Fuzzy system dynamics and optimization with application to manpower systems
- Mutingi, M., Mbohwa, Charles
- Authors: Mutingi, M. , Mbohwa, Charles
- Date: 2012
- Subjects: Manpower planning , Human resources management , Fuzzy sets
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/374238 , uj:5198 , http://hdl.handle.net/10210/14441
- Description: The dynamics of human resource recruitment and training in an uncertain environment creates a challenge for many policy makers in various organisations. In the presence of fuzzy manpower demand and training capacity, many companies fear losing critical human resources when their employees leave. As such, the development of effective dynamic policies for recruitment and training in a fuzzy dynamic environment is imperative.
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- Authors: Mutingi, M. , Mbohwa, Charles
- Date: 2012
- Subjects: Manpower planning , Human resources management , Fuzzy sets
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/374238 , uj:5198 , http://hdl.handle.net/10210/14441
- Description: The dynamics of human resource recruitment and training in an uncertain environment creates a challenge for many policy makers in various organisations. In the presence of fuzzy manpower demand and training capacity, many companies fear losing critical human resources when their employees leave. As such, the development of effective dynamic policies for recruitment and training in a fuzzy dynamic environment is imperative.
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Expert fuzzy control based upon man-in-the-loop model identification
- Authors: Shaw, Ian Stephan
- Date: 2014-06-11
- Subjects: Fuzzy sets
- Type: Thesis
- Identifier: uj:11495 , http://hdl.handle.net/10210/11191
- Description: M.Ing. (Electrical & Electronic Engineering) , A dynamic process is considered modelled and identified when the model can predict its future behaviour as a result of a known stimulus. However, practical reality is complex and it is quite difficult to totally encompass a model representing a physical phenomenon in a mathematical formulation. Besides, to keep such formulations tractable, certain restrictive assumptions such as, for example, linearity, are often required. The common feature of general control-theoretic methods used for modelling is that they presuppose the valid and accurate knowledge of the processes to be controlled. If, however, one does not understand the inner workings of a complex process that one wishes to model, traditional techniques rarely yield satisfactory results. As systems become more complex it becomes increasingly difficult to make mathematical statements about them which are both meaningful and precise. Thus one is compelled to concede that imprecision and inexactness must be accepted in any real system application. The theory of fuzzy sets is a methodology for the handling of qualitative, inexact, imprecise, information in a systematic and rigorous way. This approach provides an excellent tool for the modelling of human-centered systems, especially because fuzziness seems to be an important facet of the human thinking process. Instead of using a precisely defined or measured value of a variable, a human being tends to summarize available information by classifying into vague and imprecise categories such as, for example, low, medium, high. In this way, the information received from the outside world is reduced to just what is needed to perform the task on hand with the required precision. Thus there is no need for precise mathematical models and thereby the human (i.e. fuzzy) decision-making mechanism has considerably less computational overhead and is thus faster and more conducive to biological survival than an equivalent precise mathematical model...
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- Authors: Shaw, Ian Stephan
- Date: 2014-06-11
- Subjects: Fuzzy sets
- Type: Thesis
- Identifier: uj:11495 , http://hdl.handle.net/10210/11191
- Description: M.Ing. (Electrical & Electronic Engineering) , A dynamic process is considered modelled and identified when the model can predict its future behaviour as a result of a known stimulus. However, practical reality is complex and it is quite difficult to totally encompass a model representing a physical phenomenon in a mathematical formulation. Besides, to keep such formulations tractable, certain restrictive assumptions such as, for example, linearity, are often required. The common feature of general control-theoretic methods used for modelling is that they presuppose the valid and accurate knowledge of the processes to be controlled. If, however, one does not understand the inner workings of a complex process that one wishes to model, traditional techniques rarely yield satisfactory results. As systems become more complex it becomes increasingly difficult to make mathematical statements about them which are both meaningful and precise. Thus one is compelled to concede that imprecision and inexactness must be accepted in any real system application. The theory of fuzzy sets is a methodology for the handling of qualitative, inexact, imprecise, information in a systematic and rigorous way. This approach provides an excellent tool for the modelling of human-centered systems, especially because fuzziness seems to be an important facet of the human thinking process. Instead of using a precisely defined or measured value of a variable, a human being tends to summarize available information by classifying into vague and imprecise categories such as, for example, low, medium, high. In this way, the information received from the outside world is reduced to just what is needed to perform the task on hand with the required precision. Thus there is no need for precise mathematical models and thereby the human (i.e. fuzzy) decision-making mechanism has considerably less computational overhead and is thus faster and more conducive to biological survival than an equivalent precise mathematical model...
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A satisficing approach to home healthcare worker scheduling
- Mutingi, Michael, Mbohwa, Charles
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2013
- Subjects: Home healthcare staff scheduling , Fuzzy sets
- Type: Article
- Identifier: uj:6174 , ISBN 978-93-82242-26-0 , http://hdl.handle.net/10210/13781
- Description: The homecare worker scheduling problem is inundated with fuzzy and often conflicting goals, constraints and preferences. In such an uncertain environment, the decision maker needs to find a satisficing solution approach that takes into account the humanistic judgments and the conflicting nature of the goals. This paper proposes a fuzzy satisficing approach, based on fuzzy set theory, for addressing the homecare worker scheduling problem. The aim is to provide a satisficing approach that considers the management goals, the worker preferences, as well as the service quality as specified by the healthcare clients. By addressing the desired goals or preferences of the three players, (i) the management, (ii) the worker, and (iii) the client, the approach provides a more realistic, flexible and adaptable method for real-world healthcare staff scheduling in an uncertain environment.
