Multi-objective mammography unit location–allocation problem: A case study

IF 1.5 Q3 HEALTH CARE SCIENCES & SERVICES Operations Research for Health Care Pub Date : 2024-03-30 DOI:10.1016/j.orhc.2024.100430

Marcos Vinícius Andrade de Campos , Romário dos Santos Lopes de Assis , Marcone Jamilson Freitas Souza , Eduardo Camargo de Siqueira , Maria Amélia Lopes Silva , Sérgio Ricardo de Souza

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Abstract

This work addresses the Multi-Objective Mammography Unit Location–allocation Problem (MOMULAP), aiming to meet three objectives: maximize mammography screening coverage, minimize the total distance traveled weighted by the number of users, and maximize equity in access to mammography screening. We introduce a mixed-integer nonlinear programming (MINLP) formulation to represent the MOMULAP and algorithms based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA2) for treating it. The algorithms were tested with data from seven Brazilian states. In these states, the number of cities ranges from 139 to 853, equipment from 23 to 347 units, and estimated annual demand for screenings from 96,592 to 1,739,085. The solutions provided by this work allow health managers to choose the appropriate location and allocation of the mammography units, considering different objectives.

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多目标乳腺 X 射线照相单元位置分配问题：案例研究

本研究针对多目标乳腺放射摄影单位位置分配问题（MOMULAP），旨在实现三个目标：最大化乳腺放射摄影筛查覆盖率、最小化用户数量加权的总路程，以及最大化乳腺放射摄影筛查的公平性。我们引入了一个混合整数非线性编程（MINLP）公式来表示 MOMULAP，并引入了基于非支配排序遗传算法 II（NSGA-II）和强度帕累托进化算法（SPEA2）的算法来处理该问题。这些算法使用巴西七个州的数据进行了测试。在这些州中，城市数量从 139 个到 853 个不等，设备从 23 台到 347 台不等，估计每年的筛查需求从 96,592 到 1,739,085 不等。这项工作提供的解决方案使卫生管理人员能够在考虑不同目标的情况下，选择合适的地点和乳腺 X 射线照相设备的分配。

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