Detail výsledku
Action-based Representation for Stochastic Optimization of Complex Real-World RVRP
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.
Rich Vehicle Routing Problem, RVRP, Vehicle Routing Problem, VRP, Logistics,
Stochastic Optimization
@inproceedings{BUT197086,
author="SEDLÁK, D. and BIDLO, M. and CERVENKA, M.",
title="Action-based Representation for Stochastic Optimization of Complex Real-World RVRP",
booktitle="2025 IEEE Congress on Evolutionary Computation, CEC 2025",
year="2025",
pages="1--4",
publisher="Institute of Electrical and Electronics Engineers",
address="Hangzhou",
doi="10.1109/CEC65147.2025.11043066",
isbn="979-8-3315-3431-8",
url="https://ieeexplore.ieee.org/document/11043066"
}