Handling fuzzy temporal constraints in a planning environment

Asunción, Marc de la; Castillo, Luis; Fernández-Olivares, Juan; García-Pérez, Óscar; González, Antonio; Palao, Francisco

doi:10.1007/s10479-007-0207-z

Cited by 12 publications

(5 citation statements)

References 16 publications

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“…Also, artificial intelligence techniques have been applied to nurse rostering, although to a less extent compared to meta-heuristics. Recent ones include case based reasoning (Beddoe et al 2009), fuzzy logic (de la Asunción et al 2007), multi-agent systems (Kaplansky and Meisels 2007), etc.…”

Section: Introductionmentioning

confidence: 99%

A comparison of two approaches to nurse rostering problems

Petrović

Berghe

2010

Ann Oper Res

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Despite decades of research into automated methods for nurse rostering and some academic successes, one may notice that there is no consistency in the knowledge that has been built up over the years and that many healthcare institutions still resort to manual practices. One of the possible reasons for this gap between the nurse rostering theory and practice is that often the academic community focuses on the development of new techniques rather than developing systems for healthcare institutions. In addition, methods suitable for one problem are usually not easily transferable to other problems. In real-world healthcare environments, a personnel manager cannot afford to model a problem and construct a roster using available approaches in order to quantitatively determine which one suits best. There is a lack of criteria for the comparison of approaches to provide a clear picture about their advantages and disadvantages and therefore their suitability to a problem in hand. This paper introduces seven criteria: expressive power, flexibility, algorithmic power, learning capabilities, maintenance, rescheduling capabilities, and parameter tuning, that may offer guidance to researchers and developers of systems for nurse rostering. Two approaches to nurse rostering, which are of very different nature, are evaluated and compared against the introduced criteria. One approach is based on meta-heuristics, while the other employs case-based reasoning.

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Section: Introductionmentioning

confidence: 99%

A comparison of two approaches to nurse rostering problems

Petrović

Berghe

2010

Ann Oper Res

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“…The literature on optimization problems is replete with works that formulate and address practical optimization issues like manufacturing, transportation, production and scheduling, among others, by utilizing FSs and IFSs [4][5][6][7][8][9]. Real-world transportation issues often involve unclear variables for various reasons, including transportation costs [10], supply and demand, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However our proposed method overcomes those weaknesses of existing method as illustrated in the above Table 1. In Table 2, initially we consider two different normal TrIFSs P=< (3,5,7,9); (12,14,16,18)> and B=< (1,5,7,9); (12,14,16,20)> to point out the limitations of established methods. The rank of order P and B can not be determined through Henry [37] method as M (P µ ) = M (B µ ) = 10.5, M (P ν ) = M (B ν ) = 10.5.…”

Section: Comparative Studymentioning

confidence: 99%

New Ranking Approach of Intuitionistic Fuzzy Sets and its Application in Cognitive Transportation Problem

Saikia,

Dutta

2023

Preprint

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In real-life problems, we frequently face uncertain situations because of human cognitive behaviour. Researchers have suggested meticulous mathematical notions to tackle such situations since its initiation. An intuitionistic fuzzy set is one of the effective extensions of a fuzzy set. Intuitionistic fuzzy sets have been applied in various real-life cognitive decision-making problems, for instance, engineering, medicine, pattern recognition, transportation, criminal investigation, etc. Numerous research works can be found in the literature, and most had several restrictions or were performed as unfit for providing human satisfaction outcomes. This work proposes a way to rank intuitionistic fuzzy sets using membership and non-membership functions. We discuss the benefits and provide examples to illustrate the approach. Then, we use our proposed ranking approach to deal with cognitive transportation problems, as it is well known that the core objective of transportation problems is to find optimal solutions. The literature contains various research studies on intuitionistic fuzzy problems with transportation. However, most of them have drawbacks.. Some methods are impractical, and others can't meet supply and demand limitations.. As a result, a method has been developed to lessen restrictions on solving methods of the intuitionistic fuzzy transportation problem. Although, some existing transportation problems are depicted in this work for a comparative study to elaborate on the significance of the present work. Our work deals with solving transportation problems in different uncertain situations using intuitionistic fuzzy environments like normal mixed type triangular, fully triangular and generalized trapezoidal intuitionistic fuzzy.. At the end of the study, hesitancy, cost effectiveness, statistical significant, sensitivity, result analysis, computational complexity and advantages are discussed to establish the effectiveness of the proposed work.

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“…The treatment monitoring step starts once an initial therapy plan has been generated and validated by the oncologist. This step is supported by an execution monitoring system that has been defined in de la Asunción et al (2007) and applied to a different domain application (Fdez‐Olivares et al 2006) that, nevertheless, shares the same representation of plans. At present, it is being adapted to the new requirements of the application domain object of this work, although the main features of this process are also valid for this application.…”

Section: Operation Of the Systemmentioning

confidence: 99%

Supporting Clinical Processes and Decisions by Hierarchical Planning and Scheduling

Fdez-Olivares

Castillo

Cózar

et al. 2011

Computational Intelligence

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This article is focused on how a general-purpose hierarchical planning representation, based on the hierarchical task networks (HTN) paradigm, can be used to support the representation of oncology treatment protocols. The planning algorithm used is a temporally extended HTN planning process capable of interpreting such representation and generating oncology treatment plans that have been proven to support clinical decisions in the area of pediatrics oncology.

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Handling fuzzy temporal constraints in a planning environment

Cited by 12 publications

References 16 publications

A comparison of two approaches to nurse rostering problems

A comparison of two approaches to nurse rostering problems

New Ranking Approach of Intuitionistic Fuzzy Sets and its Application in Cognitive Transportation Problem

Supporting Clinical Processes and Decisions by Hierarchical Planning and Scheduling

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