and Pollution Research, vol. 40(30), pp.92594-92610, 2023. https://doi.org/10.1007/s11356-023-28881-3
[10] A. Mohtashmi, et al. “Designing a mathematical model of green routing in multiple cross docking systems with the approach of reducing carbon dioxide gas,” Journal of industrial engineering research in production systems, vol. 7(14), pp. 59-77, 2018. https://doi.org/10.22084/ier.2019.17125.1787 (in Persian)
[11] R. Eshtehadi, et al. “Robust solutions to the pollution-routing problem with demand and travel time uncertainty”. Transportation Research Part D: Transport and Environment, vol. 51, pp. 351-363, 2017. https://doi.org/10.1016/j.trd.2017.01.003
[12] G. Poonthalir and R. Nadarajan, “A fuel-efficient green vehicle routing problem with varying speed constraint (F-GVRP)”. Expert Systems with Applications, vol. 100, pp. 131-144, 2018. https://doi.org/10.1016/j.eswa.2018.01.052
[13] D. Zhang, et al. “Joint optimization of green vehicle scheduling and routing problem with time-varying speeds”. PloS one, vol. 13(2), pp. 192000, 2018. https://doi.org/10.1371/journal.pone.0192000
[14] M. Affi, et al. “Variable neighborhood search algorithm for the green vehicle routing problem”. International Journal of Industrial Engineering Computations, vol. 9(2), pp. 195-204, 2018. http://dx.doi.org/10.5267/j.ijiec.2017.6.004
[15] Y. Niu, et al. “A hybrid tabu search algorithm for a real-world open vehicle routing problem involving fuel consumption constraints”. Complexity, vol. 19(6), pp. 1-12, 2018. https://doi.org/10.1155/2018/5754908
[16] H. Abad, et al. “A bi-objective model for pickup and delivery pollution-routing problem with integration and consolidation shipments in cross-docking system”. Journal of Cleaner Production, vol. 193, pp. 784-801, 2018. https://doi.org/10.1016/j.jclepro.2018.05.046
[17] J. C. Ferreira, et al. “Multi-objective optimization for the green vehicle routing problem: A systematic literature review and future directions”. Cogent Engineering, vol. 7(1), pp. 1807082, 2020. https://doi.org/10.1080/23311916.2020.1807082
[18] L. He, et al. “The impacts from cold start and road grade on real-world emissions and fuel consumption of gasoline, diesel and hybrid-electric light-duty passenger vehicles”. Science of The Total Environment, vol. 851, pp. 158045, 2022. https://doi.org/10.1016/j.scitotenv.2022.158045
[19] D. M. Utama, et al. “A novel hybrid jellyfish algorithm for minimizing fuel consumption capacitated vehicle routing problem”. Bulletin of Electrical Engineering and Informatics, vol. 11(3), pp. 1272-1279, 2022. https://doi.org/10.11591/eei.v11i3.3263
[20] Z. Liu, et al. “The pollution-routing problem with one general period of congestion”. Journal of Modelling in Management, vol. 18(5), pp. 1529-1560, 2022. https://doi.org/10.1108/JM2-12-2021-0290
[21] Koç, Ç., et al. “The fleet size and mix pollution-routing problem”. Transportation Research Part B: Methodological, vol.  70, pp. 239-254, 2014. https://doi.org/10.1016/j.trb.2014.09.008
[22] W. Shi, et al. “A Bi-Objective Pollution Routing Optimisation Problem with Decentralised Cooperation and Split Delivery”. IEEE Transactions on Intelligent Transportation Systems, vol. 24(11), pp. 12357-12371, 2023. https://doi.org/10.1109/TITS.2023.3293507
[23] R. Kramer, et al. “A matheuristic approach for the pollution-routing problem”. European Journal of Operational Research, vol. 243(2), pp. 523-539, 2015. https://doi.org/10.1016/j.ejor.2014.12.009
[24] R. S. Kumar, et al. “Multi-objective modeling of production and pollution routing problem with time window: A self-learning particle swarm optimization approach”. Computers & Industrial Engineering, vol. 99, pp. 29-40, 2016. https://doi.org/10.1016/j.cie.2015.07.003
[25] A. Boru İpek, “Multi-Objective Simulation Optimization Integrated with Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution for Pollution Routing Problem”. Transportation Research Record, vol. 2677(1), pp. 1658-1674, 2023. https://doi.org/10.1177/03611981221105503
[26] C. M. Chen, et al. “A Genetic Algorithm for the Waitable Time-Varying Multi-Depot Green Vehicle Routing Problem”. Symmetry, vol. 15(1), pp. 124, 2023.  https://doi.org/10.3390/sym15010124
