GIS Spatial Optimization for Corridor Alignment Using Simulated Annealing

Planning corridors for new facilities such as pipeline or transmission lines through geographical spaces is a topographical constraint optimization problem. The corridor planning problem requires finding an optimal route or a set of alternative paths between two locations. This article presents a si...

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Bibliographic Details
Published in:Applied sciences 2020-09, Vol.10 (18), p.6190
Main Authors: Cruz-Chávez, Marco Antonio, Moreno-Bernal, Pedro, Rivera-López, Rafael, Ávila-Melgar, Erika Yesenia, Martínez-Bahena, Beatriz, Cruz-Rosales, Martín H.
Format: Article
Language:English
Subjects:
Business competition
corridor planning
Design specifications
Environmental impact
Genetic algorithms
Geographic information systems
Geography
Global positioning systems
GPS
Methods
Neighborhoods
Optimization
shortest path algorithms
Simulated annealing
spatial optimization
topographical constraint
Transmission lines
variable neighborhood search
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Summary:Planning corridors for new facilities such as pipeline or transmission lines through geographical spaces is a topographical constraint optimization problem. The corridor planning problem requires finding an optimal route or a set of alternative paths between two locations. This article presents a simulated-annealing-based (SA) approach applying a variable neighborhood strategy in a continuous space to generate competitive and different alternative paths to solve the corridor planning problem. The variable neighborhood method randomly selects two points from a variable interval of the current solution generated by SA creating pseudo-random paths inside a corridor and finding spatially different alternatives. The proposed approach is evaluated with three practical problems using real topographic data from the Veracruz Basin in Mexico. The experimental results show that this approach obtains efficient and competitive solutions with improvements above 18% over those gotten by the compared method.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10186190