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A Hybrid Genetic Algorithm for Multi-Trip Green Capacitated Arc Routing Problem in the Scope of Urban Services

Author

Listed:
  • Erfan Babaee Tirkolaee

    (Department of Industrial Engineering, Mazandaran University of Science and Technology, 47166-85635 Babol, Iran)

  • Ali Asghar Rahmani Hosseinabadi

    (Young Researchers and Elite Club, Ayatollah Amoli Branch, Islamic Azad University, 46351-43358 Amol, Iran)

  • Mehdi Soltani

    (Department of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, 34185-1416 Qazvin, Iran)

  • Arun Kumar Sangaiah

    (School of Computing Science and Engineering, Vellore Institute of Technology (VIT), 632014 Vellore, India)

  • Jin Wang

    (School of Computer & Communication Engineering, Changsha University of Science & Technology, 410004 Changsha, China)

Abstract

Greenhouse gases (GHG) are the main reason for the global warming during the past decades. On the other hand, establishing a well-structured transportation system will yield to create least cost-pollution. This paper addresses a novel model for the multi-trip Green Capacitated Arc Routing Problem (G-CARP) with the aim of minimizing total cost including the cost of generation and emission of greenhouse gases, the cost of vehicle usage and routing cost. The cost of generation and emission of greenhouse gases is based on the calculation of the amount of carbon dioxide emitted from vehicles, which depends on such factors as the vehicle speed, weather conditions, load on the vehicle and traveled distance. The main applications of this problem are in municipalities for urban waste collection, road surface marking and so forth. Due to NP-hardness of the problem, a Hybrid Genetic Algorithm (HGA) is developed, wherein a heuristic and simulated annealing algorithm are applied to generate initial solutions and a Genetic Algorithm (GA) is then used to generate the best possible solution. The obtained numerical results indicate that the proposed algorithm could present desirable performance within a suitable computational run time. Finally, a sensitivity analysis is implemented on the maximum available time of the vehicles in order to determine the optimal policy.

Suggested Citation

  • Erfan Babaee Tirkolaee & Ali Asghar Rahmani Hosseinabadi & Mehdi Soltani & Arun Kumar Sangaiah & Jin Wang, 2018. "A Hybrid Genetic Algorithm for Multi-Trip Green Capacitated Arc Routing Problem in the Scope of Urban Services," Sustainability, MDPI, vol. 10(5), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:5:p:1366-:d:143612
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    References listed on IDEAS

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    Cited by:

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    3. Amine Masmoudi, M. & Mancini, Simona & Baldacci, Roberto & Kuo, Yong-Hong, 2022. "Vehicle routing problems with drones equipped with multi-package payload compartments," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    4. Xiaoqiu Shi & Wei Long & Yanyan Li & Dingshan Deng, 2020. "Multi-population genetic algorithm with ER network for solving flexible job shop scheduling problems," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-23, May.
    5. Jin Wang & Yu Gao & Wei Liu & Arun Kumar Sangaiah & Hye-Jin Kim, 2019. "An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 15(3), pages 15501477198, March.
    6. Emna Marrekchi & Walid Besbes & Diala Dhouib & Emrah Demir, 2021. "A review of recent advances in the operations research literature on the green routing problem and its variants," Annals of Operations Research, Springer, vol. 304(1), pages 529-574, September.
    7. Ping Liu & Jin Wang & Arun Kumar Sangaiah & Yang Xie & Xinchun Yin, 2019. "Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment," Sustainability, MDPI, vol. 11(7), pages 1-14, April.
    8. Erfan Babaee Tirkolaee & Alireza Goli & Selma Gütmen & Gerhard-Wilhelm Weber & Katarzyna Szwedzka, 2023. "A novel model for sustainable waste collection arc routing problem: Pareto-based algorithms," Annals of Operations Research, Springer, vol. 324(1), pages 189-214, May.

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