Integrating Minimum Spanning Tree and MILP in Urban Planning: A Novel Algorithmic Perspective

This paper presents a novel eight-step iterative algorithm for optimizing the layout of a neighborhood, focusing on the efficient allocation of houses to strategically placed facilities, herein referred to as ’points of interest’. The methodology integrates a mixed integer linear programming (MILP)...

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Vydané v:Buildings (Basel) Ročník 14; číslo 1; s. 213
Hlavní autori: Pavon, Wilson, Torres, Myriam, Inga, Esteban
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.01.2024
Predmet:
Algorithms
Analysis
Development projects
Dwellings
facility location problem
Geographic Information Systems (GIS)
Geographical coordinates
Graph theory
Greedy algorithms
Heuristic
heuristic algorithms
Heuristic methods
Houses
Housing
Industrial locations
Infrastructure
Integer programming
Iterative algorithms
Iterative methods
Layouts
Linear programming
Minimum Spanning Tree (MST)
Mixed integer
Mixed Integer Linear Programming (MILP)
Neighborhoods
Network design
Operations research
Optimization techniques
Residential areas
Site selection
Urban planning
urban planning optimization
ISSN:2075-5309, 2075-5309
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Shrnutí:This paper presents a novel eight-step iterative algorithm for optimizing the layout of a neighborhood, focusing on the efficient allocation of houses to strategically placed facilities, herein referred to as ’points of interest’. The methodology integrates a mixed integer linear programming (MILP) approach with a heuristic algorithm to address a variant of the facility location problem combined with network design considerations. The algorithm begins by defining a set of geographic coordinates to represent houses within a predefined area. It then identifies key points of interest, forming the basis for subsequent connectivity and allocation analyses. The methodology’s core involves applying the Greedy algorithm to assign houses to the nearest points of interest, subject to capacity constraints. The method is followed by computing a Minimum Spanning Tree (MST) among these points to ensure efficient overall connectivity. The proposed algorithm’s iterative design is a key attribute. The most promising result of this approach is its ability to minimize the distance between houses and points of interest while optimizing the network’s total length. This dual optimization ensures a balanced distribution of houses and an efficient layout, making it particularly suitable for urban planning and infrastructure development. The paper’s findings demonstrate the algorithm’s effectiveness in creating a practical and efficient neighborhood layout, highlighting its potential application in large-scale urban planning and development projects.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2075-5309
2075-5309
DOI:10.3390/buildings14010213