The impact of quality management tools in municipal water distributors : case of Namibia
- Mutingi, Michael, Silombela, Timothy, Mashauri, Damas, Mbohwa, Charles
- Authors: Mutingi, Michael , Silombela, Timothy , Mashauri, Damas , Mbohwa, Charles
- Date: 2016
- Subjects: Total quality management , Municipal water supply - Namibia - Management
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/55529 , uj:16298 , Citation: Mutingi, M. et al. 2016. The impact of quality management tools in municipal water distributors : case of Namibia. Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management, Kuala Lumpur, Malaysia, March 8-10, 2016:2188-2196. , ISBN:978-1-4673-7762-1
- Description: Abstract: Municipalities always face the challenge of water losses (known as non-revenue water (NRW)) due to poor maintenance of water supply infrastructure. This research investigates the impact of Quality Management (QM) Tools in the maintenance function of Namibia Municipal Water Distributors. The study reveals that 18 municipalities use an average of 8 QM tools and produce an average of 23% Non-Revenue Water (NRW), which is 3% higher than the amount recommended by the International Water Association (IWA) for well managed municipalities. Only 6 out of the 18 municipalities can be classified as well managed. The study shows that the application of QM tools is still low, considering that there are over 100 available QM tools. Surprisingly, the study shows that user friendliness has little influence on tool adoption, contrary to the initial hypothesis. QM tools are adopted because they are perceived useful. It is concluded that the application of QM tools helps to reduce the generation of NRW. Furthermore, the study finds that there is a marginal negative correlation between the use of QM tools and the generation of NRW. This confirms that the application of QM tools has a positive impact on the reduction of NRW generation.
- Full Text:
- Authors: Mutingi, Michael , Silombela, Timothy , Mashauri, Damas , Mbohwa, Charles
- Date: 2016
- Subjects: Total quality management , Municipal water supply - Namibia - Management
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/55529 , uj:16298 , Citation: Mutingi, M. et al. 2016. The impact of quality management tools in municipal water distributors : case of Namibia. Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management, Kuala Lumpur, Malaysia, March 8-10, 2016:2188-2196. , ISBN:978-1-4673-7762-1
- Description: Abstract: Municipalities always face the challenge of water losses (known as non-revenue water (NRW)) due to poor maintenance of water supply infrastructure. This research investigates the impact of Quality Management (QM) Tools in the maintenance function of Namibia Municipal Water Distributors. The study reveals that 18 municipalities use an average of 8 QM tools and produce an average of 23% Non-Revenue Water (NRW), which is 3% higher than the amount recommended by the International Water Association (IWA) for well managed municipalities. Only 6 out of the 18 municipalities can be classified as well managed. The study shows that the application of QM tools is still low, considering that there are over 100 available QM tools. Surprisingly, the study shows that user friendliness has little influence on tool adoption, contrary to the initial hypothesis. QM tools are adopted because they are perceived useful. It is concluded that the application of QM tools helps to reduce the generation of NRW. Furthermore, the study finds that there is a marginal negative correlation between the use of QM tools and the generation of NRW. This confirms that the application of QM tools has a positive impact on the reduction of NRW generation.
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System reliability optimization : a fuzzy genetic algorithm approach
- Mutingi, Michael, Mbohwa, Charles
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2013
- Subjects: System reliability optimization , Multi-objective optimization , Genetic algorithm , Fuzzy optimization
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/376308 , uj:4944 , http://hdl.handle.net/10210/13044
- Description: System reliability optimization is often faced with imprecise and conflicting goals such as reducing the cost of the system and improving the reliability of the system. The decision making process becomes fuzzy and multi-objective. In this paper, we formulate the problem as a fuzzy multi-objective nonlinear program (FMOOP). A fuzzy multiobjective genetic algorithm approach (FMGA) is proposed for solving the multi-objective decision problem in order to handle the fuzzy goals and constraints. The approach is able flexible and adaptable, allowing for intermediate solutions, leading to high quality solutions. Thus, the approach incorporates the preferences of the decision maker concerning the cost and reliability goals through the use of fuzzy numbers. The utility of the approach is demonstrated on benchmark problems in the literature. Computational results show that the FMGA approach is promising.