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- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2013
- Subjects: Home healthcare staff scheduling , Fuzzy sets
- Type: Article
- Identifier: uj:6174 , ISBN 978-93-82242-26-0 , http://hdl.handle.net/10210/13781
- Description: The homecare worker scheduling problem is inundated with fuzzy and often conflicting goals, constraints and preferences. In such an uncertain environment, the decision maker needs to find a satisficing solution approach that takes into account the humanistic judgments and the conflicting nature of the goals. This paper proposes a fuzzy satisficing approach, based on fuzzy set theory, for addressing the homecare worker scheduling problem. The aim is to provide a satisficing approach that considers the management goals, the worker preferences, as well as the service quality as specified by the healthcare clients. By addressing the desired goals or preferences of the three players, (i) the management, (ii) the worker, and (iii) the client, the approach provides a more realistic, flexible and adaptable method for real-world healthcare staff scheduling in an uncertain environment.
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A multi-criteria approach for nurse scheduling : fuzzy simulated metamorphosis algorithm approach
- Mutingi, Michael, Mbohwa, Charles
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2015-03-03
- Subjects: Healthcare staff scheduling , Nurse rostering , Fuzzy sets
- Type: Article
- Identifier: uj:5221 , http://hdl.handle.net/10210/14507
- Description: Motivated by the biological metamorphosis process and the need to solve multi-objective optimization problems with conflicting and fuzzy goals and constraints, this paper proposes a simulated metamorphosis algorithm, based on the concepts of biological evolution in insects, such as moths, butterflies, and beetles. By mimicking the hormone controlled evolution process the algorithm works on a single candidate solution, going through initialization, iterative growth loop, and finally maturation loop. The method is a practical way to optimizing multi-objective problems with fuzzy conflicting goals and constraints. The approach is applied to the nurse scheduling problem. Equipped with the facility to incorporate the user’s choices and wishes, the algorithm offers an interactive approach that can accommodate the decision maker’s expert intuition and experience, which is otherwise impossible with other optimization algorithms. By using hormonal guidance and unique operators, the algorithm works on a single candidate solution, and efficiently evolves it to a near-optimal solution. Computational experiments show that the algorithm is competitive.
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- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2015-03-03
- Subjects: Healthcare staff scheduling , Nurse rostering , Fuzzy sets
- Type: Article
- Identifier: uj:5221 , http://hdl.handle.net/10210/14507
- Description: Motivated by the biological metamorphosis process and the need to solve multi-objective optimization problems with conflicting and fuzzy goals and constraints, this paper proposes a simulated metamorphosis algorithm, based on the concepts of biological evolution in insects, such as moths, butterflies, and beetles. By mimicking the hormone controlled evolution process the algorithm works on a single candidate solution, going through initialization, iterative growth loop, and finally maturation loop. The method is a practical way to optimizing multi-objective problems with fuzzy conflicting goals and constraints. The approach is applied to the nurse scheduling problem. Equipped with the facility to incorporate the user’s choices and wishes, the algorithm offers an interactive approach that can accommodate the decision maker’s expert intuition and experience, which is otherwise impossible with other optimization algorithms. By using hormonal guidance and unique operators, the algorithm works on a single candidate solution, and efficiently evolves it to a near-optimal solution. Computational experiments show that the algorithm is competitive.
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A fuzzy genetic algorithm for healthcare staff scheduling
- Mutingi, Michael, Mbohwa, Charles
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2013
- Subjects: Healthcare staff scheduling , Fuzzy-based genetic algorithm , Fuzzy sets
- Type: Article
- Identifier: uj:6172 , ISBN 978-93-82242-26-0 , http://hdl.handle.net/10210/13779
- Description: In the presence of multiple conflicting objectives and constraints, healthcare staff scheduling is complex. This research presents a fuzzy-based genetic algorithm (FGA) for handling multiple conflicting objectives and constraints common in healthcare manpower scheduling problems. Fuzzy set theory is used for genetic evaluations of alternative staff schedules by representing the fitness of each alternative solution as a fuzzy membership functions. The proposed FGA framework is designed to incorporate the often imprecise decision maker’s preferences and choices in terms of weights. The framework is also designed to provide a population of alternative solutions for the decision maker, rather than prescribe a single decision. It is anticipated that the FGA procedure forms a useful decision support tool for healthcare staff scheduling in a fuzzy environment with multiple conflicting objectives and constraints.
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- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2013
- Subjects: Healthcare staff scheduling , Fuzzy-based genetic algorithm , Fuzzy sets
- Type: Article
- Identifier: uj:6172 , ISBN 978-93-82242-26-0 , http://hdl.handle.net/10210/13779
- Description: In the presence of multiple conflicting objectives and constraints, healthcare staff scheduling is complex. This research presents a fuzzy-based genetic algorithm (FGA) for handling multiple conflicting objectives and constraints common in healthcare manpower scheduling problems. Fuzzy set theory is used for genetic evaluations of alternative staff schedules by representing the fitness of each alternative solution as a fuzzy membership functions. The proposed FGA framework is designed to incorporate the often imprecise decision maker’s preferences and choices in terms of weights. The framework is also designed to provide a population of alternative solutions for the decision maker, rather than prescribe a single decision. It is anticipated that the FGA procedure forms a useful decision support tool for healthcare staff scheduling in a fuzzy environment with multiple conflicting objectives and constraints.
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