Optimal Heuristic Algorithms for Connecting Electrical Networks using an unique Path

Pavón, Wilson; Medina, Heriberto; Andrade, Doris; Jaramillo, Dario; Muñoz, Jorge; Barrera-Singaña, Carlos

doi:10.1109/globconht56829.2023.10087852

Cited by 2 publications

(1 citation statement)

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“…This paper introduces a novel eight-step iterative algorithm that addresses this challenge by efficiently allocating houses to strategically identified points of interest within a neighborhood [2]. Utilizing a Mixed Integer Linear Programming (MILP) framework combined with a heuristic approach, the algorithm uniquely integrates the facility location problem with network design principles [3]. The methodology minimizes the distance between houses and points of interest and optimizes the overall network length through a Minimum Spanning Tree (MST) calculation [4].…”

Section: Introductionmentioning

confidence: 99%

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

Pavon,

Torres,

Inga

2024

Buildings

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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.

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Section: Introductionmentioning

confidence: 99%