- Full Text:
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2013
- Subjects: System reliability optimization , Multi-objective optimization , Genetic algorithm , Fuzzy optimization
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/376308 , uj:4944 , http://hdl.handle.net/10210/13044
- Description: System reliability optimization is often faced with imprecise and conflicting goals such as reducing the cost of the system and improving the reliability of the system. The decision making process becomes fuzzy and multi-objective. In this paper, we formulate the problem as a fuzzy multi-objective nonlinear program (FMOOP). A fuzzy multiobjective genetic algorithm approach (FMGA) is proposed for solving the multi-objective decision problem in order to handle the fuzzy goals and constraints. The approach is able flexible and adaptable, allowing for intermediate solutions, leading to high quality solutions. Thus, the approach incorporates the preferences of the decision maker concerning the cost and reliability goals through the use of fuzzy numbers. The utility of the approach is demonstrated on benchmark problems in the literature. Computational results show that the FMGA approach is promising.
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Simulated metamorphosis - a novel optimizer
- Mutingi, Michael, Mbohwa, Charles
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2014
- Subjects: Metamorphosis , Evolution , Optimization , Algorithm , Metaheuristics , Simulated metamorphosis
- Type: Article
- Identifier: uj:4972 , ISSN 2078-0966 , http://hdl.handle.net/10210/13073
- Description: This paper presents a novel metaheuristic algorithm, simulated metamorphosis (SM), inspired by the biological concepts of metamorphosis evolution. The algorithm is motivated by the need for interactive, multi-objective, and fast optimization approaches to solving problems with fuzzy conflicting goals and constraints. The algorithm mimics the metamorphosis process, going through three phases: initialization, growth, and maturation. Initialization involves random but guided generation of a candidate solution. After initialization, the algorithm successively goes through two loops, that is, growth and maturation. Computational tests performed on benchmark problems in the literature show that, when compared to competing metaheuristic algorithms, SM is more efficient and effective, producing better solutions within reasonable computation times.
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- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2014
- Subjects: Metamorphosis , Evolution , Optimization , Algorithm , Metaheuristics , Simulated metamorphosis
- Type: Article
- Identifier: uj:4972 , ISSN 2078-0966 , http://hdl.handle.net/10210/13073
- Description: This paper presents a novel metaheuristic algorithm, simulated metamorphosis (SM), inspired by the biological concepts of metamorphosis evolution. The algorithm is motivated by the need for interactive, multi-objective, and fast optimization approaches to solving problems with fuzzy conflicting goals and constraints. The algorithm mimics the metamorphosis process, going through three phases: initialization, growth, and maturation. Initialization involves random but guided generation of a candidate solution. After initialization, the algorithm successively goes through two loops, that is, growth and maturation. Computational tests performed on benchmark problems in the literature show that, when compared to competing metaheuristic algorithms, SM is more efficient and effective, producing better solutions within reasonable computation times.
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Multi-criteria reliability optimization for a complex system with a bridge structure in a fuzzy environment : A fuzzy multi-criteria genetic algorithm approach
- Mutingi, Michael, Mbohwa, Charles, Kommula, Venkata P.
- Authors: Mutingi, Michael , Mbohwa, Charles , Kommula, Venkata P.
- Date: 2016
- Subjects: Multi-criteria optimization, , Reliability optimization , Complex bridge system
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/216965 , uj:21579 , Citation: Mutingi, M., Mbohwa, C & Kommula, V. 2016. Multi-criteria reliability optimization for a complex system with a bridge structure in a fuzzy environment : A fuzzy multi-criteria genetic algorithm approach.
- Description: Abstract: Optimizing system reliability in a fuzzy environment is complex due to the presence of imprecise multiple decision criteria such as maximizing system reliability and minimizing system cost. This calls for multi-criteria decision making approaches that incorporate fuzzy set theory concepts and heuristic methods. This paper presents a fuzzy multi-criteria nonlinear model, and proposes a fuzzy multi-criteria genetic algorithm (FMGA) for complex bridge system reliability design in a fuzzy environment. The algorithm uses fuzzy multi-criteria evaluation techniques to handle fuzzy goals, preferences, and constraints. The evaluation approach incorporates fuzzy preferences and expert choices of the decision maker in regards to cost and reliability goals. Fuzzy evaluation gives the algorithm flexibility and adaptability, yielding near-optimal solutions within short computation times. Results from computational experiments based on benchmark problems demonstrate that the FMGA approach is a more reliable and effective approach than best known algorithm, especially in a fuzzy multi-criteria environment.
