Solving Transport Infrastructure Investment Project Selection and Scheduling Using Genetic Algorithms

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...

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Veröffentlicht in:Mathematics (Basel) Jg. 12; H. 19; S. 3056
Hauptverfasser: Ječmen, Karel, Mocková, Denisa, Teichmann, Dušan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.10.2024
Schlagworte:
Algorithms
Chromosomes
Computing time
Decision making
Decision support systems
Economic development
Economic growth
Exact solutions
Genetic algorithms
Infrastructure
Infrastructure (Economics)
Knapsack problem
Methods
multiple knapsack problem
Mutation
Scheduling
Scheduling (Management)
Strategic planning (Business)
Sustainable development
transport infrastructure development
transport infrastructure investment projects
Transportation policy
Czech Republic
Germany
ISSN:2227-7390, 2227-7390
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Zusammenfassung: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.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math12193056