Solving Vehicle Routing Problems under Uncertainty and in Dynamic Scenarios: From Simheuristics to Agile Optimization

Many real-life applications of the vehicle routing problem (VRP) occur in scenarios subject to uncertainty or dynamic conditions. Thus, for instance, traveling times or customers’ demands might be better modeled as random variables than as deterministic values. Likewise, traffic conditions could evo...

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Vydáno v:Applied sciences Ročník 13; číslo 1; s. 101
Hlavní autoři: Ammouriova, Majsa, Herrera, Erika M., Neroni, Mattia, Juan, Angel A., Faulin, Javier
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.01.2023
Témata:
Autonomous vehicles
Collaboration
dynamic environments
Electric vehicles
Heuristic
heuristics
Industrial engineering
Internet of Things
Monte Carlo simulation
Optimization
Pollutants
real-time optimization
Smart cities
Sustainability
uncertainty
vehicle routing problem
ISSN:2076-3417, 2076-3417
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Shrnutí:Many real-life applications of the vehicle routing problem (VRP) occur in scenarios subject to uncertainty or dynamic conditions. Thus, for instance, traveling times or customers’ demands might be better modeled as random variables than as deterministic values. Likewise, traffic conditions could evolve over time, synchronization issues should need to be considered, or a real-time re-optimization of the routing plan can be required as new data become available in a highly dynamic environment. Clearly, different solving approaches are needed to efficiently cope with such a diversity of scenarios. After providing an overview of current trends in VRPs, this paper reviews a set of heuristic-based algorithms that have been designed and employed to solve VRPs with the aforementioned properties. These include simheuristics for stochastic VRPs, learnheuristics and discrete-event heuristics for dynamic VRPs, and agile optimization heuristics for VRPs with real-time requirements.
Bibliografie:ObjectType-Article-1
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ISSN:2076-3417
2076-3417
DOI:10.3390/app13010101