[27] Ma, B., et al. “Time-dependent vehicle routing problem with departure time and speed optimization for shared autonomous electric vehicle service”. Applied Mathematical Modelling, vol. 113, pp. 333-357, 2023. https://doi.org/10.1016/j.apm.2022.09.020
[27] Fatemi Qomi, et al. “Solving a location-routing transportation problem considering green transportation routes using a meta-heuristic algorithm”. Scientific Journal of Supply Chain Management, vol. 24(75), pp. 1-12, 2023. https://dorl.net/dor/20.1001.1.20089198.1401.24.75.1.0 (in Persian)
[29] N. Manavizadeh, et al. “A New Mathematical Model for the Green Vehicle Routing Problem by Considering a Bi-Fuel Mixed Vehicle Fleet”. Journal of Optimization in Industrial Engineering, vol. 13(2), pp. 165-183, 2020. https://doi.org/10.22094/JOIE.2020.1871922.1667
[30] M. Yousefi Khoshpat, et al. “An ant-optimized hybrid algorithm for solving the open-capacity vehicle routing problem”. Modeling in Engineering, vol. 15(50), pp. 179-191, 2016. https://doi.org/22075/10/jme.2560/2017
[31] S. M. Hosseini Motlaq, et al. “Presenting a mathematical model and an innovative solution method for the two-level positioning-routing problem considering the conditions of placing and picking up in the state of uncertainty”. Modeling in Engineering, vol. 16(53), pp. 339-361, 2017. https://doi.org/22075/10/jme.5869/2017 (in Persian)
[32] E. Babaei Tirklai, et al. “Solving the vehicle routing problem considering multiple trips and time windows in urban waste management using the gray wolf optimization algorithm”. Modeling in Engineering, vol. 17(57), pp. 93-110, 2018. https://doi.org/22075/10/jme.16445/2019.1633 (in Persian)
[33] A. Abdi, et al. “Examining the probabilistic multi-objective model for the sustainable closed-loop supply chain problem considering vehicle routing using new and meta-heuristic hybrid algorithms”. Modeling in Engineering, vol. 17(59), pp. 67-85, 2018. https://doi.org/22075/10/jme.14151/2019.1389 (in Persian)
[34] M. Ehsani, et al. “Modeling of vehicle fuel consumption and carbon dioxide emission in road transport”. Renewable and sustainable energy reviews, vol. 53, pp. 1638-1648, 2016. https://doi.org/10.1016/j.rser.2015.08.062
[35] T. Bektaş and G. Laporte, “The pollution-routing problem”. Transportation Research Part B: Methodological, vol. 45(8), pp. 1232-1250, 2011. https://doi.org/10.1016/j.trb.2011.02.004
[36] G. B. Huang, et al. “Extreme learning machine: theory and applications”. Neurocomputing, vol. 70(1-3), pp. 489-501, 2006. https://doi.org/10.1016/j.neucom.2005.12.126
[37] X. D. Zhang, “A Matrix Algebra Approach to Artificial Intelligence”. Springer Singapore, eBook ISBN 978-981-15-2770-8, 2020. https://doi.org/10.1007/978-981-15-2770-8
[38] J. R. Koza, “Genetic programming as a means for programming computers by natural selection”. Statistics and computing, vol. 4(2), pp. 87-112, 1994. https://doi.org/10.1007/BF00175355
[39] S. I. Tamura and M. Tateishi, “Capabilities of a four-layered feedforward neural network: four layers versus three”. IEEE Transactions on Neural Networks, vol. 8(2), pp. 251-255, 1997. https://doi.org/10.1109/72.557662
[40] T. R. P. Ramos, et al. “Minimizing CO2 emissions in a recyclable waste collection system with multiple depots”. In EUROMA/POMS joint conference, pp. 1-5, 2012. https://doi.org/10.1016/j.tre.2013.12.002
[41] K. Gurney, “An introduction to neural networks”. CRC press, eBook-ISBN 9781315273570, 2018. https://doi.org/10.1201/9781315273570.
[42] X. Wang and M. Han, “Multivariate time series prediction based on multiple kernel extreme learning machine”. In 2014 International joint conference on neural networks (IJCNN), pp. 198-201, 2014. https://doi.org/10.1109/IJCNN.2014.6889479
[43] R. K. Roy, “Taguchi method. Society of Manufacturing Engineers”, Society of Manufacturing Engineers, 2010. https://doi.org/10.1007/978-94-009-1355-4_30

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History

  • Receive Date: 29 April 2023
  • Revise Date: 28 August 2023
  • Accept Date: 25 October 2023

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