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Modeling a periodic electric vehicle–routing problem considering delivery due date and mixed charging rates using metaheuristic method

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Abstract

The coupling of ever-increasing consumption of fossil fuels around the globe with the decrease in the availability of fossil fuel supplies has led to an increased cost of energy commodities, which together with ever-expanding requirements for reducing the level of environmental pollutions has resulted in an ever-increasing deal of attention to alternative transportation schemes such as electric vehicles (EVs). Since decades ago, national governments and environmental activists have initiated various efforts towards reducing atmospheric pollutions. A part of such effort has been focused on reducing the use of internal combustion vehicles and rather replacing them with EVs. In this research, we attempt to fill in this research gap by presenting a mathematical model for minimizing the sum of traveled distance and recharging cost of EVs per a given period and then solving it by simulated annealing (SA) algorithm. Results of the proposed algorithm were then compared to those of coding in GAMS for 30 different sample problems with different counts of customers, EVs, and charging stations. Numerical results indicated good efficiency of the metaheuristic algorithm in terms of processing time and solution quality. Indeed, with the SA algorithm, the processing time was seen to increase gradually with increasing the problem complexity, while the rate of increase in processing time was much steeper with the GAMS.

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The authors declare that the data are not available and can be presented upon the request of the readers.

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Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran

    Maryam Elahi & Soroush Avakh Darestani

  2. Guidhall School of Business and Law, London Metropolitan University, London, UK

    Soroush Avakh Darestani

Authors
  1. Maryam Elahi
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  2. Soroush Avakh Darestani
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Contributions

Maryam Elahi (ME) has contributed to the methodology, investigation, project administration, validation, data collection, and software. Soroush Avakh Darestani (SAD) contributed to the conceptualization, writing — review and editing, visualization, and supervision.

Corresponding author

Correspondence to Soroush Avakh Darestani.

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Responsible Editor: Philippe Garrigues

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Appendix

Appendix

EVRPTW:

: electric vehicle routing problem with time window.

GVRP:

: green vehicle routing problem.

G-VRPPD:

: green-vehicle routing problem with pickup and delivery.

HVRP:

: hybrid vehicle–routing problem.

EVRP:

: electric vehicle–routing problem.

DARP-EV:

: dial-a-ride problem with electric vehicle.

MDVLRPTW:

: multi-depot electric vehicle–location routing problem with time windows.

EFV-CSP:

: electric freight vehicle charge scheduling problem.

F-GVRP:

: fuel efficient green vehicle–routing problem.

LRPIF:

: location-routing problem with intra-route facilities.

2sEVRP:

: two-stage electric vehicle–routing problem.

E-VRP-NL:

: electric vehicle–routing problem with non-linear charging.

H2E-FTW:

: hybrid heterogeneous electric fleet–routing problem with time window

BEVRP:

: battery electric vehicle routing.

2E-EVRP-BSS:

: two-echelon capacitated electric vehicle–routing problem with battery swapping stations.

MFGVRP:

: mixed-fleet green vehicle–routing problem.

GMFVRPPRTW:

: green mixed fleet vehicle–routing problem with partial battery recharging and time windows.

CGVRP:

: capacitated green vehicle–routing problem.

PEVRP:

: periodic electric vehicle–routing problem.

DFC:

: design for changeability.

BS:

: battery substitute.

TD:

: time dependent.

H:

: hybrid.

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Elahi, M., Avakh Darestani, S. Modeling a periodic electric vehicle–routing problem considering delivery due date and mixed charging rates using metaheuristic method. Environ Sci Pollut Res 29, 69691–69704 (2022). https://doi.org/10.1007/s11356-022-20776-z

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  • DOI: https://doi.org/10.1007/s11356-022-20776-z

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