A framework for analysis and evaluation of renewable energy policies
- Mutingi, Michael, Mbohwa, Charles
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2014
- Subjects: Energy planning , Renewable energy policies
- Type: Article
- Identifier: uj:4921 , http://hdl.handle.net/10210/13021
- Description: The formulation and evaluation of renewable energy policies is a burning subject matter all over the globe. Policy makers seek to cautiously perceive information from the renewable energy market place so as to determine the dynamic factors, variables and policy parameters that influence the design of renewable energy policies. The perceived information is often imprecise or fuzzy, which makes policy formulation difficult. This paper presents a framework for evaluating renewable energy policies based on a fuzzy system dynamics (FSD) paradigm. First, we describe the renewable energy policy problem, with a case study example. Second, we present a framework for FSD modeling. Third, we propose a high-level causal loop analysis to capture the complex dynamic interactions among various energy demand and supply factors, from a fuzzy system dynamics perspective. Fourth, and finally, we propose an FSD model for renewable energy policy formulation and evaluation.
- Full Text:
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2014
- Subjects: Energy planning , Renewable energy policies
- Type: Article
- Identifier: uj:4921 , http://hdl.handle.net/10210/13021
- Description: The formulation and evaluation of renewable energy policies is a burning subject matter all over the globe. Policy makers seek to cautiously perceive information from the renewable energy market place so as to determine the dynamic factors, variables and policy parameters that influence the design of renewable energy policies. The perceived information is often imprecise or fuzzy, which makes policy formulation difficult. This paper presents a framework for evaluating renewable energy policies based on a fuzzy system dynamics (FSD) paradigm. First, we describe the renewable energy policy problem, with a case study example. Second, we present a framework for FSD modeling. Third, we propose a high-level causal loop analysis to capture the complex dynamic interactions among various energy demand and supply factors, from a fuzzy system dynamics perspective. Fourth, and finally, we propose an FSD model for renewable energy policy formulation and evaluation.
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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.
- Full Text:
- 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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A fuzzy grouping genetic algorithm for care assignment task
- Mutingi, Michael, Mbohwa, Charles
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2014
- Subjects: Care tasks , Task assignment , Fuzzy grouping genetic algorithm , Fuzzy theory
- Type: Article
- Identifier: uj:4935 , ISSN 2078-0966 , http://hdl.handle.net/10210/13035
- Description: The assignment of care tasks to nurses is often done manually in most hospitals. A high quality care task schedule is crucial for efficient and effective execution of nursing care duties. High quality schedules seek to satisfy patient preferences over time window for the care, schedule fairness among nurses, and management goals regarding care activity completion times and labor costs. This paper suggests a grouping genetic approach to care task scheduling in a hospital setting. By taking advantage of the group structure of the problem, the algorithm uses fuzzy evaluation techniques, permuting tasks across candidate nurse schedules and within each nurse schedule. Results of the computational experiments show that the proposed approach is effective.
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- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2014
- Subjects: Care tasks , Task assignment , Fuzzy grouping genetic algorithm , Fuzzy theory
- Type: Article
- Identifier: uj:4935 , ISSN 2078-0966 , http://hdl.handle.net/10210/13035
- Description: The assignment of care tasks to nurses is often done manually in most hospitals. A high quality care task schedule is crucial for efficient and effective execution of nursing care duties. High quality schedules seek to satisfy patient preferences over time window for the care, schedule fairness among nurses, and management goals regarding care activity completion times and labor costs. This paper suggests a grouping genetic approach to care task scheduling in a hospital setting. By taking advantage of the group structure of the problem, the algorithm uses fuzzy evaluation techniques, permuting tasks across candidate nurse schedules and within each nurse schedule. Results of the computational experiments show that the proposed approach is effective.
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A fuzzy-based particle swarm optimization algorithm for nurse scheduling
- Mutingi, Michael, Mbohwa, Charles
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2014
- Subjects: Nurse scheduling , Nurse rostering , Personnel scheduling , Particle swarm optimization
- Type: Article
- Identifier: uj:4926 , ISSN 2078-0966 , http://hdl.handle.net/10210/13026
- Description: The nurse scheduling problem (NSP) has a great impact on the quality and efficiency of health care operations. Healthcare Operations Analysts have to assign daily shifts to nurses over the planning horizon, so that operations costs are minimized, health care quality is improved, and the nursing staff is satisfied. Due to conflicting objectives and a myriad of restrictions imposed by labor laws, company requirements, and other legislative laws, the NSP is a hard problem. In this paper we present a particle swarm optimization-based algorithm that relies on a heuristic mechanism that incorporates hard constraints to improve the computational efficiency of the algorithm. Further, we incorporate soft constraints into objective function evaluation to guide the algorithm. Results from illustrative examples show that the algorithm is effective and efficient, even over large scale problems.
