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Dynamic Stochastic Electric Vehicle Routing with Safe Reinforcement Learning

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Title: Dynamic Stochastic Electric Vehicle Routing with Safe Reinforcement Learning
Authors: Basso, Rafael, 1979, Kulcsár, Balázs Adam, 1975, Sanchez-Diaz, Ivan, 1984, Qu, Xiaobo, 1983
Source: EL FORT - El Flottor Optimering i Real-Tid EL FORT - Optimering av elfordonsflotta i Real-Tid - (Fas 2) Transportation Research Part E: Logistics and Transportation Review. 157(157)
Subject Terms: Reinforcement Learning, Approximate Dynamic Programming, Energy Consumption, Electric Vehicles, Vehicle Routing, Green Logistics
Description: Dynamic routing of electric commercial vehicles can be a challenging problem since besides the uncertainty of energy consumption there are also random customer requests. This paper introduces the Dynamic Stochastic Electric Vehicle Routing Problem (DS-EVRP). A Safe Reinforcement Learning method is proposed for solving the problem. The objective is to minimize expected energy consumption in a safe way, which means also minimizing the risk of battery depletion while en route by planning charging whenever necessary. The key idea is to learn offline about the stochastic customer requests and energy consumption using Monte Carlo simulations, to be able to plan the route predictively and safely online. The method is evaluated using simulations based on energy consumption data from a realistic traffic model for the city of Luxembourg and a high-fidelity vehicle model. The results indicate that it is possible to save energy at the same time maintaining reliability by planning the routes and charging in an anticipative way. The proposed method has the potential to improve transport operations with electric commercial vehicles capitalizing on their environmental benefits
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  Label: Title
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  Data: Dynamic Stochastic Electric Vehicle Routing with Safe Reinforcement Learning
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Basso%2C+Rafael%22">Basso, Rafael</searchLink>, 1979<br /><searchLink fieldCode="AR" term="%22Kulcsár%2C+Balázs+Adam%22">Kulcsár, Balázs Adam</searchLink>, 1975<br /><searchLink fieldCode="AR" term="%22Sanchez-Diaz%2C+Ivan%22">Sanchez-Diaz, Ivan</searchLink>, 1984<br /><searchLink fieldCode="AR" term="%22Qu%2C+Xiaobo%22">Qu, Xiaobo</searchLink>, 1983
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  Data: <i>EL FORT - El Flottor Optimering i Real-Tid EL FORT - Optimering av elfordonsflotta i Real-Tid - (Fas 2) Transportation Research Part E: Logistics and Transportation Review</i>. 157(157)
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  Data: <searchLink fieldCode="DE" term="%22Reinforcement+Learning%22">Reinforcement Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Approximate+Dynamic+Programming%22">Approximate Dynamic Programming</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+Consumption%22">Energy Consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+Vehicles%22">Electric Vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Vehicle+Routing%22">Vehicle Routing</searchLink><br /><searchLink fieldCode="DE" term="%22Green+Logistics%22">Green Logistics</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Dynamic routing of electric commercial vehicles can be a challenging problem since besides the uncertainty of energy consumption there are also random customer requests. This paper introduces the Dynamic Stochastic Electric Vehicle Routing Problem (DS-EVRP). A Safe Reinforcement Learning method is proposed for solving the problem. The objective is to minimize expected energy consumption in a safe way, which means also minimizing the risk of battery depletion while en route by planning charging whenever necessary. The key idea is to learn offline about the stochastic customer requests and energy consumption using Monte Carlo simulations, to be able to plan the route predictively and safely online. The method is evaluated using simulations based on energy consumption data from a realistic traffic model for the city of Luxembourg and a high-fidelity vehicle model. The results indicate that it is possible to save energy at the same time maintaining reliability by planning the routes and charging in an anticipative way. The proposed method has the potential to improve transport operations with electric commercial vehicles capitalizing on their environmental benefits
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        Value: 10.1016/j.tre.2021.102496
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      – Text: English
    Subjects:
      – SubjectFull: Reinforcement Learning
        Type: general
      – SubjectFull: Approximate Dynamic Programming
        Type: general
      – SubjectFull: Energy Consumption
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      – SubjectFull: Electric Vehicles
        Type: general
      – SubjectFull: Vehicle Routing
        Type: general
      – SubjectFull: Green Logistics
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      – TitleFull: Dynamic Stochastic Electric Vehicle Routing with Safe Reinforcement Learning
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            NameFull: Basso, Rafael
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            NameFull: Kulcsár, Balázs Adam
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            NameFull: Sanchez-Diaz, Ivan
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            NameFull: Qu, Xiaobo
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              M: 01
              Type: published
              Y: 2022
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