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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/1/101 |
| _version_ | 1797626353123590144 |
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| author | Majsa Ammouriova Erika M. Herrera Mattia Neroni Angel A. Juan Javier Faulin |
| author_facet | Majsa Ammouriova Erika M. Herrera Mattia Neroni Angel A. Juan Javier Faulin |
| author_sort | Majsa Ammouriova |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T10:09:12Z |
| format | Article |
| id | doaj.art-af13a493a7ce46b4a3d1d10cb07370e8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T10:09:12Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-af13a493a7ce46b4a3d1d10cb07370e82023-11-16T14:50:40ZengMDPI AGApplied Sciences2076-34172022-12-0113110110.3390/app13010101Solving Vehicle Routing Problems under Uncertainty and in Dynamic Scenarios: From Simheuristics to Agile OptimizationMajsa Ammouriova0Erika M. Herrera1Mattia Neroni2Angel A. Juan3Javier Faulin4Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, SpainComputer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, SpainResearch & Development Team, aHead Research—Spindox SpA, 10149 Torino, ItalyDepartment of Applied Statistics and Operations Research, Universitat Politècnica de València, 03801 Alcoy, SpainInstitute of Smart Cities, Department of Statistics, Computer Science and Mathematics, Public University of Navarra, 31006 Pamplona, SpainMany 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.https://www.mdpi.com/2076-3417/13/1/101vehicle routing problemheuristicsuncertaintydynamic environmentsreal-time optimization |
| spellingShingle | Majsa Ammouriova Erika M. Herrera Mattia Neroni Angel A. Juan Javier Faulin Solving Vehicle Routing Problems under Uncertainty and in Dynamic Scenarios: From Simheuristics to Agile Optimization Applied Sciences vehicle routing problem heuristics uncertainty dynamic environments real-time optimization |
| title | Solving Vehicle Routing Problems under Uncertainty and in Dynamic Scenarios: From Simheuristics to Agile Optimization |
| title_full | Solving Vehicle Routing Problems under Uncertainty and in Dynamic Scenarios: From Simheuristics to Agile Optimization |
| title_fullStr | Solving Vehicle Routing Problems under Uncertainty and in Dynamic Scenarios: From Simheuristics to Agile Optimization |
| title_full_unstemmed | Solving Vehicle Routing Problems under Uncertainty and in Dynamic Scenarios: From Simheuristics to Agile Optimization |
| title_short | Solving Vehicle Routing Problems under Uncertainty and in Dynamic Scenarios: From Simheuristics to Agile Optimization |
| title_sort | solving vehicle routing problems under uncertainty and in dynamic scenarios from simheuristics to agile optimization |
| topic | vehicle routing problem heuristics uncertainty dynamic environments real-time optimization |
| url | https://www.mdpi.com/2076-3417/13/1/101 |
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