- Full Text:
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2014
- Subjects: Nurse scheduling , Nurse rostering , Personnel scheduling , Particle swarm optimization
- Type: Article
- Identifier: uj:4926 , ISSN 2078-0966 , http://hdl.handle.net/10210/13026
- Description: The nurse scheduling problem (NSP) has a great impact on the quality and efficiency of health care operations. Healthcare Operations Analysts have to assign daily shifts to nurses over the planning horizon, so that operations costs are minimized, health care quality is improved, and the nursing staff is satisfied. Due to conflicting objectives and a myriad of restrictions imposed by labor laws, company requirements, and other legislative laws, the NSP is a hard problem. In this paper we present a particle swarm optimization-based algorithm that relies on a heuristic mechanism that incorporates hard constraints to improve the computational efficiency of the algorithm. Further, we incorporate soft constraints into objective function evaluation to guide the algorithm. Results from illustrative examples show that the algorithm is effective and efficient, even over large scale problems.
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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 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 taxonomic framework for formulating strategies in green supply chain management
- Mutingi, Michael, Mbohwa, Charles
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2012-07-03
- Subjects: Green supply chain management
- Type: Article
- Identifier: uj:5189 , http://hdl.handle.net/10210/14432
- Description: This paper addresses the increasingly important question of formulating appropriate strategies for green supply chain management. Due to increasing attention to green strategies and their impact on the natural environment, the development of a taxonomic framework for selecting appropriate strategies is imperative. In this study, we develop a taxonomic framework for formulating strategies for green supply chains based on characteristic green supply chain dimensions. The practical implication of this work is that the choice of green supply chain strategy impacts environmental and operations performance. The framework developed can be used as a tool for developing green supply chain management strategies, providing sound managerial insights.
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- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2012-07-03
- Subjects: Green supply chain management
- Type: Article
- Identifier: uj:5189 , http://hdl.handle.net/10210/14432
- Description: This paper addresses the increasingly important question of formulating appropriate strategies for green supply chain management. Due to increasing attention to green strategies and their impact on the natural environment, the development of a taxonomic framework for selecting appropriate strategies is imperative. In this study, we develop a taxonomic framework for formulating strategies for green supply chains based on characteristic green supply chain dimensions. The practical implication of this work is that the choice of green supply chain strategy impacts environmental and operations performance. The framework developed can be used as a tool for developing green supply chain management strategies, providing sound managerial insights.
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An alternative framework for managing engineering change
- Mutingi, Michael, Mbohwa, Charles, Mapfaira, Herbert
- Authors: Mutingi, Michael , Mbohwa, Charles , Mapfaira, Herbert
- Date: 2015-03-03
- Subjects: Engineering change management , Engineering change
- Type: Article
- Identifier: uj:5216 , http://hdl.handle.net/10210/14502
- Description: Effective engineering change management (ECM) procedures are very important over the whole life cycle of every engineering change (EC), from EC proposal to implementation and documentation. However, the success of an EC procedure depends on the amount of focus on the critical areas of the EC project. The purpose of this research to develop an alternative ECM framework based on critical success factors of ECM. The study follows through three steps: (i) identify the common focus areas of ECM, (ii) identify, from past empirical studies, the critical success factors for ECM, and (iii) develop a proposed framework that incorporates the identified critical success factors for ECM. The proposed ECM framework provides practitioners with a change management process that incorporate ECM critical success factors, to guide in implementation of ECM projects.. This is anticipated to increase the chance of success for the ECM projects.
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- Authors: Mutingi, Michael , Mbohwa, Charles , Mapfaira, Herbert
- Date: 2015-03-03
- Subjects: Engineering change management , Engineering change
- Type: Article
- Identifier: uj:5216 , http://hdl.handle.net/10210/14502
- Description: Effective engineering change management (ECM) procedures are very important over the whole life cycle of every engineering change (EC), from EC proposal to implementation and documentation. However, the success of an EC procedure depends on the amount of focus on the critical areas of the EC project. The purpose of this research to develop an alternative ECM framework based on critical success factors of ECM. The study follows through three steps: (i) identify the common focus areas of ECM, (ii) identify, from past empirical studies, the critical success factors for ECM, and (iii) develop a proposed framework that incorporates the identified critical success factors for ECM. The proposed ECM framework provides practitioners with a change management process that incorporate ECM critical success factors, to guide in implementation of ECM projects.. This is anticipated to increase the chance of success for the ECM projects.
