Kooperativer Bibliotheksverbund

UID:

Umfang: VIII, 214 p. 52 illus., 24 illus. in color. , online resource.

Ausgabe: 1st ed. 2022.

ISBN: 9783030883157

Serie: Adaptation, Learning, and Optimization, 26

Inhalt: This book consists of eight chapters, authored by distinguished researchers and practitioners, that highlight the state of the art and recent trends in addressing the project portfolio selection and scheduling problem (PPSSP) across a variety of domains, particularly defense, social programs, supply chains, and finance. Many organizations face the challenge of selecting and scheduling a subset of available projects subject to various resource and operational constraints. In the simplest scenario, the primary objective for an organization is to maximize the value added through funding and implementing a portfolio of projects, subject to the available budget. However, there are other major difficulties that are often associated with this problem such as qualitative project benefits, multiple conflicting objectives, complex project interdependencies, workforce and manufacturing constraints, and deep uncertainty regarding project costs, benefits, and completion times. It is well known that the PPSSP is an NP-hard problem and, thus, there is no known polynomial-time algorithm for this problem. Despite the complexity associated with solving the PPSSP, many traditional approaches to this problem make use of exact solvers. While exact solvers provide definitive optimal solutions, they quickly become prohibitively expensive in terms of computation time when the problem size is increased. In contrast, evolutionary and memetic computing afford the capability for autonomous heuristic approaches and expert knowledge to be combined and thereby provide an efficient means for high-quality approximation solutions to be attained. As such, these approaches can provide near real-time decision support information for portfolio design that can be used to augment and improve existing human-centric strategic decision-making processes. This edited book provides the reader with a broad overview of the PPSSP, its associated challenges, and approaches to addressing the problem using evolutionary and memetic computing.

Anmerkung: Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling: An Introduction -- Evolutionary Approaches for Project Portfolio Optimization: An Overview -- An Introduction to Evolutionary and Memetic Algorithms for Parameter Optimization -- An Overall Characterization of the Project Portfolio Optimization Problem and an Approach Based on Evolutionary Algorithms to Address It -- A New Model for the Project Portfolio Selection and Scheduling Problem with Defence Capability Options -- Analysis of New Approaches used in Portfolio Optimization: A Systematic Literature Review -- A Temporal Knapsack Approach to Defence Portfolio Selection -- A Decision Support System for Planning Portfolios of Supply Chain Improvement Projects in the Semiconductor Industry.

In: Springer Nature eBook

Weitere Ausg.: Printed edition: ISBN 9783030883140

Weitere Ausg.: Printed edition: ISBN 9783030883164

Weitere Ausg.: Printed edition: ISBN 9783030883171

Sprache: Englisch

DOI: 10.1007/978-3-030-88315-7

URL: https://doi.org/10.1007/978-3-030-88315-7

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Umfang: 1 online resource (218 pages) : , VIII, 214 p. 52 illus., 24 illus. in color.

ISBN: 3-030-88315-9

Serie: Adaptation, Learning and Optimization ; Volume 26

Anmerkung: 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.

Weitere Ausg.: Print version: Harrison, Kyle Robert Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling Cham : Springer International Publishing AG,c2022 ISBN 9783030883140

Sprache: Englisch

UID:

Umfang: 1 online resource (218 pages) : , VIII, 214 p. 52 illus., 24 illus. in color.

ISBN: 3-030-88315-9

Serie: Adaptation, Learning and Optimization ; Volume 26

Anmerkung: 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.

Weitere Ausg.: Print version: Harrison, Kyle Robert Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling Cham : Springer International Publishing AG,c2022 ISBN 9783030883140

Sprache: Englisch