| Description |
1 online resource |
| Series |
Studies in systems, decision and control, 2198-4182 ; v. 108 |
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Studies in systems, decision and control ; v. 108.
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| Contents |
Foreword; Acknowledgements; Contents; Acronyms; List of Figures; Part I Agent-Based Approach to the Single and Multi-mode Resource-Constrained Project Scheduling; 1 Introduction; References; 2 Agent-Based Optimization; 2.1 Basics of the Agent-Based Approaches; 2.2 Agents-Based Approaches to Optimization; 2.2.1 A-Team Concept; 2.2.2 A-Team Implementation -- JABAT; 2.3 Agents-Based Approaches to Project Scheduling; References; 3 Project Scheduling Models; 3.1 Historical Review; 3.2 Basic Models and Classifications Review; 3.3 Generalizations and Special Cases of the RCPSP |
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3.4 Objective FunctionsReferences; 4 Resource-Constrained Project Scheduling; 4.1 Problem Formulation; 4.2 State of the Art Review; 4.3 Agent-Based Approaches to Solving RCPSP; 4.4 A-Teams Solving the RCPSP; 4.4.1 Single A-Teams with the Static Cooperation Strategies; 4.4.2 Algorithms Used in the Further A-Team Approaches; 4.4.3 Randomized Team of A-Teams with Static Cooperation Strategy; 4.4.4 A-Team with the Dynamic Cooperation Strategy with Reinforcement Learning; 4.4.5 A-Team with the Dynamic Strategy Based on Population Learning |
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4.4.6 A-Team with Dynamic Cooperation Strategy Based on Integration4.4.7 Concluding Remarks; References; 5 Multi-mode Resource-Constrained Project Scheduling; 5.1 Problem Formulation; 5.2 State of the Art Review; 5.3 Agent-Based Approaches to MRCPSP; 5.4 A-Teams Solving the MRCPSP; 5.4.1 Single A-Teams with the Static Cooperation Strategies; 5.4.2 Algorithms Used in the Further A-Team Approaches; 5.4.3 A-Team with Dynamic Cooperation Strategy with Reinforcement Learning; 5.4.4 A-Team with Dynamic Cooperation Strategy Based on Population Learning |
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5.4.5 A-Team with Dynamic Cooperation Strategy Based on Integration5.4.6 Concluding Remarks; References; 6 Conclusions; Part II Population-Based Approaches to the Discrete-Continuous Scheduling; 7 Introduction; 8 Discrete-Continuous Scheduling Problem; 8.1 General Resource-Constrained Scheduling Problem; 8.2 Practical Applications of the DCSP; 8.3 Notation; 8.4 Task Models; 8.4.1 Processing Time Versus Resource-Amount Model; 8.4.2 Processing Rate Versus Resource-Amount Model; 8.5 Problem Formulation; 8.6 Variants of the DCSP; 8.7 General Approach to Solving the DCSP |
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8.8 Main Properties of Optimal Schedules8.8.1 Convex Functions fi d"ci·ui, ci = fi(1); 8.8.2 Concave Functions fi and n d"m; 8.8.3 Concave Functions fi and n greaterthan m; 8.8.3.1 Identical Concave Functions; 8.8.3.2 Concave Power Functions; 8.9 Minimization of the Maximum Lateness Lmax; 8.10 Minimization of Mean Flow Time \overline{F} ; References; 9 State-of-the-Art Review; 9.1 Theoretical Research on the DCSP; 9.1.1 Another Formulation of the DCSP; 9.1.2 The New Approach to Optimal Resource Allocation; 9.1.3 New Properties of the Discrete Part of the DCSP; 9.2 Discretisation of the DCSP |
| Summary |
This book addresses two of the most difficult and computationally intractable classes of problems: discrete resource constrained scheduling, and discrete-continuous scheduling. The first part of the book discusses problems belonging to the first class, while the second part deals with problems belonging to the second class. Both parts together offer valuable insights into the possibility of implementing modern techniques and tools with a view to obtaining high-quality solutions to practical and, at the same time, computationally difficult problems. It offers a valuable source of information for practitioners dealing with the real-world scheduling problems in industry, management and administration. The authors have been working on the respective problems for the last decade, gaining scientific recognition through publications and active participation in the international scientific conferences, and their results are obtained using population-based methods. Dr E. Ratajczk-Ropel explores multiple agent and A-Team concepts, while Dr A. Skakovski focuses on evolutionary algorithms with a particular focus on the population learning paradigm |
| Bibliography |
Includes bibliographical references |
| Notes |
Print version record |
| Subject |
Scheduling -- Data processing
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Mathematical optimization.
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BUSINESS & ECONOMICS -- Industrial Management.
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BUSINESS & ECONOMICS -- Management.
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BUSINESS & ECONOMICS -- Management Science.
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BUSINESS & ECONOMICS -- Organizational Behavior.
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Mathematical optimization
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Scheduling -- Data processing
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| Form |
Electronic book
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| Author |
Skakovski, Aleksander
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| ISBN |
9783319628936 |
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3319628933 |
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