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Complicating factors in healthcare staff scheduling part 1 : case of nurse rostering
- Mutingi, Michael, Mbohwa, Charles
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2015-07-01
- Subjects: Healthcare staff scheduling , Nurse rostering
- Type: Article
- Identifier: uj:5220 , ISBN 978-988-14047-0-1 , http://hdl.handle.net/10210/14506
- Description: Nurse rostering is a hard problem inundated with inherent complicating features. This paper explores case studies on nurse rostering in order identify complicating factors common in the nurse rostering problem. A taxonomy of complicating factors is then derived. Furthermore, a closer look at the complicating factors and the solution methods applied is performed. Inadequacies of the approaches are identified, and suitable approaches derived. The study recommends future methods that are more intelligent, interactive, making use of techniques such fuzzy theory, fuzzy logic, multi-criteria decision making, and expert systems.
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- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2015-07-01
- Subjects: Healthcare staff scheduling , Nurse rostering
- Type: Article
- Identifier: uj:5220 , ISBN 978-988-14047-0-1 , http://hdl.handle.net/10210/14506
- Description: Nurse rostering is a hard problem inundated with inherent complicating features. This paper explores case studies on nurse rostering in order identify complicating factors common in the nurse rostering problem. A taxonomy of complicating factors is then derived. Furthermore, a closer look at the complicating factors and the solution methods applied is performed. Inadequacies of the approaches are identified, and suitable approaches derived. The study recommends future methods that are more intelligent, interactive, making use of techniques such fuzzy theory, fuzzy logic, multi-criteria decision making, and expert systems.
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Complicating factors in healthcare staff scheduling part 2 : case of nurse re-rostering
- Mutingi, Michael, Mbohwa, Charles
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2015-07-01
- Subjects: Healthcare staff scheduling , Nurse re-rostering
- Type: Article
- Identifier: uj:5215 , ISBN 978-988-14047-0-1 , http://hdl.handle.net/10210/14499
- Description: Nurse re-rostering is a highly constrained combinatorial problem characterized with several complicating features. This paper explores recent case studies on nurse re-rostering and identifies the common complicating factors in the nurse re-rostering problem. A taxonomic analysis of complicating factors is then presented. Further, an evaluation of the complicating factors and the solution methods applied, showing the shortfalls of the approaches. A more robust and appropriate approach is realized for the complex problem. Future approaches should be intelligent, interactive, making use of a combination of fuzzy theory, fuzzy logic, multi-criteria decision making, and expert systems techniques.
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- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2015-07-01
- Subjects: Healthcare staff scheduling , Nurse re-rostering
- Type: Article
- Identifier: uj:5215 , ISBN 978-988-14047-0-1 , http://hdl.handle.net/10210/14499
- Description: Nurse re-rostering is a highly constrained combinatorial problem characterized with several complicating features. This paper explores recent case studies on nurse re-rostering and identifies the common complicating factors in the nurse re-rostering problem. A taxonomic analysis of complicating factors is then presented. Further, an evaluation of the complicating factors and the solution methods applied, showing the shortfalls of the approaches. A more robust and appropriate approach is realized for the complex problem. Future approaches should be intelligent, interactive, making use of a combination of fuzzy theory, fuzzy logic, multi-criteria decision making, and expert systems techniques.
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Design of comminution circuits for improved productivity using a multi-objective evolutionary algorithm (MOEA)
- Mhlanga, Samson, Ndlovu, Jabulani, Mbohwa, Charles, Mutingi, Michael
- Authors: Mhlanga, Samson , Ndlovu, Jabulani , Mbohwa, Charles , Mutingi, Michael
- Date: 2011
- Subjects: Comminution circuits , Evolutionary algorithms , Multi-objective optimisation , Multi-objective evolutionary algorithms , Genetic algorithms
- Type: Article
- Identifier: uj:5170 , http://hdl.handle.net/10210/14411
- Description: The performance of a processing plant has a large impact on the profitability of a mining operation, yet plant desig optimisation decisions are based on past experience and intuition rather than on scientific analysis. Genetic algorithms as a tool for circuit analysis in plant design and optimisation was considered. The multi-objective evolutionary algorithm initialises the plant design and optimisation based on experimental results, which are used to formulate and determine the objective function values. A simulation was conducted to assess the performance of candidate solutions. The two optima are then traded-of using cost objective, which is sought to be minimized. Once an optimum was selected, the circuit mass balance and equipment design was performed, bringing the theory of network design and genetic algorithms into unison. Results of the study provide financial benefits, optimal parameter settings for the comminution equipment and ultimate better plant performance.
