Action-based Representation for Stochastic Optimization of Complex Real-World RVRP

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Action-based Representation for Stochastic Optimization of Complex Real-World RVRP

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Action-based Representation for Stochastic Optimization of Complex Real-World RVRP

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2025 IEEE Congress on Evolutionary Computation, CEC 2025

ISBN

979-8-3315-3431-8

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

1-4

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

Hangzhou

Místo konání akce

Hangzhou

Datum konání akce

8. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001539410900134