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Polytechnic Institute of Bragança, Portugal
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4 Papers

Automatic Nurse Allocation based on a Population Algorithm for Home Health Care

Authors: Filipe Alves 1 , Ana Rocha 2 , Ana Pereira 1 and Paulo Leitão 3

Affiliations: 1 Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Bragança, Portugal, ALGORITMI Center, University of Minho, Braga, Portugal , 2 ALGORITMI Center, University of Minho, Braga, Portugal , 3 Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Bragança, Portugal

Digital Object Identifier: 10.5220/0009386103950402

Proceedings of ND2A 2020, ISBN: 978-989-758-396-4

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The provision of home health care services is becoming an important research area, mainly because in Portugal the population is ageing and it is necessary to perform home care services. Home care visits are organized taking into account the medical treatments and general support that elder/sick people need at home. This health service can be provided by nurses teams from Health Units, requiring some logistics for this purpose. Usually, the visits are manually planned and without computational support. The main goal of this work is to carry out the automatic nurse’s allocation of home care visits, of one Bragança Health Unit, in order to minimize the nurse’s workload balancing, spent time in all home care visits and, consequently, reduce the costs involved. The developed methodology was coded in MatLab Software and the problems were efficiently solved by the particle swarm optimization method. The nurse’s allocation solution of home care visits for the presented case study shows a significant improvement and reduction in the maximum time, in the nurse workload balancing, as well as the patients waiting time.

A Multi-objective Approach to the Optimization of Home Care Visits Scheduling

Topics: Decision Support Systems; Optimization; OR in Health

Authors: Filipe Alves 1 , Lino Costa 2 , Ana Rocha 2 , Ana Pereira 1 and Paulo Leitão 3

Affiliations: 1 Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Bragança, Portugal, Algoritmi R&D Centre, University of Minho, Braga and Portugal , 2 Department of Production and Systems, Algoritmi R&D Centre, University of Minho, Braga and Portugal , 3 Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Bragança and Portugal

Digital Object Identifier: 10.5220/0007565704350442

Proceedings of ICORES 2019, ISBN: 978-989-758-352-0

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Due to the increasing of life expectancy in the developed countries, the demand for home health care services is growing dramatically. Usually, home services are planned manually and lead to various optimization problems in their activities. In this sense, health units are confronted with appropriate scheduling which may contain multiple, often conflicting, objectives such as minimizing the costs related to the traveling distance while minimizing the traveling time. In order to analyze and discuss different trade-offs between these objectives, it is proposed a multi-objective approach to home health care scheduling in which the problem is solved using the Tchebycheff method and a Genetic algorithm. Different alternative solutions are presented to the decision maker that taking into account his/her preferences chooses the appropriate solution. A problem with real data from a home health care service is solved. The results highlight the importance of a multi-objective approach to optimize and support decision making in home health care services. Moreover, this approach provides efficient and good solutions in a reasonable time.
Collapse Button Related Ontology Subjects/Areas/Topics (18)
Applications

Decision Support Systems

Operational Research

Artificial Intelligence

Decision Support Systems, Remote Data Analysis

Optimization

Business Analytics

Health Engineering and Technology Applications

OR in Health

Cardiovascular Technologies

Knowledge Discovery and Information Retrieval

Pattern Recognition

Computing and Telecommunications in Cardiology

Knowledge-Based Systems

Software Engineering

Data Engineering

Methodologies and Technologies

Symbolic Systems

Distributing Intelligence among Cloud, Fog and Edge in Industrial Cyber-physical Systems

Topics: Machine Learning in Control Applications; Distributed Control Systems; Software Agents for Intelligent Control Systems; Industrial Internet of Things

Authors: Jonas Queiroz 1 , Paulo Leitão 2 , José Barbosa 2 and Eugénio Oliveira 1

Affiliations: 1 University of Porto, Faculty of Engineering - LIACC, Porto and Portugal , 2 Research Center in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Bragança and Portugal

Digital Object Identifier: 10.5220/0007979404470454

Proceedings of ICINCO 2019, ISBN: 978-989-758-380-3

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The 4th industrial revolution advent promotes the reorganization of the traditional hierarchical automation systems towards decentralized Cyber-Physical Systems (CPS). In this context, Artificial Intelligence (AI) can address the new requirements through the use of data-driven and distributed problem solving approaches, such those based on Machine-Learning and Multi-agent Systems. Although their promising perspectives to enable and manage intelligent Internet of Things environments, the traditional Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitive. The solution lies in taking advantage of Edge and Fog computing to create a decentralized multi-level data analysis computing infrastructure that supports the development of industrial CPS. However, this is not a straightforward task, posing several challenges and demanding new approaches and technologies. In this context, this work discusses the distribution of intelligence along Cloud, Fog and Edge computing layers in industrial CPS, leveraging some research challenges and future directions.
Collapse Button Related Ontology Subjects/Areas/Topics (5)
Distributed Control Systems

Intelligent Control Systems and Optimization

Software Agents for Intelligent Control Systems
Informatics in Control, Automation and Robotics

Machine Learning in Control Applications

Periodic Vehicle Routing Problem in a Health Unit

Topics: Decision Support Systems; Scheduling; Routing; OR in Health

Authors: F. Alves 1 , F. Alvelos 2 , A. Rocha 2 , Ana Pereira 1 and Paulo Leitão 3

Affiliations: 1 Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal, Algoritmi R&D Centre, University of Minho, Braga and Portugal , 2 Department of Production and Systems, Algoritmi Research Centre, University of Minho, Braga and Portugal , 3 Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança and Portugal

Digital Object Identifier: 10.5220/0007392803840389

Proceedings of ICORES 2019, ISBN: 978-989-758-352-0

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In logistics of home health care services in the Health Units, the managers and nurses need to carry out the schedule and the vehicles routes for the provision of care at the patients’ homes. Currently, in Portugal, these services are increasingly used but the problem is still, usually, solved manually and without computational resources. The increased demand for home health care due to the boost of the elderly people number entails a high associated cost which, sometimes, does not guarantee the quality of the service. In this sense, the periodic vehicle routing problem is a generalization of the classical vehicle routing problem in which routes are determined for a time horizon of several days. In this work, it is provided a periodic vehicle routing problem applied in the Health Unit in Bragança. An integer linear programming formulation for the real database, allowed to solve the problem in an efficient and optimized way using the CPLEX R software.
Collapse Button Related Ontology Subjects/Areas/Topics (17)
Applications

Decision Support Systems

Pattern Recognition

Artificial Intelligence

Decision Support Systems, Remote Data Analysis

Routing

Business Analytics

Health Engineering and Technology Applications

Scheduling

Cardiovascular Technologies

Knowledge-Based Systems

Software Engineering

Computing and Telecommunications in Cardiology

Operational Research

Symbolic Systems
Data Engineering

OR in Health

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