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- Authors: Mhlanga, Samson , Ndlovu, Jabulani , Mbohwa, Charles , Mutingi, Michael
- Date: 2011
- Subjects: Comminution circuits , Evolutionary algorithms , Multi-objective optimisation , Multi-objective evolutionary algorithms , Genetic algorithms
- Type: Article
- Identifier: uj:5170 , http://hdl.handle.net/10210/14411
- Description: The performance of a processing plant has a large impact on the profitability of a mining operation, yet plant desig optimisation decisions are based on past experience and intuition rather than on scientific analysis. Genetic algorithms as a tool for circuit analysis in plant design and optimisation was considered. The multi-objective evolutionary algorithm initialises the plant design and optimisation based on experimental results, which are used to formulate and determine the objective function values. A simulation was conducted to assess the performance of candidate solutions. The two optima are then traded-of using cost objective, which is sought to be minimized. Once an optimum was selected, the circuit mass balance and equipment design was performed, bringing the theory of network design and genetic algorithms into unison. Results of the study provide financial benefits, optimal parameter settings for the comminution equipment and ultimate better plant performance.
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Developing performance management systems for the green supply chain
- Mutingi, Michael, Mapfaira, Herbert, Monageng, Robert
- Authors: Mutingi, Michael , Mapfaira, Herbert , Monageng, Robert
- Date: 2015-04-15
- Subjects: Performance measurement , Performance management systems , Supply chain management
- Type: Article
- Identifier: uj:5085 , http://hdl.handle.net/10210/13656
- Description: As “green” issues continue to become a global concern in the manufacturing supply chain, developing appropriate performance measurement systems for specific supply chains is imperative. Various green supply chain management strategies have been proposed in different contexts. On the other hand, a number of performance management systems (PMS) have been proposed. However, given the variations in the contexts of the available green strategies and the performance measurement approaches, selecting or developing suitable performance measures and the ensuing PMS under a given supply chain context is not trivial. The purpose of this study is to develop a structured taxonomic approach to developing PMS under various green supply chain conditions, contexts, and business objectives. Therefore, we (i) explore extant empirical studies on green supply chain activities and environmental management, (ii) develop a taxonomy of green supply chain strategies, (iii) derive a structured approach to developing green performance management systems, and, (iv) provide a taxonomic performance measurement framework consisting of environmental, economic and social performance metrics. Unlike past studies, the taxonomic framework forms a practical platform to assist decision makers when developing a suitable set of performance measures and the ultimate PMS while considering the particular context of specific green strategies under which the PMS is supposed to operate.
- Full Text: false
- Authors: Mutingi, Michael , Mapfaira, Herbert , Monageng, Robert
- Date: 2015-04-15
- Subjects: Performance measurement , Performance management systems , Supply chain management
- Type: Article
- Identifier: uj:5085 , http://hdl.handle.net/10210/13656
- Description: As “green” issues continue to become a global concern in the manufacturing supply chain, developing appropriate performance measurement systems for specific supply chains is imperative. Various green supply chain management strategies have been proposed in different contexts. On the other hand, a number of performance management systems (PMS) have been proposed. However, given the variations in the contexts of the available green strategies and the performance measurement approaches, selecting or developing suitable performance measures and the ensuing PMS under a given supply chain context is not trivial. The purpose of this study is to develop a structured taxonomic approach to developing PMS under various green supply chain conditions, contexts, and business objectives. Therefore, we (i) explore extant empirical studies on green supply chain activities and environmental management, (ii) develop a taxonomy of green supply chain strategies, (iii) derive a structured approach to developing green performance management systems, and, (iv) provide a taxonomic performance measurement framework consisting of environmental, economic and social performance metrics. Unlike past studies, the taxonomic framework forms a practical platform to assist decision makers when developing a suitable set of performance measures and the ultimate PMS while considering the particular context of specific green strategies under which the PMS is supposed to operate.
