Electric Vehicle Routing With Flexible Time Windows: a Column Generation Solution Approach

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Date

2020

Authors

Taş, Duygu

Journal Title

Journal ISSN

Volume Title

Publisher

Taylor & Francis

Open Access Color

Green Open Access

Yes

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Publicly Funded

No
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Top 10%
Influence
Top 10%
Popularity
Top 10%

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Abstract

In this paper, we introduce the Electric Vehicle Routing Problem with Flexible Time Windows (EVRPFTW) in which vehicles are allowed to serve customers before and after the earliest and latest time window bounds, respectively. The objective of this problem is to assign electric vehicles to feasible routes and make schedules with minimum total cost that includes the traveling costs, the costs of using electric vehicles and the penalty costs incurred for earliness and lateness. The proposed mathematical model is solved by a column generation procedure. To generate an integer solution, we solve an integer programming problem using the routes constructed by the column generation algorithm. We further develop a linear programming model to compute the optimal times to start service at each customer for the selected routes. A number of wellknown benchmark instances is solved by our solution procedure to evaluate the operational gains obtained by employing flexible time windows.

Description

Keywords

Time windows, Electric vehicles, Routing, Column generation, Time Windows, Column Generation, Electric Vehicles, Routing

Fields of Science

0502 economics and business, 05 social sciences, 0211 other engineering and technologies, 02 engineering and technology

Citation

Taş, D. (January 10, 2020). Electric vehicle routing with flexible time windows: a column generation solution approach. Transportation Letters - The International Journal of Transportation Research, pp. 1-7, DOI: 10.1080/19427867.2020.1711581

WoS Q

Q2

Scopus Q

Q2
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OpenCitations Citation Count
39

Source

Transportation Letters - The International Journal of Transportation Research

Volume

13

Issue

Start Page

1

End Page

7
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Citations

CrossRef : 28

Scopus : 37

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Mendeley Readers : 34

SCOPUS™ Citations

40

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Web of Science™ Citations

43

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Page Views

199

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Downloads

38

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6.2411

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