.png){kind=icon}
.png){kind=icon}
Return
Multi-depot vehicle routing problem with risk mitigation: Model and solution algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2019.113099.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In practice, the execution of plans with vehicle routing components is often subjected to external events since the transporting vehicles can be exposed to various risk factors. This may lead to delivery failure, vehicle breakdown, commodity loses, etc. In this setting, the stakeholders can benefit from logistic planning techniques whereby potential vehicle breakdown and cargo delivery failure can be mitigated by limiting vehicle risk exposure and prioritizing deliveries of larger payloads. In this paper, we propose a cost effective learning-based heuristic technique to minimize the routing cost along with the potential cost due to the risk of vehicle breakdown and cargo delivery failure. The approach is elaborated by means of an illustrative case study, and it is accompanied by benchmark results along with a comparative study. The heuristic solution generation approach can be used to mitigate vehicle routing risk at the planning stage as well as during various proactive and reactive plan adaptation activities in response to the occurrence of exogenous events. Crown Copyright (C) 2019 Published by Elsevier Ltd. All rights reserved.
Keywords:
Multi-depot vehicle routing problem
Heuristic algorithm
Supply chain management
Transportation risk mitigation
Transportation plan adaptation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W