- Full Text: false
Environmental impacts assessment of the platinum nanophase composite electrode by Eco-indicator 99 methodology
- Mabiza, Junior M., Mbohwa, Charles, Mutingi, Michael
- Authors: Mabiza, Junior M. , Mbohwa, Charles , Mutingi, Michael
- Date: 2012
- Subjects: Environmental impact assessments , Eco-indicator 99 , Platinum nanophase composite electrodes
- Type: Article
- Identifier: uj:6018 , http://hdl.handle.net/10210/10033
- Description: Platinum nanophase composite electrode for hydrogen generation by water electrolysis process has to meet sustainable development requirements even in its development phase by reducing GHG emissions to irrelevance. It is therefore important to determine possible emissions, to estimate the energy consumption and identify key parameters in the improvement of the process used to develop the electrode. Eco indicator 99 was used to assess and determine the types of impacts on the environment of the process of the preparation of the composite electrode and Umberto software was used to develop life cycle assessment inventory (LCIA).
- Full Text:
- Authors: Mabiza, Junior M. , Mbohwa, Charles , Mutingi, Michael
- Date: 2012
- Subjects: Environmental impact assessments , Eco-indicator 99 , Platinum nanophase composite electrodes
- Type: Article
- Identifier: uj:6018 , http://hdl.handle.net/10210/10033
- Description: Platinum nanophase composite electrode for hydrogen generation by water electrolysis process has to meet sustainable development requirements even in its development phase by reducing GHG emissions to irrelevance. It is therefore important to determine possible emissions, to estimate the energy consumption and identify key parameters in the improvement of the process used to develop the electrode. Eco indicator 99 was used to assess and determine the types of impacts on the environment of the process of the preparation of the composite electrode and Umberto software was used to develop life cycle assessment inventory (LCIA).
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Fuzzy heuristic approaches for healthcare staff scheduling
- Authors: Mutingi, Michael
- Date: 2015
- Subjects: Health facilities - Personnel management , Nursing services - Administration , Production scheduling - Data processing , Hours of labor - Data processing , Fuzzy systems
- Language: English
- Type: Doctoral (Thesis)
- Identifier: http://hdl.handle.net/10210/56218 , uj:16345
- Description: Abstract: Healthcare staff scheduling is often inundated with fuzzy conflicting (i) patient preferences (ii) staff preferences, and (iii) management goals. In such a fuzzy multi-criteria situation, the decision maker needs interactive fuzzy evaluation heuristics for effective decision making. Hence, the aim of this thesis is to develop fuzzy multi-criteria heuristic approaches for solving healthcare staff scheduling problems. This thesis comprises three parts: The first part develops multi-criteria fuzzy heuristic approaches to address nurse scheduling problems with conflicting fuzzy goals and nurse preferences. An enhanced fuzzy simulated evolution algorithm and a novel fuzzy simulated metamorphosis algorithm are developed, based on fuzzy evaluation techniques and problem specific heuristics. The approaches can model fuzzy preferences, incorporate decision maker’s choices, and provide reliable solutions efficiently. The second part focuses on homecare staff scheduling in a home healthcare setting where management goals, staff preferences, and patient preferences are fuzzy. The objective is to construct high quality schedules with minimal violation of patient preferences, fair staff workload, and minimal schedules costs. A novel grouping particle swarm optimization algorithm is proposed for the problem. Computational results show that the algorithm can efficiently provide a pool of optimal or near-optimal solutions. The third part focuses on daily assignment of healthcare tasks to care workers in a hospital setting, so that patients receive the expected healthcare service, howbeit, with minimal violation of restrictions on care giver capacity and task precedence relationships. By viewing the problem... , D.Ing.
- Full Text:
- Authors: Mutingi, Michael
- Date: 2015
- Subjects: Health facilities - Personnel management , Nursing services - Administration , Production scheduling - Data processing , Hours of labor - Data processing , Fuzzy systems
- Language: English
- Type: Doctoral (Thesis)
- Identifier: http://hdl.handle.net/10210/56218 , uj:16345
- Description: Abstract: Healthcare staff scheduling is often inundated with fuzzy conflicting (i) patient preferences (ii) staff preferences, and (iii) management goals. In such a fuzzy multi-criteria situation, the decision maker needs interactive fuzzy evaluation heuristics for effective decision making. Hence, the aim of this thesis is to develop fuzzy multi-criteria heuristic approaches for solving healthcare staff scheduling problems. This thesis comprises three parts: The first part develops multi-criteria fuzzy heuristic approaches to address nurse scheduling problems with conflicting fuzzy goals and nurse preferences. An enhanced fuzzy simulated evolution algorithm and a novel fuzzy simulated metamorphosis algorithm are developed, based on fuzzy evaluation techniques and problem specific heuristics. The approaches can model fuzzy preferences, incorporate decision maker’s choices, and provide reliable solutions efficiently. The second part focuses on homecare staff scheduling in a home healthcare setting where management goals, staff preferences, and patient preferences are fuzzy. The objective is to construct high quality schedules with minimal violation of patient preferences, fair staff workload, and minimal schedules costs. A novel grouping particle swarm optimization algorithm is proposed for the problem. Computational results show that the algorithm can efficiently provide a pool of optimal or near-optimal solutions. The third part focuses on daily assignment of healthcare tasks to care workers in a hospital setting, so that patients receive the expected healthcare service, howbeit, with minimal violation of restrictions on care giver capacity and task precedence relationships. By viewing the problem... , D.Ing.
