Planning of Vehicle Routing with Mixed Time Windows Based on the Improved Genetic Algorithm (IGA). Issue 4 (June 2021)
- Record Type:
- Journal Article
- Title:
- Planning of Vehicle Routing with Mixed Time Windows Based on the Improved Genetic Algorithm (IGA). Issue 4 (June 2021)
- Main Title:
- Planning of Vehicle Routing with Mixed Time Windows Based on the Improved Genetic Algorithm (IGA)
- Authors:
- Jiang, Yueyang
- Abstract:
- Abstract: In recent years, the rapid expansion of urban distribution business and the increase use of traditional fuel logistics trucks have resulted in a lot of environmental, traffic and resource problems. "Green logistics" has become the direction of urban distribution transformation, and replacing fuel vehicles with pure electric logistics vehicles has become a way to meet the requirements of urban end distribution. In this paper, the characteristics of electric logistics vehicles are combined with the constraints of cargo capacity to meet the demand for sufficient power and driving range in the distribution process. Charging operations are carried out using charging piles at fixed locations when necessary, and the time windows of customers at each node are considered and penalty factors are set so that the distribution path of electric logistics vehicles can be well planned, which makes the total logistics cost lowest.
- Is Part Of:
- Journal of physics. Volume 1952:Issue 4(2021)
- Journal:
- Journal of physics
- Issue:
- Volume 1952:Issue 4(2021)
- Issue Display:
- Volume 1952, Issue 4 (2021)
- Year:
- 2021
- Volume:
- 1952
- Issue:
- 4
- Issue Sort Value:
- 2021-1952-0004-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-06
- Subjects:
- Physics -- Congresses
530.5 - Journal URLs:
- http://www.iop.org/EJ/journal/1742-6596 ↗
http://ioppublishing.org/ ↗ - DOI:
- 10.1088/1742-6596/1952/4/042003 ↗
- Languages:
- English
- ISSNs:
- 1742-6588
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5036.223000
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British Library HMNTS - ELD Digital store - Ingest File:
- 17479.xml