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Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling
| Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling |
| Autore | Harrison Kyle Robert |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2022 |
| Descrizione fisica | 1 online resource (218 pages) |
| Altri autori (Persone) |
ElsayedSaber
GaranovichIvan Leonidovich WeirTerence BoswellSharon G SarkerRuhul Amin |
| Collana | Adaptation, Learning, and Optimization Ser. |
| Soggetto genere / forma | Electronic books. |
| ISBN |
9783030883157
9783030883140 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling: An Introduction -- 1 Introduction -- 2 Problem Formulation -- 3 Solution Methodologies -- 3.1 Mathematical Optimization -- 3.2 Evolutionary Computation -- 3.3 Memetic Computing -- 4 Summary of Chapters -- 5 Guide for Readers -- References -- Evolutionary Approaches for Project Portfolio Optimization: An Overview -- 1 Introduction -- 2 Problem Description -- 2.1 Public and Social Projects -- 2.2 Software/IT Projects -- 2.3 R& -- D and Production Projects -- 2.4 Construction and Infrastructure Projects -- 2.5 Investment Projects -- 2.6 Defense Projects -- 2.7 Summary of Problem Descriptions -- 3 Problem Formulation -- 3.1 Basic Problem Formulation -- 3.2 Public and Social Projects -- 3.3 Software/IT Projects -- 3.4 R& -- D and Production Projects -- 3.5 Construction and Infrastructure Projects -- 3.6 Investment Projects -- 3.7 Defense Projects -- 3.8 Summary of Formulations -- 4 Solution Approaches -- 4.1 Public and Social Projects -- 4.2 Software/IT Projects -- 4.3 R& -- D and Production Projects -- 4.4 Construction and Infrastructure Projects -- 4.5 Investment Projects -- 4.6 Defense Projects -- 4.7 Summary of Solution Approaches -- 5 Summary -- References -- An Introduction to Evolutionary and Memetic Algorithms for Parameter Optimization -- 1 Introduction -- 2 Comparison Between EAs and Classical Optimization Methods -- 2.1 Robustness -- 2.2 Efficiency -- 3 Building Blocks of EAs -- 4 Genetic Algorithm -- 4.1 Initialization -- 4.2 Selection -- 4.3 Crossover -- 4.4 Mutation -- 4.5 Population Update -- 4.6 Stopping Criteria -- 5 Evolution Strategies -- 5.1 Selection -- 5.2 Recombination -- 5.3 Mutation -- 5.4 Adjusting the Mutation Profile -- 6 Evolutionary Programming -- 7 Differential Evolution -- 7.1 Mutation.
7.2 Crossover -- 7.3 Selection -- 7.4 Recent Variants -- 8 Other Relevant Methods -- 9 Memetic Algorithms -- 10 Summary and Conclusions -- References -- An Overall Characterization of the Project Portfolio Optimization Problem and an Approach Based on Evolutionary Algorithms to Address It -- 1 Introduction -- 2 A Review of the Project Portfolio Selection Process -- 2.1 Phases in the Project Portfolio Selection Process -- 2.2 Characterizing a Plausible Project Portfolio Selection Approach -- 3 Problem Statement -- 3.1 Problem Description -- 3.2 An Illustrative Example -- 3.3 Problem Formalization -- 4 An Overall Approach to Project Portfolio Selection -- 4.1 Framework of the Approach -- 4.2 Coping with Imperfect Information on the Criteria Impacts -- 4.3 Representing Preferences -- 4.4 Using Evolutionary Algorithms to Optimize Portfolios -- 5 Conclusions and Future Work -- References -- A New Model for the Project Portfolio Selection and Scheduling Problem with Defence Capability Options -- 1 Introduction -- 2 Background -- 2.1 The Knapsack Problem -- 2.2 Evolutionary Meta-Heuristic Approaches -- 2.3 Differential Evolution -- 3 Problem Formulation -- 3.1 Analysis of Problem Formulation -- 3.2 NP-Hardness -- 3.3 Sample Problem Data -- 3.4 Similarity to Existing Problems -- 4 Heuristic Solution Approach -- 5 Experimental Design -- 5.1 Synthetic Problem Instance Generation -- 5.2 Problem Instances -- 5.3 Algorithmic Control Parameters -- 5.4 Statistical Analysis -- 6 Results -- 6.1 Validating the Solution Approaches -- 6.2 Effect of Seeding -- 6.3 Main Results -- 6.4 Summary -- 7 Conclusions and Future Work -- References -- Analysis of New Approaches Used in Portfolio Optimization: A Systematic Literature Review -- 1 Introduction -- 2 Research Method -- 2.1 Research Questions -- 2.2 Search Sources -- 2.3 Inclusion Criteria and Exclusion Criteria. 2.4 Data Extraction -- 2.5 Data Analysis -- 2.6 Deviations in the Protocol -- 3 Results -- 3.1 Journal Impact Factor -- 3.2 Classification of Methods -- 4 Discussion -- 4.1 Which Key Methods, Tools, or Optimization Techniques Are Used in the Portfolio Optimization Problem? -- 4.2 Which Realistic Constraints Are Used? -- 4.3 What Type of Analysis Is Done Regarding the Stock: Fundamental, Technical, or Mixed (Fundamental and Technical)? -- 4.4 Which Software/Programming Languages Are Used? -- 4.5 Recent Researches -- 5 Conclusions -- 6 Research Gaps -- References -- A Temporal Knapsack Approach to Defence Portfolio Selection -- 1 Introduction -- 2 Project and Portfolio Selection in DoD -- 3 Problem Formulation -- 3.1 Inherent Solution Challenges -- 4 Implementation in Microsoft Excel® -- 5 Performance and Budget-Value Trade-Offs -- 5.1 Relaxation -- 5.2 Value-Slack Trade-Offs and the Issue of Sensitivity -- 6 Discussion and Future Work -- References -- A Decision Support System for Planning Portfolios of Supply Chain Improvement Projects in the Semiconductor Industry -- 1 Introduction -- 2 Literature -- 3 Decision Making Framework and Integer Programming Model -- 4 Decision Support System -- 5 Case Study -- 6 Conclusions and Future Research -- References -- Index. |
| Record Nr. | UNINA-9910508437403321 |
Harrison Kyle Robert
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| Cham : , : Springer International Publishing AG, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Intelligent and Evolutionary Systems : The 20th Asia Pacific Symposium, IES 2016, Canberra, Australia, November 2016, Proceedings / / edited by George Leu, Hemant Kumar Singh, Saber Elsayed
| Intelligent and Evolutionary Systems : The 20th Asia Pacific Symposium, IES 2016, Canberra, Australia, November 2016, Proceedings / / edited by George Leu, Hemant Kumar Singh, Saber Elsayed |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XIV, 510 p.) |
| Disciplina | 005.8 |
| Collana | Proceedings in Adaptation, Learning and Optimization |
| Soggetto topico |
Computational intelligence
Artificial intelligence Computational Intelligence Artificial Intelligence |
| ISBN | 3-319-49049-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | From the Content,- An Evolutionary Optimization approach for Path Planning of Arrival Aircraft for Optimal Sequencing -- A Game-Theoretic Approach to the Analysis of Traffic Assignment -- Simulation of Darwin’s and Lamarck’s Theories of Evolution Using. |
| Record Nr. | UNINA-9910150402403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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