Solving Transport Infrastructure Investment Project Selection and Scheduling Using Genetic Algorithms
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F24%3A00377641" target="_blank" >RIV/68407700:21260/24:00377641 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.3390/math12193056" target="_blank" >https://doi.org/10.3390/math12193056</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/math12193056" target="_blank" >10.3390/math12193056</a>
Alternative languages
Result language
angličtina
Original language name
Solving Transport Infrastructure Investment Project Selection and Scheduling Using Genetic Algorithms
Original language description
The development of transport infrastructure is crucial for economic growth, social connectivity, and sustainable development. Many countries have historically underinvested in transport infrastructure, necessitating more efficient strategic planning in the implementation of transport infrastructure investment projects. This article addresses the selection and scheduling of transport infrastructure projects, specifically within the context of utilizing pre-allocated funds within a multi-annual budget investment program. The current decision-making process relies heavily on expert judgment and lacks quantitative decision support methods. We propose a genetic algorithm as a decision-support tool, framing the problem as an NP-hard 0-1 multiple knapsack problem. The proposed genetic algorithm (GA) is unique for its matrix-encoded chromosomes, specially designed genetic operators, and a customized repair operator to address the large number of invalid chromosomes generated during the GA computation. In computational experiments, the proposed GA is compared to an exact solution and proves to be efficient in terms of quality of obtained solutions and computational time, with an average computational time of 108 s and the quality of obtained solutions typically ranging between 85% and 95% of the optimal solution. These results highlight the potential of the proposed GA to enhance strategic decision-making in transport infrastructure development.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Mathematics
ISSN
2227-7390
e-ISSN
2227-7390
Volume of the periodical
12
Issue of the periodical within the volume
19
Country of publishing house
CH - SWITZERLAND
Number of pages
28
Pages from-to
1-28
UT code for WoS article
001331978300001
EID of the result in the Scopus database
2-s2.0-85206564758