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Heuristic time-dependent personal scheduling problem with electric vehicles

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Listed:
  • Dimitrios Rizopoulos

    (Budapest University of Technology and Economics)

  • Domokos Esztergár-Kiss

    (Budapest University of Technology and Economics)

Abstract

In this paper, a heuristic method which contributes to the solution of the Daily Activity Chains Optimization problem with the use of Electric Vehicles (DACO-EV) is presented. The DACO-EV is a time-dependent activity-scheduling problem of individual travelers in urban environments. The heuristic method is comprised of a genetic algorithm that considers as its parameters a set of preferences of the travelers regarding their initial activity chains as well as parameters concerning the transportation network and the urban environment. The objective of the algorithm is to calculate the traveler’s optimized activity chains within a single day as they emerge from the improved combinations of the available options for each individual traveler based on their flexibility preferences. Special emphasis is laid on the underlying speed-up techniques of the GA and the mechanisms that account for specific characteristics of EVs, such as consumption according to the EV model and international standards, charging station locations, and the types of charging plugs. From the results of this study, it is proven that the method is suitable for efficiently aiding travelers in the meaningful planning of their daily activity schedules and that the algorithm can serve as a tool for the analysis and derivation of the insights into the transportation network itself.

Suggested Citation

  • Dimitrios Rizopoulos & Domokos Esztergár-Kiss, 2023. "Heuristic time-dependent personal scheduling problem with electric vehicles," Transportation, Springer, vol. 50(5), pages 2009-2048, October.
    • Handle: RePEc:kap:transp:v:50:y:2023:i:5:d:10.1007_s11116-022-10300-0
    DOI: 10.1007/s11116-022-10300-0
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    References listed on IDEAS

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    1. Stergios Statharas & Yannis Moysoglou & Pelopidas Siskos & Georgios Zazias & Pantelis Capros, 2019. "Factors Influencing Electric Vehicle Penetration in the EU by 2030: A Model-Based Policy Assessment," Energies, MDPI, vol. 12(14), pages 1-25, July.
    2. Jee Eun Kang & Will Recker, 2015. "Strategic Hydrogen Refueling Station Locations with Scheduling and Routing Considerations of Individual Vehicles," Transportation Science, INFORMS, vol. 49(4), pages 767-783, November.
    3. Bowman, J. L. & Ben-Akiva, M. E., 2001. "Activity-based disaggregate travel demand model system with activity schedules," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(1), pages 1-28, January.
    4. David Charypar & Kai Nagel, 2005. "Generating complete all-day activity plans with genetic algorithms," Transportation, Springer, vol. 32(4), pages 369-397, July.
    5. Zhou, Guangyou & Zhu, Zhiwei & Luo, Sumei, 2022. "Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm," Energy, Elsevier, vol. 247(C).
    6. Domokos Esztergár-Kiss & Zoltán Rózsa & Tamás Tettamanti, 2018. "Extensions of the Activity Chain Optimization Method," Journal of Urban Technology, Taylor & Francis Journals, vol. 25(2), pages 125-142, April.
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