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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Published in:	Buildings (Basel) Vol. 14; no. 1; p. 213
Main Authors:	Pavon, Wilson, Torres, Myriam, Inga, Esteban
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.01.2024
Subjects:	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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Abstract	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.
AbstractList	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.
Audience	Academic
Author	Inga, Esteban Torres, Myriam Pavon, Wilson
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SubjectTerms	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
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Title	Integrating Minimum Spanning Tree and MILP in Urban Planning: A Novel Algorithmic Perspective
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