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A mathematical model for the coverage location problem with overlap control.

Eliseu J Araújo^1,2, Antônio A Chaves^1,2, Luiz A N Lorena^1,2

¹Univ Fed of São Paulo, São José dos Campos, Brazil.

Computers & Industrial Engineering

|August 25, 2020

Summary

This summary is machine-generated.

This study introduces the Coverage Location Problem with Overlap Control (CLPOC) to optimize facility placement for public services. The new model effectively manages coverage overlaps, ensuring reliable service delivery and efficient resource allocation.

Keywords:

Coverage location problem Emergency systems Mathematical model Overlap

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Area of Science:

Operations Research
Facility Location Analysis
Optimization Theory

Background:

The standard Coverage Location Problem (CLP) aims to minimize facilities for demand satisfaction.
Real-world systems, like emergency services, require redundant coverage due to potential facility failures.
Existing CLP models can lead to excessive overlap, increasing unnecessary facility counts.

Purpose of the Study:

To introduce and model a new problem variant: the Coverage Location Problem with Overlap Control (CLPOC).
To develop a mathematical model that actively controls overlap between facility coverage zones.
To address the need for robust service coverage in critical public and emergency systems.

Main Methods:

Formulation of a novel mathematical model for the Coverage Location Problem with Overlap Control (CLPOC).
Utilization of a commercial solver to determine optimal solutions for established problem instances.
Computational testing to evaluate the model's performance on various scenarios.

Main Results:

The proposed mathematical model successfully controls coverage zone overlaps.
Optimal solutions were found for existing literature instances.
The model demonstrated effectiveness in balancing minimum coverage zones with sufficient redundancy for high-demand points.

Conclusions:

The developed CLPOC model provides an effective approach to managing facility coverage overlaps.
This optimization strategy is crucial for enhancing the reliability of public and emergency services.
The findings suggest improved resource allocation and service continuity in complex operational environments.