Action-based Representation for Stochastic Optimization of Complex Real-World RVRP
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0197086" target="_blank" >RIV/00216305:26230/26:0197086 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/11043066" target="_blank" >https://ieeexplore.ieee.org/document/11043066</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CEC65147.2025.11043066" target="_blank" >10.1109/CEC65147.2025.11043066</a>
Alternative languages
Result language
angličtina
Original language name
Action-based Representation for Stochastic Optimization of Complex Real-World RVRP
Original language description
Logistic planning is, in some cases, still done mostly manually with supporting software tools, mainly due to the high complexity of real-world constraints. While research of the classical Vehicle Routing Problem variants is often not directly applicable to real-world logistics problems, this work deals with the problem of logistic planning faced by a particular European logistics company. The studied problem can be modeled as a static, multi-trip, single objective Rich Vehicle Routing Problem with multiple depots, pickup and delivery operations, load splitting, limited heterogeneous vehicles, multiple capacities, single time windows, and compartmentalized cargo groups, alongside various additional incompatibility constraints. The presented research investigates whether the currently used handmade logistic plans can be automatically improved while considering the given real-world constraints. We propose a suitable problem representation based on actions, together with two mutation operators, and compare three stochastic optimization methods (Metropolis-Hastings algorithm, Evolutionary Strategy, Evolutionary Programming). The proposed optimizers achieved an average improvements of 7.7 % on real-world data sets with historic plans provided by the logistics company.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 IEEE Congress on Evolutionary Computation, CEC 2025
ISBN
979-8-3315-3431-8
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
1-4
Publisher name
Institute of Electrical and Electronics Engineers
Place of publication
Hangzhou
Event location
Hangzhou
Event date
Jun 8, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001539410900134