Effective Ant Colony Optimization Solution for the Brazilian Family Health Team Scheduling Problem

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI:10.1109/ICTAI.2018.00082

Willian Heitor Martins, Lucia Helena Souza Alves de Santiago, Rafael de Santiago, L. Lamb

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Abstract

The family health strategy in Brazil is a program that aims at universal access to actions and services of health promotion, protection, and recovery. In this nationwide program, teams of health professionals are responsible for attending and promoting health actions to a community of a specific area. These teams perform home visits that will support the patients of their respective target areas who demand special health care. To help in the scheduling process of these visits, we propose a new bi-objective problem and two methods for its implementation. The main method is an Ant Colony Optimization-based (ACO) heuristic. The other one is an exact linear programming algorithm designed to allow for experimental comparisons. Our experiments suggest that our ACO surpassed the exact solver in runtime, reaching the optimal solutions for all the solutions known. Amortized complexity analysis showed that the ACO heuristic has sublinear complexity over the number of patients.

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巴西家庭医疗队调度问题的有效蚁群优化解决方案

巴西的家庭保健战略是一项旨在普及促进、保护和恢复健康的行动和服务的方案。在这个全国性的项目中，由卫生专业人员组成的团队负责参加并促进特定地区社区的卫生行动。这些小组进行家访，为各自目标地区需要特殊保健服务的病人提供支持。本文提出了一个新的双目标问题，并提出了两种实现方法。主要方法是基于蚁群优化的启发式算法。另一个是一个精确的线性规划算法，旨在允许实验比较。我们的实验表明，我们的蚁群算法在运行时超越了精确求解器，达到了所有已知解的最优解。平摊复杂度分析表明，蚁群算法的复杂度随患者数量呈亚线性变化。

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2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)

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[Title page i] Enhanced Unsatisfiable Cores for QBF: Weakening Universal to Existential Quantifiers Effective Ant Colony Optimization Solution for the Brazilian Family Health Team Scheduling Problem Exploiting Global Semantic Similarity Biterms for Short-Text Topic Discovery Assigning and Scheduling Service Visits in a Mixed Urban/Rural Setting