Scheduling of Home Health Care Services Based on Multi-agent Systems

Alves, Filipe; Pereira, Ana I.; Barbosa, José; Leitão, Paulo

doi:10.1007/978-3-319-94779-2_2

Cited by 10 publications

(7 citation statements)

References 12 publications

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“…The main idea is to find the schedule for home care visits for a certain day or days in a period horizon. A simulation has been carried out involving a real case study of a Health Unit in Bragança subject to unexpected events [2].…”

Section: Preliminary Results And/or Evaluation Planmentioning

confidence: 99%

Distributed Scheduling Based on Multi-agent Systems and Optimization Methods

Alves

Rocha

Pereira

et al. 2019

Communications in Computer and Information Science

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The increasing relevance of complex systems in dynamic environments has received special attention during the last decade from the researchers. Such systems need to satisfy products or clients desires, which, after accomplished might change, becoming a very dynamic situation. Currently, decentralized approaches could assist in the automation of dynamic scheduling, based on the distribution of control functions over a swarm network of decision-making entities. Distributed scheduling, in an automatic manner, can be answered by a service coordination architecture of the different schedule components. However, it is necessary to introduce the control layer in the solution, encapsulating an intelligent service that merge agents with optimization methods. Multi-agent systems (MAS) can be combined with several optimization methods to extract the best of the two worlds: the intelligent control, cooperation and autonomy provided by MAS solutions and the optimum offered by optimization methods. The proposal intends to test the intelligent management of the schedule composition quality, in two case studies namely, manufacturing and home health care.

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Section: Preliminary Results And/or Evaluation Planmentioning

confidence: 99%

Distributed Scheduling Based on Multi-agent Systems and Optimization Methods

Alves

Rocha

Pereira

et al. 2019

Communications in Computer and Information Science

Self Cite

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show abstract

“…For that reason, MAS has already been introduced to take advantage of cooperation and intelligent mechanisms to decentralize the system, which normally operates in static mode. In a first approach, a simulated methodology in NetLogo, provided an excellent capacity to generate good autonomous and coordinated solutions, contributing to a better decision making and usefulness (Alves et al, 2018a). In the future, the idea is to contribute to an HHC system based on a digital and distributed ecosystem (platform/app), using different ICT-based solutions, sources, tools, and techniques.…”

Section: Home Health Carementioning

confidence: 99%

Conceptual Multi-Agent System Design for Distributed Scheduling Systems

Alves¹,

Rocha²,

Pereira³

et al. 2022

Smart and Sustainable Manufacturing Systems for Industry 4.0

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With the progressive increase in the complexity of dynamic environments, systems require an evolutionary configuration and optimization to meet the increased demand. In this sense, any change in the conditions of systems or products may require distributed scheduling and resource allocation of more elementary services. Centralized approaches might fall into bottleneck issues, becoming complex to adapt, especially in case of unexpected events. Thus, Multi-agent systems (MAS) can extract their automatic and autonomous behaviour to enhance the task effort distribution and support the scheduling decision-making. On the other hand, MAS is able to obtain quick solutions, through cooperation and smart control by agents, empowered by their coordination and interoperability. By leveraging an architecture that benefits of a collaboration with distributed artificial intelligence, it is proposed an approach based on a conceptual MAS design that allows distributed and intelligent management to promote technological innovation in basic concepts of society for more sustainable in everyday applications for domains with emerging needs, such as, manufacturing and healthcare scheduling systems.

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“…Then, we find the changes in the pool of patients (new patients 28% and departure or cancellations of patients 25%), travel times (25%), closely followed by new demands (22%). On the contrary, some criteria are marginal: changes in visit frequencies (Mosquera et al, 2018) or broken vehicles (Alves et al, 2018a) are only considered in one paper each.…”

Section: Types Of Uncertaintiesmentioning

confidence: 99%