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Fuzzy multi-criteria simulated evolution for nurse re-rostering
- Mutingi, Michael, Mbohwa, Charles
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2016
- Subjects: Fuzzy simulated evolution , Nurse re-rostering
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/55541 , uj:16299 , Citation: Mutingi, M. & Mbohwa, C. 2016. Fuzzy multi-criteria simulated evolution for nurse re-rostering. 2016. Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management, Kuala Lumpur, Malaysia, March 8-10, 2016:2025-2033. , ISBN: 978-1-4673-7762-1
- Description: Abstract: In a fuzzy environment where the decision making involves multiple criteria, fuzzy multi-criteria decision making approaches are a viable option. The nurse re-rostering problem is a typical complex problem situation, where scheduling decisions should consider fuzzy human preferences, such as nurse preferences, decision maker’s choices, and patient expectations. For effective nurse schedules, fuzzy theoretic evaluation approaches have to be used to incorporate the fuzzy human preferences and choices. The present study seeks to develop a fuzzy multi-criteria simulated evolution approach for the nurse re-rostering problem. Experimental results show that the fuzzy multi-criteria approach has a potential to solve large scale problems within reasonable computation times.
- Full Text:
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2016
- Subjects: Fuzzy simulated evolution , Nurse re-rostering
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/55541 , uj:16299 , Citation: Mutingi, M. & Mbohwa, C. 2016. Fuzzy multi-criteria simulated evolution for nurse re-rostering. 2016. Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management, Kuala Lumpur, Malaysia, March 8-10, 2016:2025-2033. , ISBN: 978-1-4673-7762-1
- Description: Abstract: In a fuzzy environment where the decision making involves multiple criteria, fuzzy multi-criteria decision making approaches are a viable option. The nurse re-rostering problem is a typical complex problem situation, where scheduling decisions should consider fuzzy human preferences, such as nurse preferences, decision maker’s choices, and patient expectations. For effective nurse schedules, fuzzy theoretic evaluation approaches have to be used to incorporate the fuzzy human preferences and choices. The present study seeks to develop a fuzzy multi-criteria simulated evolution approach for the nurse re-rostering problem. Experimental results show that the fuzzy multi-criteria approach has a potential to solve large scale problems within reasonable computation times.
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Grouping genetic algorithm for industrial engineering applications
- Mutingi, Michael, Mapfaira, Herbert, Dube, Partson
- Authors: Mutingi, Michael , Mapfaira, Herbert , Dube, Partson
- Date: 2013
- Subjects: Grouping problems , Grouping genetic algorithms , Genetic algorithms , Metaheuristics , Industrial engineering
- Type: Article
- Identifier: uj:4734 , http://hdl.handle.net/10210/11560
- Description: Industry is inundated with grouping problems concerned with formation of groups or clusters of system entities for the purpose of improving the overall system efficiency and effectiveness. Various extant grouping problems include cell formation problem, vehicle routing problem, bin packing problem, truck loading, home healthcare scheduling, and task assignment problem. Given the widespread grouping problems in industry, it is important to develop a tool for solving such problems from a common view point. This paper seeks to identify common grouping problems, identify their common grouping structures, present an outline of group genetic algorithm (GGA), and map the problems to the GGA approach. The practicality of the GGA tool in is highly promising in Industrial Engineering applications.
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- Authors: Mutingi, Michael , Mapfaira, Herbert , Dube, Partson
- Date: 2013
- Subjects: Grouping problems , Grouping genetic algorithms , Genetic algorithms , Metaheuristics , Industrial engineering
- Type: Article
- Identifier: uj:4734 , http://hdl.handle.net/10210/11560
- Description: Industry is inundated with grouping problems concerned with formation of groups or clusters of system entities for the purpose of improving the overall system efficiency and effectiveness. Various extant grouping problems include cell formation problem, vehicle routing problem, bin packing problem, truck loading, home healthcare scheduling, and task assignment problem. Given the widespread grouping problems in industry, it is important to develop a tool for solving such problems from a common view point. This paper seeks to identify common grouping problems, identify their common grouping structures, present an outline of group genetic algorithm (GGA), and map the problems to the GGA approach. The practicality of the GGA tool in is highly promising in Industrial Engineering applications.
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Healthcare staff scheduling in a fuzzy environment : a fuzzy genetic algorithm approach
- Mutingi, Michael, Mbohwa, Charles
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2014
- Subjects: Healthcare staff scheduling , Nurse scheduling , Fuzzy modeling , Genetic algorithms , Jarosite precipitate
- Type: Article
- Identifier: uj:4970 , http://hdl.handle.net/10210/13071
- Description: In the presence of imprecise management targets, staff preferences, and patients’ expectations, the healthcare staff scheduling problem becomes complicated. The goals, preferences, and client expectations, being humanistic, are often imprecise and always evolving over time. We present a Jarosite precipitate (FGA) approach for addressing healthcare staff scheduling problems in fuzzy environments. The proposed FGA-based approach can handle multiple conflicting objectives and constraints. To improve the algorithm, fuzzy set theory is used for fitness evaluations of alternative candidate schedules by modeling the fitness of each alternative solution using fuzzy membership functions. Furthermore, the algorithm is designed to incorporate the decision maker’s choices and preferences, in addition to staff preferences. Rather than prescribing a sing solution to the decision maker, the approach provides a population of alternative solutions from which the decision maker can choose the most satisfactory solution. The FGA-based approach is potential platform upon which useful decision support tools can be developing for solving healthcare staff scheduling problems in a fuzzy environment characterized with multiple conflicting objectives and preference constraints.
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- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2014
- Subjects: Healthcare staff scheduling , Nurse scheduling , Fuzzy modeling , Genetic algorithms , Jarosite precipitate
- Type: Article
- Identifier: uj:4970 , http://hdl.handle.net/10210/13071
- Description: In the presence of imprecise management targets, staff preferences, and patients’ expectations, the healthcare staff scheduling problem becomes complicated. The goals, preferences, and client expectations, being humanistic, are often imprecise and always evolving over time. We present a Jarosite precipitate (FGA) approach for addressing healthcare staff scheduling problems in fuzzy environments. The proposed FGA-based approach can handle multiple conflicting objectives and constraints. To improve the algorithm, fuzzy set theory is used for fitness evaluations of alternative candidate schedules by modeling the fitness of each alternative solution using fuzzy membership functions. Furthermore, the algorithm is designed to incorporate the decision maker’s choices and preferences, in addition to staff preferences. Rather than prescribing a sing solution to the decision maker, the approach provides a population of alternative solutions from which the decision maker can choose the most satisfactory solution. The FGA-based approach is potential platform upon which useful decision support tools can be developing for solving healthcare staff scheduling problems in a fuzzy environment characterized with multiple conflicting objectives and preference constraints.
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Home healthcare staff scheduling: a clustering particle swarm optimization approach
- Mutingi, Michael, Mbohwa, Charles
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2014
- Subjects: Healthcare service providers , Particle swarm optimization , Healthcare staff scheduling
- Type: Article
- Identifier: uj:4973 , http://hdl.handle.net/10210/13074
- Description: The home healthcare staff scheduling problem is concerned with the allocation of care tasks to healthcare staff at a minimal cost, subject to healthcare service requirements, labor law, organizational requirements, staff preferences, and other restrictions. Healthcare service providers strive to meet the time window restrictions specified by the patients to improve their service quality. This paper proposes a clustering particle swam optimization methodology (CPSO) for addressing the scheduling problem. The approach utilizes the strengths of unique grouping techniques to efficiently exploit the group structure of the scheduling problem, enabling the algorithm to provide good solutions within reasonable computation times. Computational results obtained in this study demonstrate the efficiency and effectiveness of CPSO approach.
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- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2014
- Subjects: Healthcare service providers , Particle swarm optimization , Healthcare staff scheduling
- Type: Article
- Identifier: uj:4973 , http://hdl.handle.net/10210/13074
- Description: The home healthcare staff scheduling problem is concerned with the allocation of care tasks to healthcare staff at a minimal cost, subject to healthcare service requirements, labor law, organizational requirements, staff preferences, and other restrictions. Healthcare service providers strive to meet the time window restrictions specified by the patients to improve their service quality. This paper proposes a clustering particle swam optimization methodology (CPSO) for addressing the scheduling problem. The approach utilizes the strengths of unique grouping techniques to efficiently exploit the group structure of the scheduling problem, enabling the algorithm to provide good solutions within reasonable computation times. Computational results obtained in this study demonstrate the efficiency and effectiveness of CPSO approach.
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Integrated cellular manufacturing system design : an evolutionary algorithm approach
- Mutingi, Michael, Mbohwa, Charles, Mhlanga, Samson, Goriwondo, William
- Authors: Mutingi, Michael , Mbohwa, Charles , Mhlanga, Samson , Goriwondo, William
- Date: 2012-07-03
- Subjects: Evolutionary algorithms , Cellular manufacturing , Plant layout , Building layout
- Type: Article
- Identifier: uj:5179 , http://hdl.handle.net/10210/14421
- Description: Cellular manufacturing system design has received much attention for the past three decades. The design process involves decisions on (i) cell formation, (ii) cell layout, and (iii) layout of cells on the shop floor. These decisions should be addressed jointly, if full benefits of cellular manufacturing are to be realised. However, due to the complexity of the problem, most researchers addressed these phases sequentially. In this paper, we propose an enhanced evolutionary algorithm to jointly address cell formation and layout problems, based on sequence data. The approach compares favourably to well-known heuristics and performed well on published data sets, providing improved solutions.
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- Authors: Mutingi, Michael , Mbohwa, Charles , Mhlanga, Samson , Goriwondo, William
- Date: 2012-07-03
- Subjects: Evolutionary algorithms , Cellular manufacturing , Plant layout , Building layout
- Type: Article
- Identifier: uj:5179 , http://hdl.handle.net/10210/14421
- Description: Cellular manufacturing system design has received much attention for the past three decades. The design process involves decisions on (i) cell formation, (ii) cell layout, and (iii) layout of cells on the shop floor. These decisions should be addressed jointly, if full benefits of cellular manufacturing are to be realised. However, due to the complexity of the problem, most researchers addressed these phases sequentially. In this paper, we propose an enhanced evolutionary algorithm to jointly address cell formation and layout problems, based on sequence data. The approach compares favourably to well-known heuristics and performed well on published data sets, providing improved solutions.
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Lean healthcare implementation in Southern Africa : a SWOT analysis
- Mutingi, Michael, Monageng, Robert, Mbohwa, Charles
- Authors: Mutingi, Michael , Monageng, Robert , Mbohwa, Charles
- Date: 2015-07-01
- Subjects: Lean healthcare , SWOT analysis
- Type: Article
- Identifier: uj:5210 , ISBN 978-988-14047-0-1 , http://hdl.handle.net/10210/14494
- Description: As more and more healthcare service providers realize the imperative of improving quality and eliminating waste, lean healthcare is increasingly becoming a strong initiative. Though the concepts of lean have been frequently presented and advocated, the current state of adoption in Southern African countries faces challenges. There still exist a number of different perspectives as to what lean is fundamentally capable of in the healthcare setting. In this paper, we present an analysis of the strengths, weaknesses, opportunities, and threats associated with the application of the lean philosophy in healthcare. We collate expert views from a number of leading consultants, practitioners and academics from the Southern African region. The leading expert participants were selected based on their good knowledge and expertise in the field of lean. The study provides a useful resource for many researchers and practitioners concerned with research and application of improvement methodologies in healthcare to transform their healthcare organizations into high-performing healthcare delivery systems.
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- Authors: Mutingi, Michael , Monageng, Robert , Mbohwa, Charles
- Date: 2015-07-01
- Subjects: Lean healthcare , SWOT analysis
- Type: Article
- Identifier: uj:5210 , ISBN 978-988-14047-0-1 , http://hdl.handle.net/10210/14494
- Description: As more and more healthcare service providers realize the imperative of improving quality and eliminating waste, lean healthcare is increasingly becoming a strong initiative. Though the concepts of lean have been frequently presented and advocated, the current state of adoption in Southern African countries faces challenges. There still exist a number of different perspectives as to what lean is fundamentally capable of in the healthcare setting. In this paper, we present an analysis of the strengths, weaknesses, opportunities, and threats associated with the application of the lean philosophy in healthcare. We collate expert views from a number of leading consultants, practitioners and academics from the Southern African region. The leading expert participants were selected based on their good knowledge and expertise in the field of lean. The study provides a useful resource for many researchers and practitioners concerned with research and application of improvement methodologies in healthcare to transform their healthcare organizations into high-performing healthcare delivery systems.
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