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- Authors: Mutingi, Michael , Mbohwa, Charles , Kommula, Venkata P.
- Date: 2016
- Subjects: Multi-criteria optimization, , Reliability optimization , Complex bridge system
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/216965 , uj:21579 , Citation: Mutingi, M., Mbohwa, C & Kommula, V. 2016. Multi-criteria reliability optimization for a complex system with a bridge structure in a fuzzy environment : A fuzzy multi-criteria genetic algorithm approach.
- Description: Abstract: Optimizing system reliability in a fuzzy environment is complex due to the presence of imprecise multiple decision criteria such as maximizing system reliability and minimizing system cost. This calls for multi-criteria decision making approaches that incorporate fuzzy set theory concepts and heuristic methods. This paper presents a fuzzy multi-criteria nonlinear model, and proposes a fuzzy multi-criteria genetic algorithm (FMGA) for complex bridge system reliability design in a fuzzy environment. The algorithm uses fuzzy multi-criteria evaluation techniques to handle fuzzy goals, preferences, and constraints. The evaluation approach incorporates fuzzy preferences and expert choices of the decision maker in regards to cost and reliability goals. Fuzzy evaluation gives the algorithm flexibility and adaptability, yielding near-optimal solutions within short computation times. Results from computational experiments based on benchmark problems demonstrate that the FMGA approach is a more reliable and effective approach than best known algorithm, especially in a fuzzy multi-criteria environment.
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Lean manufacturing adoption and implementation barriers in Botswana manufacturing companies
- Mapfaira, Herbert, Mutingi, Michael, Lefatshe, Koketso, Mashaba, Thabo
- Authors: Mapfaira, Herbert , Mutingi, Michael , Lefatshe, Koketso , Mashaba, Thabo
- Date: 2014
- Subjects: Lean manufacturing - Botswana , Manufacturing industries - Botswana , Industrial productivity - Botswana
- Type: Article
- Identifier: uj:5098 , http://hdl.handle.net/10210/13743
- Description: Lean manufacturing, has been identified as one of the most powerful productivity improvement tools. Many accompanies across the world, especially in the developed economies have aggressively implemented lean, resulting in drastic improvements in productivity. Since 2008, Botswana has suffered from a decline in productivity. Though productivity awareness training has been provided to Botswana business, productivity remains very low and on a downward trend. This indicates that there are either barriers stopping companies from adoption of productivity improvement tools or hindering the successful implementation of productivity improvement tools. The main purpose of this study was to investigate lean manufacturing adoption and implementation barriers for Botswana manufacturing industry. The study was carried out through a survey of manufacturing companies in Botswana. Results indicate that most manufacturing companies are unfamiliar with productivity improvement tools or lack the technical know-how of implementing the tools.
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- Authors: Mapfaira, Herbert , Mutingi, Michael , Lefatshe, Koketso , Mashaba, Thabo
- Date: 2014
- Subjects: Lean manufacturing - Botswana , Manufacturing industries - Botswana , Industrial productivity - Botswana
- Type: Article
- Identifier: uj:5098 , http://hdl.handle.net/10210/13743
- Description: Lean manufacturing, has been identified as one of the most powerful productivity improvement tools. Many accompanies across the world, especially in the developed economies have aggressively implemented lean, resulting in drastic improvements in productivity. Since 2008, Botswana has suffered from a decline in productivity. Though productivity awareness training has been provided to Botswana business, productivity remains very low and on a downward trend. This indicates that there are either barriers stopping companies from adoption of productivity improvement tools or hindering the successful implementation of productivity improvement tools. The main purpose of this study was to investigate lean manufacturing adoption and implementation barriers for Botswana manufacturing industry. The study was carried out through a survey of manufacturing companies in Botswana. Results indicate that most manufacturing companies are unfamiliar with productivity improvement tools or lack the technical know-how of implementing the tools.
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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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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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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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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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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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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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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.
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- 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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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).
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- 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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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
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.
- Full Text:
- 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.
- Full Text:
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.
- Full Text:
- 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.
- Full Text:
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.
- Full Text:
- 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.
- Full Text:
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.
- Full Text:
- 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.
- Full Text:
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.
- Full Text:
- 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.
- Full Text:
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.
- Full Text:
- 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.
- Full Text: