Two-level principal–agent model for schedule risk control of IT outsourcing project based on genetic algorithm
With increasing developments in the Information Technology (IT) outsourcing industry, many enterprises outsource IT services to reduce costs. However, the schedule risk of IT outsourcing (ITO) projects may result in enormous economic losses for an enterprise. In this paper, the principal–agent theor...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bi, Hualing [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation - Liu, Xiang ELSEVIER, 2015, the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:91 ; year:2020 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.engappai.2020.103584 |
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ELV050004840 |
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| 520 | |a With increasing developments in the Information Technology (IT) outsourcing industry, many enterprises outsource IT services to reduce costs. However, the schedule risk of IT outsourcing (ITO) projects may result in enormous economic losses for an enterprise. In this paper, the principal–agent theory is used to control the schedule risk of ITO projects. A two-level mathematical model is built to describe the decision process of the client and vendors. With an increase to the number of subprojects and activities, the scale of the problem will become very large. The resulting optimization is an NP hard problem with continuous domain. Therefore, a genetic algorithm (GA) is designed to solve the proposed model. Experiments are performed to test the ability of the proposed algorithm. Some insights from simulation analysis – the principal–agent theory and two-level mathematical model – are suitable for describing the cooperative relationship between principle and agent. By comparing with ant colony optimization and simulated annealing, the proposed GA shows strong optimization abilities for convergence, reliability, and efficiency, which is a good tool for this kind of optimization problem. The near-optimal plan reduced the schedule risk of the project remarkably, which is the scientific quantitative proposal for the decision maker. This study provides practitioners insights on relationships of schedule risk and ITO projects, and the design model and algorithms of this paper provides practitioners effective potential method to reduce the schedule risk of ITO projects in their operations. However, the uncertain characteristics of key and multiple factors should be considered in future work. Stochastic Programming and the Monte Carlo Simulation Method are two potential tools for dealing with uncertain factors. Additionally, the proposed GA could potentially be improved in terms of convergence. The advantages of other intelligent algorithms could be applied to the GA in order to improve its searching ability, such as the Taboo mechanism. | ||
| 520 | |a With increasing developments in the Information Technology (IT) outsourcing industry, many enterprises outsource IT services to reduce costs. However, the schedule risk of IT outsourcing (ITO) projects may result in enormous economic losses for an enterprise. In this paper, the principal–agent theory is used to control the schedule risk of ITO projects. A two-level mathematical model is built to describe the decision process of the client and vendors. With an increase to the number of subprojects and activities, the scale of the problem will become very large. The resulting optimization is an NP hard problem with continuous domain. Therefore, a genetic algorithm (GA) is designed to solve the proposed model. Experiments are performed to test the ability of the proposed algorithm. Some insights from simulation analysis – the principal–agent theory and two-level mathematical model – are suitable for describing the cooperative relationship between principle and agent. By comparing with ant colony optimization and simulated annealing, the proposed GA shows strong optimization abilities for convergence, reliability, and efficiency, which is a good tool for this kind of optimization problem. The near-optimal plan reduced the schedule risk of the project remarkably, which is the scientific quantitative proposal for the decision maker. This study provides practitioners insights on relationships of schedule risk and ITO projects, and the design model and algorithms of this paper provides practitioners effective potential method to reduce the schedule risk of ITO projects in their operations. However, the uncertain characteristics of key and multiple factors should be considered in future work. Stochastic Programming and the Monte Carlo Simulation Method are two potential tools for dealing with uncertain factors. Additionally, the proposed GA could potentially be improved in terms of convergence. The advantages of other intelligent algorithms could be applied to the GA in order to improve its searching ability, such as the Taboo mechanism. | ||
| 650 | 7 | |a Principal–agent theory |2 Elsevier | |
| 650 | 7 | |a Risk control |2 Elsevier | |
| 650 | 7 | |a Genetic algorithm |2 Elsevier | |
| 650 | 7 | |a IT outsourcing project |2 Elsevier | |
| 650 | 7 | |a Schedule risk |2 Elsevier | |
| 700 | 1 | |a Lu, Fuqiang |4 oth | |
| 700 | 1 | |a Duan, Shupeng |4 oth | |
| 700 | 1 | |a Huang, Min |4 oth | |
| 700 | 1 | |a Zhu, Jinwen |4 oth | |
| 700 | 1 | |a Liu, Mengying |4 oth | |
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10.1016/j.engappai.2020.103584 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000977.pica (DE-627)ELV050004840 (ELSEVIER)S0952-1976(20)30062-2 DE-627 ger DE-627 rakwb eng 540 VZ 610 VZ 44.00 bkl Bi, Hualing verfasserin aut Two-level principal–agent model for schedule risk control of IT outsourcing project based on genetic algorithm 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With increasing developments in the Information Technology (IT) outsourcing industry, many enterprises outsource IT services to reduce costs. However, the schedule risk of IT outsourcing (ITO) projects may result in enormous economic losses for an enterprise. In this paper, the principal–agent theory is used to control the schedule risk of ITO projects. A two-level mathematical model is built to describe the decision process of the client and vendors. With an increase to the number of subprojects and activities, the scale of the problem will become very large. The resulting optimization is an NP hard problem with continuous domain. Therefore, a genetic algorithm (GA) is designed to solve the proposed model. Experiments are performed to test the ability of the proposed algorithm. Some insights from simulation analysis – the principal–agent theory and two-level mathematical model – are suitable for describing the cooperative relationship between principle and agent. By comparing with ant colony optimization and simulated annealing, the proposed GA shows strong optimization abilities for convergence, reliability, and efficiency, which is a good tool for this kind of optimization problem. The near-optimal plan reduced the schedule risk of the project remarkably, which is the scientific quantitative proposal for the decision maker. This study provides practitioners insights on relationships of schedule risk and ITO projects, and the design model and algorithms of this paper provides practitioners effective potential method to reduce the schedule risk of ITO projects in their operations. However, the uncertain characteristics of key and multiple factors should be considered in future work. Stochastic Programming and the Monte Carlo Simulation Method are two potential tools for dealing with uncertain factors. Additionally, the proposed GA could potentially be improved in terms of convergence. The advantages of other intelligent algorithms could be applied to the GA in order to improve its searching ability, such as the Taboo mechanism. With increasing developments in the Information Technology (IT) outsourcing industry, many enterprises outsource IT services to reduce costs. However, the schedule risk of IT outsourcing (ITO) projects may result in enormous economic losses for an enterprise. In this paper, the principal–agent theory is used to control the schedule risk of ITO projects. A two-level mathematical model is built to describe the decision process of the client and vendors. With an increase to the number of subprojects and activities, the scale of the problem will become very large. The resulting optimization is an NP hard problem with continuous domain. Therefore, a genetic algorithm (GA) is designed to solve the proposed model. Experiments are performed to test the ability of the proposed algorithm. Some insights from simulation analysis – the principal–agent theory and two-level mathematical model – are suitable for describing the cooperative relationship between principle and agent. By comparing with ant colony optimization and simulated annealing, the proposed GA shows strong optimization abilities for convergence, reliability, and efficiency, which is a good tool for this kind of optimization problem. The near-optimal plan reduced the schedule risk of the project remarkably, which is the scientific quantitative proposal for the decision maker. This study provides practitioners insights on relationships of schedule risk and ITO projects, and the design model and algorithms of this paper provides practitioners effective potential method to reduce the schedule risk of ITO projects in their operations. However, the uncertain characteristics of key and multiple factors should be considered in future work. Stochastic Programming and the Monte Carlo Simulation Method are two potential tools for dealing with uncertain factors. Additionally, the proposed GA could potentially be improved in terms of convergence. The advantages of other intelligent algorithms could be applied to the GA in order to improve its searching ability, such as the Taboo mechanism. Principal–agent theory Elsevier Risk control Elsevier Genetic algorithm Elsevier IT outsourcing project Elsevier Schedule risk Elsevier Lu, Fuqiang oth Duan, Shupeng oth Huang, Min oth Zhu, Jinwen oth Liu, Mengying oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:91 year:2020 pages:0 https://doi.org/10.1016/j.engappai.2020.103584 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 91 2020 0 |
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10.1016/j.engappai.2020.103584 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000977.pica (DE-627)ELV050004840 (ELSEVIER)S0952-1976(20)30062-2 DE-627 ger DE-627 rakwb eng 540 VZ 610 VZ 44.00 bkl Bi, Hualing verfasserin aut Two-level principal–agent model for schedule risk control of IT outsourcing project based on genetic algorithm 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With increasing developments in the Information Technology (IT) outsourcing industry, many enterprises outsource IT services to reduce costs. However, the schedule risk of IT outsourcing (ITO) projects may result in enormous economic losses for an enterprise. In this paper, the principal–agent theory is used to control the schedule risk of ITO projects. A two-level mathematical model is built to describe the decision process of the client and vendors. With an increase to the number of subprojects and activities, the scale of the problem will become very large. The resulting optimization is an NP hard problem with continuous domain. Therefore, a genetic algorithm (GA) is designed to solve the proposed model. Experiments are performed to test the ability of the proposed algorithm. Some insights from simulation analysis – the principal–agent theory and two-level mathematical model – are suitable for describing the cooperative relationship between principle and agent. By comparing with ant colony optimization and simulated annealing, the proposed GA shows strong optimization abilities for convergence, reliability, and efficiency, which is a good tool for this kind of optimization problem. The near-optimal plan reduced the schedule risk of the project remarkably, which is the scientific quantitative proposal for the decision maker. This study provides practitioners insights on relationships of schedule risk and ITO projects, and the design model and algorithms of this paper provides practitioners effective potential method to reduce the schedule risk of ITO projects in their operations. However, the uncertain characteristics of key and multiple factors should be considered in future work. Stochastic Programming and the Monte Carlo Simulation Method are two potential tools for dealing with uncertain factors. Additionally, the proposed GA could potentially be improved in terms of convergence. The advantages of other intelligent algorithms could be applied to the GA in order to improve its searching ability, such as the Taboo mechanism. With increasing developments in the Information Technology (IT) outsourcing industry, many enterprises outsource IT services to reduce costs. However, the schedule risk of IT outsourcing (ITO) projects may result in enormous economic losses for an enterprise. In this paper, the principal–agent theory is used to control the schedule risk of ITO projects. A two-level mathematical model is built to describe the decision process of the client and vendors. With an increase to the number of subprojects and activities, the scale of the problem will become very large. The resulting optimization is an NP hard problem with continuous domain. Therefore, a genetic algorithm (GA) is designed to solve the proposed model. Experiments are performed to test the ability of the proposed algorithm. Some insights from simulation analysis – the principal–agent theory and two-level mathematical model – are suitable for describing the cooperative relationship between principle and agent. By comparing with ant colony optimization and simulated annealing, the proposed GA shows strong optimization abilities for convergence, reliability, and efficiency, which is a good tool for this kind of optimization problem. The near-optimal plan reduced the schedule risk of the project remarkably, which is the scientific quantitative proposal for the decision maker. This study provides practitioners insights on relationships of schedule risk and ITO projects, and the design model and algorithms of this paper provides practitioners effective potential method to reduce the schedule risk of ITO projects in their operations. However, the uncertain characteristics of key and multiple factors should be considered in future work. Stochastic Programming and the Monte Carlo Simulation Method are two potential tools for dealing with uncertain factors. Additionally, the proposed GA could potentially be improved in terms of convergence. The advantages of other intelligent algorithms could be applied to the GA in order to improve its searching ability, such as the Taboo mechanism. Principal–agent theory Elsevier Risk control Elsevier Genetic algorithm Elsevier IT outsourcing project Elsevier Schedule risk Elsevier Lu, Fuqiang oth Duan, Shupeng oth Huang, Min oth Zhu, Jinwen oth Liu, Mengying oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:91 year:2020 pages:0 https://doi.org/10.1016/j.engappai.2020.103584 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 91 2020 0 |
| allfields_unstemmed |
10.1016/j.engappai.2020.103584 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000977.pica (DE-627)ELV050004840 (ELSEVIER)S0952-1976(20)30062-2 DE-627 ger DE-627 rakwb eng 540 VZ 610 VZ 44.00 bkl Bi, Hualing verfasserin aut Two-level principal–agent model for schedule risk control of IT outsourcing project based on genetic algorithm 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With increasing developments in the Information Technology (IT) outsourcing industry, many enterprises outsource IT services to reduce costs. However, the schedule risk of IT outsourcing (ITO) projects may result in enormous economic losses for an enterprise. In this paper, the principal–agent theory is used to control the schedule risk of ITO projects. A two-level mathematical model is built to describe the decision process of the client and vendors. With an increase to the number of subprojects and activities, the scale of the problem will become very large. The resulting optimization is an NP hard problem with continuous domain. Therefore, a genetic algorithm (GA) is designed to solve the proposed model. Experiments are performed to test the ability of the proposed algorithm. Some insights from simulation analysis – the principal–agent theory and two-level mathematical model – are suitable for describing the cooperative relationship between principle and agent. By comparing with ant colony optimization and simulated annealing, the proposed GA shows strong optimization abilities for convergence, reliability, and efficiency, which is a good tool for this kind of optimization problem. The near-optimal plan reduced the schedule risk of the project remarkably, which is the scientific quantitative proposal for the decision maker. This study provides practitioners insights on relationships of schedule risk and ITO projects, and the design model and algorithms of this paper provides practitioners effective potential method to reduce the schedule risk of ITO projects in their operations. However, the uncertain characteristics of key and multiple factors should be considered in future work. Stochastic Programming and the Monte Carlo Simulation Method are two potential tools for dealing with uncertain factors. Additionally, the proposed GA could potentially be improved in terms of convergence. The advantages of other intelligent algorithms could be applied to the GA in order to improve its searching ability, such as the Taboo mechanism. With increasing developments in the Information Technology (IT) outsourcing industry, many enterprises outsource IT services to reduce costs. However, the schedule risk of IT outsourcing (ITO) projects may result in enormous economic losses for an enterprise. In this paper, the principal–agent theory is used to control the schedule risk of ITO projects. A two-level mathematical model is built to describe the decision process of the client and vendors. With an increase to the number of subprojects and activities, the scale of the problem will become very large. The resulting optimization is an NP hard problem with continuous domain. Therefore, a genetic algorithm (GA) is designed to solve the proposed model. Experiments are performed to test the ability of the proposed algorithm. Some insights from simulation analysis – the principal–agent theory and two-level mathematical model – are suitable for describing the cooperative relationship between principle and agent. By comparing with ant colony optimization and simulated annealing, the proposed GA shows strong optimization abilities for convergence, reliability, and efficiency, which is a good tool for this kind of optimization problem. The near-optimal plan reduced the schedule risk of the project remarkably, which is the scientific quantitative proposal for the decision maker. This study provides practitioners insights on relationships of schedule risk and ITO projects, and the design model and algorithms of this paper provides practitioners effective potential method to reduce the schedule risk of ITO projects in their operations. However, the uncertain characteristics of key and multiple factors should be considered in future work. Stochastic Programming and the Monte Carlo Simulation Method are two potential tools for dealing with uncertain factors. Additionally, the proposed GA could potentially be improved in terms of convergence. The advantages of other intelligent algorithms could be applied to the GA in order to improve its searching ability, such as the Taboo mechanism. Principal–agent theory Elsevier Risk control Elsevier Genetic algorithm Elsevier IT outsourcing project Elsevier Schedule risk Elsevier Lu, Fuqiang oth Duan, Shupeng oth Huang, Min oth Zhu, Jinwen oth Liu, Mengying oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:91 year:2020 pages:0 https://doi.org/10.1016/j.engappai.2020.103584 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 91 2020 0 |
| allfieldsGer |
10.1016/j.engappai.2020.103584 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000977.pica (DE-627)ELV050004840 (ELSEVIER)S0952-1976(20)30062-2 DE-627 ger DE-627 rakwb eng 540 VZ 610 VZ 44.00 bkl Bi, Hualing verfasserin aut Two-level principal–agent model for schedule risk control of IT outsourcing project based on genetic algorithm 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With increasing developments in the Information Technology (IT) outsourcing industry, many enterprises outsource IT services to reduce costs. However, the schedule risk of IT outsourcing (ITO) projects may result in enormous economic losses for an enterprise. In this paper, the principal–agent theory is used to control the schedule risk of ITO projects. A two-level mathematical model is built to describe the decision process of the client and vendors. With an increase to the number of subprojects and activities, the scale of the problem will become very large. The resulting optimization is an NP hard problem with continuous domain. Therefore, a genetic algorithm (GA) is designed to solve the proposed model. Experiments are performed to test the ability of the proposed algorithm. Some insights from simulation analysis – the principal–agent theory and two-level mathematical model – are suitable for describing the cooperative relationship between principle and agent. By comparing with ant colony optimization and simulated annealing, the proposed GA shows strong optimization abilities for convergence, reliability, and efficiency, which is a good tool for this kind of optimization problem. The near-optimal plan reduced the schedule risk of the project remarkably, which is the scientific quantitative proposal for the decision maker. This study provides practitioners insights on relationships of schedule risk and ITO projects, and the design model and algorithms of this paper provides practitioners effective potential method to reduce the schedule risk of ITO projects in their operations. However, the uncertain characteristics of key and multiple factors should be considered in future work. Stochastic Programming and the Monte Carlo Simulation Method are two potential tools for dealing with uncertain factors. Additionally, the proposed GA could potentially be improved in terms of convergence. The advantages of other intelligent algorithms could be applied to the GA in order to improve its searching ability, such as the Taboo mechanism. With increasing developments in the Information Technology (IT) outsourcing industry, many enterprises outsource IT services to reduce costs. However, the schedule risk of IT outsourcing (ITO) projects may result in enormous economic losses for an enterprise. In this paper, the principal–agent theory is used to control the schedule risk of ITO projects. A two-level mathematical model is built to describe the decision process of the client and vendors. With an increase to the number of subprojects and activities, the scale of the problem will become very large. The resulting optimization is an NP hard problem with continuous domain. Therefore, a genetic algorithm (GA) is designed to solve the proposed model. Experiments are performed to test the ability of the proposed algorithm. Some insights from simulation analysis – the principal–agent theory and two-level mathematical model – are suitable for describing the cooperative relationship between principle and agent. By comparing with ant colony optimization and simulated annealing, the proposed GA shows strong optimization abilities for convergence, reliability, and efficiency, which is a good tool for this kind of optimization problem. The near-optimal plan reduced the schedule risk of the project remarkably, which is the scientific quantitative proposal for the decision maker. This study provides practitioners insights on relationships of schedule risk and ITO projects, and the design model and algorithms of this paper provides practitioners effective potential method to reduce the schedule risk of ITO projects in their operations. However, the uncertain characteristics of key and multiple factors should be considered in future work. Stochastic Programming and the Monte Carlo Simulation Method are two potential tools for dealing with uncertain factors. Additionally, the proposed GA could potentially be improved in terms of convergence. The advantages of other intelligent algorithms could be applied to the GA in order to improve its searching ability, such as the Taboo mechanism. Principal–agent theory Elsevier Risk control Elsevier Genetic algorithm Elsevier IT outsourcing project Elsevier Schedule risk Elsevier Lu, Fuqiang oth Duan, Shupeng oth Huang, Min oth Zhu, Jinwen oth Liu, Mengying oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:91 year:2020 pages:0 https://doi.org/10.1016/j.engappai.2020.103584 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 91 2020 0 |
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10.1016/j.engappai.2020.103584 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000977.pica (DE-627)ELV050004840 (ELSEVIER)S0952-1976(20)30062-2 DE-627 ger DE-627 rakwb eng 540 VZ 610 VZ 44.00 bkl Bi, Hualing verfasserin aut Two-level principal–agent model for schedule risk control of IT outsourcing project based on genetic algorithm 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With increasing developments in the Information Technology (IT) outsourcing industry, many enterprises outsource IT services to reduce costs. However, the schedule risk of IT outsourcing (ITO) projects may result in enormous economic losses for an enterprise. In this paper, the principal–agent theory is used to control the schedule risk of ITO projects. A two-level mathematical model is built to describe the decision process of the client and vendors. With an increase to the number of subprojects and activities, the scale of the problem will become very large. The resulting optimization is an NP hard problem with continuous domain. Therefore, a genetic algorithm (GA) is designed to solve the proposed model. Experiments are performed to test the ability of the proposed algorithm. Some insights from simulation analysis – the principal–agent theory and two-level mathematical model – are suitable for describing the cooperative relationship between principle and agent. By comparing with ant colony optimization and simulated annealing, the proposed GA shows strong optimization abilities for convergence, reliability, and efficiency, which is a good tool for this kind of optimization problem. The near-optimal plan reduced the schedule risk of the project remarkably, which is the scientific quantitative proposal for the decision maker. This study provides practitioners insights on relationships of schedule risk and ITO projects, and the design model and algorithms of this paper provides practitioners effective potential method to reduce the schedule risk of ITO projects in their operations. However, the uncertain characteristics of key and multiple factors should be considered in future work. Stochastic Programming and the Monte Carlo Simulation Method are two potential tools for dealing with uncertain factors. Additionally, the proposed GA could potentially be improved in terms of convergence. The advantages of other intelligent algorithms could be applied to the GA in order to improve its searching ability, such as the Taboo mechanism. With increasing developments in the Information Technology (IT) outsourcing industry, many enterprises outsource IT services to reduce costs. However, the schedule risk of IT outsourcing (ITO) projects may result in enormous economic losses for an enterprise. In this paper, the principal–agent theory is used to control the schedule risk of ITO projects. A two-level mathematical model is built to describe the decision process of the client and vendors. With an increase to the number of subprojects and activities, the scale of the problem will become very large. The resulting optimization is an NP hard problem with continuous domain. Therefore, a genetic algorithm (GA) is designed to solve the proposed model. Experiments are performed to test the ability of the proposed algorithm. Some insights from simulation analysis – the principal–agent theory and two-level mathematical model – are suitable for describing the cooperative relationship between principle and agent. By comparing with ant colony optimization and simulated annealing, the proposed GA shows strong optimization abilities for convergence, reliability, and efficiency, which is a good tool for this kind of optimization problem. The near-optimal plan reduced the schedule risk of the project remarkably, which is the scientific quantitative proposal for the decision maker. This study provides practitioners insights on relationships of schedule risk and ITO projects, and the design model and algorithms of this paper provides practitioners effective potential method to reduce the schedule risk of ITO projects in their operations. However, the uncertain characteristics of key and multiple factors should be considered in future work. Stochastic Programming and the Monte Carlo Simulation Method are two potential tools for dealing with uncertain factors. Additionally, the proposed GA could potentially be improved in terms of convergence. The advantages of other intelligent algorithms could be applied to the GA in order to improve its searching ability, such as the Taboo mechanism. Principal–agent theory Elsevier Risk control Elsevier Genetic algorithm Elsevier IT outsourcing project Elsevier Schedule risk Elsevier Lu, Fuqiang oth Duan, Shupeng oth Huang, Min oth Zhu, Jinwen oth Liu, Mengying oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:91 year:2020 pages:0 https://doi.org/10.1016/j.engappai.2020.103584 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 91 2020 0 |
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Two-level principal–agent model for schedule risk control of IT outsourcing project based on genetic algorithm |
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With increasing developments in the Information Technology (IT) outsourcing industry, many enterprises outsource IT services to reduce costs. However, the schedule risk of IT outsourcing (ITO) projects may result in enormous economic losses for an enterprise. In this paper, the principal–agent theory is used to control the schedule risk of ITO projects. A two-level mathematical model is built to describe the decision process of the client and vendors. With an increase to the number of subprojects and activities, the scale of the problem will become very large. The resulting optimization is an NP hard problem with continuous domain. Therefore, a genetic algorithm (GA) is designed to solve the proposed model. Experiments are performed to test the ability of the proposed algorithm. Some insights from simulation analysis – the principal–agent theory and two-level mathematical model – are suitable for describing the cooperative relationship between principle and agent. By comparing with ant colony optimization and simulated annealing, the proposed GA shows strong optimization abilities for convergence, reliability, and efficiency, which is a good tool for this kind of optimization problem. The near-optimal plan reduced the schedule risk of the project remarkably, which is the scientific quantitative proposal for the decision maker. This study provides practitioners insights on relationships of schedule risk and ITO projects, and the design model and algorithms of this paper provides practitioners effective potential method to reduce the schedule risk of ITO projects in their operations. However, the uncertain characteristics of key and multiple factors should be considered in future work. Stochastic Programming and the Monte Carlo Simulation Method are two potential tools for dealing with uncertain factors. Additionally, the proposed GA could potentially be improved in terms of convergence. The advantages of other intelligent algorithms could be applied to the GA in order to improve its searching ability, such as the Taboo mechanism. |
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With increasing developments in the Information Technology (IT) outsourcing industry, many enterprises outsource IT services to reduce costs. However, the schedule risk of IT outsourcing (ITO) projects may result in enormous economic losses for an enterprise. In this paper, the principal–agent theory is used to control the schedule risk of ITO projects. A two-level mathematical model is built to describe the decision process of the client and vendors. With an increase to the number of subprojects and activities, the scale of the problem will become very large. The resulting optimization is an NP hard problem with continuous domain. Therefore, a genetic algorithm (GA) is designed to solve the proposed model. Experiments are performed to test the ability of the proposed algorithm. Some insights from simulation analysis – the principal–agent theory and two-level mathematical model – are suitable for describing the cooperative relationship between principle and agent. By comparing with ant colony optimization and simulated annealing, the proposed GA shows strong optimization abilities for convergence, reliability, and efficiency, which is a good tool for this kind of optimization problem. The near-optimal plan reduced the schedule risk of the project remarkably, which is the scientific quantitative proposal for the decision maker. This study provides practitioners insights on relationships of schedule risk and ITO projects, and the design model and algorithms of this paper provides practitioners effective potential method to reduce the schedule risk of ITO projects in their operations. However, the uncertain characteristics of key and multiple factors should be considered in future work. Stochastic Programming and the Monte Carlo Simulation Method are two potential tools for dealing with uncertain factors. Additionally, the proposed GA could potentially be improved in terms of convergence. The advantages of other intelligent algorithms could be applied to the GA in order to improve its searching ability, such as the Taboo mechanism. |
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With increasing developments in the Information Technology (IT) outsourcing industry, many enterprises outsource IT services to reduce costs. However, the schedule risk of IT outsourcing (ITO) projects may result in enormous economic losses for an enterprise. In this paper, the principal–agent theory is used to control the schedule risk of ITO projects. A two-level mathematical model is built to describe the decision process of the client and vendors. With an increase to the number of subprojects and activities, the scale of the problem will become very large. The resulting optimization is an NP hard problem with continuous domain. Therefore, a genetic algorithm (GA) is designed to solve the proposed model. Experiments are performed to test the ability of the proposed algorithm. Some insights from simulation analysis – the principal–agent theory and two-level mathematical model – are suitable for describing the cooperative relationship between principle and agent. By comparing with ant colony optimization and simulated annealing, the proposed GA shows strong optimization abilities for convergence, reliability, and efficiency, which is a good tool for this kind of optimization problem. The near-optimal plan reduced the schedule risk of the project remarkably, which is the scientific quantitative proposal for the decision maker. This study provides practitioners insights on relationships of schedule risk and ITO projects, and the design model and algorithms of this paper provides practitioners effective potential method to reduce the schedule risk of ITO projects in their operations. However, the uncertain characteristics of key and multiple factors should be considered in future work. Stochastic Programming and the Monte Carlo Simulation Method are two potential tools for dealing with uncertain factors. Additionally, the proposed GA could potentially be improved in terms of convergence. The advantages of other intelligent algorithms could be applied to the GA in order to improve its searching ability, such as the Taboo mechanism. |
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