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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
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
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
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
8.8 Main Properties of Optimal Schedules8.8.1 Convex Functions fi ≤ ci·ui, ci = fi(1); 8.8.2 Concave Functions fi and n ≤ 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
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
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
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
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
8.8 Main Properties of Optimal Schedules8.8.1 Convex Functions fi ≤ ci·ui, ci = fi(1); 8.8.2 Concave Functions fi and n ≤ 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