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Book 图书名称：Dynamic and Stochastic Multi-Project Planning
Author 作者：Philipp Melchiors
Publisher 出版社：Springer
Page 页数：216
Publishing Date 出版时间：Apr 28, 2015
Language 语言：English
Size 大小：3 MB
Format 格式：pdf 文字版
ISBN:3319045393, 9783319045399, 9783319045405
Edition: 第1版 搜索过论坛，没有该文档
Lecture Notes in Economics and Mathematical Systems 673
Introduces new analytical models for optimal multi-project management based on decision rules in dynamic-stochastic environments
Presents new insights into the structure of optimal policies
Describes extensive experimental investigations into the performance of well-known heuristics for multi-project scheduling in dynamic-stochastic environments
Introduces new approaches for high quality computing policies which outperform existing heuristic policies
This book deals with dynamic and stochastic methods for multi-project planning. Based on the idea of using queueing networks for the analysis of dynamic-stochastic multi-project environments this book addresses two problems: detailed scheduling of project activities, and integrated order acceptance and capacity planning. In an extensive simulation study, the book thoroughly investigates existing scheduling policies. To obtain optimal and near optimal scheduling policies new models and algorithms are proposed based on the theory of Markov decision processes and Approximate Dynamic programming. Then the book presents a new model for the effective computation of optimal policies based on a Markov decision process. Finally, the book provides insights into the structure of optimal policies.
Related Subjects: Project Management, Operation Research/Decision Theory, Operations Research, Management Science, Innovation/Technology Management, Production/Logistics/Supply Chain Management, Discrete Optimization
== Table of contents ==
Table of contents :
List of Figures
List of Tables
1 Introduction
1.1 Background
1.2 Research Focus
1.2.1 Order Acceptance and Capacity Planning
1.2.2 Resource-Constrained Multi-project Scheduling
1.3 Outline
2 Problem Statements
2.1 General Assumptions and Notation
2.1.1 Projects
2.1.2 Resources
2.1.3 Project Types
2.1.4 Objective Functions
2.2 Dynamic-Stochastic Multi-project Scheduling Problem
2.2.1 Non-preemptive Scheduling Problem
2.2.2 Preemptive Scheduling Problem
2.3 Order Acceptance and Capacity Planning Problem
2.3.1 Multi-project Environment
2.3.2 Order Acceptance Decisions
2.3.3 Resource Allocation Decisions
3 Literature Review
3.1 Dynamic Programming and Approximate DynamicProgramming
3.2 Project Scheduling
3.2.1 Static–Deterministic Project Scheduling
3.2.2 Dynamic–Deterministic Project Scheduling
3.2.3 Static–Stochastic Project Scheduling
3.2.4 Dynamic–Stochastic Project Scheduling
3.3 Capacity Planning
3.4 Order Acceptance
4 Continuous-Time Markov Decision Processes
4.1 General Structure
4.2 Basic Definitions and Relevant Properties
4.3 Objective Function
4.4 Evaluation and Optimality Equations
4.5 Uniformization
4.6 General Solution Methodologies
4.6.1 Value Iteration
4.6.2 Policy Iteration
4.7 Implementation
4.7.1 Generation of the State Space
4.7.1.1 Data Structures
4.7.1.2 Generation Procedure
4.7.2 Solution Methodologies
5 Generation of Problem Instances
5.1 Generation of Project Networks
5.2 Generation Procedure
5.2.1 Step 1: Assignment of Activity Types to Resource Types
5.2.2 Step 2: Determination of Expected Durations of the Activity Types
5.2.3 Step 3: Variation Check of the Expected ActivityDurations
5.2.4 Step 4: Adjustments to Resource Type SpecificUtilizations
5.2.5 Step 5: Check of Project Type Workloads
5.2.6 Step 6: Storage of Additional Parameters
6 Scheduling Using Priority Policies
6.1 Priority Policies
6.1.1 Computation of Rule Parameters
6.1.2 Priority Rules
6.1.2.1 Bottleneck Dynamics Rules
6.1.2.2 Further Rules
6.2 Experimental Design
6.2.1 Preliminaries
6.2.2 Generation of Problem Instances
6.2.2.1 System Parameters
6.2.2.2 Project Type Parameters
6.2.3 Simulation Set Up
6.3 Main Effects of Problem Parameters
6.3.1 Due Date Tightness
6.3.2 Number of Resources
6.3.3 Order Strength
6.3.4 Variation of Expected Activity Durations
6.3.5 Utilization per Resource
6.3.6 Observations for Problem Instances with a Single Project Type
6.4 Detailed Analysis
6.4.1 Performance for Special Cases
6.4.1.1 Single Resource
6.4.1.2 Parallel Networks
6.4.2 Performance for the Remaining Problem Instances
6.4.2.1 High Due Date Tightness
6.4.2.2 Low Due Date Tightness
7 Optimal and Near Optimal Scheduling Policies
7.1 Models as a Markov Decision Process
7.1.1 Non-preemptive Scheduling Problem
7.1.1.1 Markov Decision Process
7.1.1.2 Evaluation and Optimality Equations
7.1.1.3 Elimination of Scheduling Decisions
7.1.2 Preemptive Scheduling Problem
7.1.2.1 Extension of the C*****P for the Non-preemptive Problem
7.1.2.2 Simplified C*****P
7.1.2.3 Efficient Procedure for Determining Optimal Decisions
7.1.2.4 State Space Cardinality
7.1.3 Numerical Example
7.1.3.1 General Observations
7.1.3.2 Optimal Policy for the Preemptive Scheduling Problem
7.1.3.3 Optimal Policy for the Non-preemptive Scheduling Problem
7.1.3.4 Effect of Rejection Cost
7.2 Optimal Policy for the Single Resource Case Without Preemptions
7.3 Project State Ordering Policies
7.3.1 Preemptive Project State Ordering Policies
7.3.1.1 Definitions and General Structural Results
7.3.1.2 Project State Ordering Policies and Queueing Networks Representation of the System
7.3.2 Non-preemptive Project State Ordering Policies
7.3.3 Project State Ordering Priority Policies
7.3.4 Numerical Example
7.4 Scheduling Using Approximate Dynamic Programming
7.4.1 Basic Idea
7.4.2 Approximation Based on the Preemptive Problem
7.4.3 Approximation Using Linear Function Approximation
7.4.3.1 Selection of Basis Functions
7.4.3.2 Semi-open System as an Approximation for the Open System
7.4.3.3 Bellman Error Minimization
7.4.3.4 Determining Sets of Representative States
7.4.4 Approximation for the Non-preemptive Problem Based on Linear Function Approximation for the Preemptive Problem
7.5 Computational Study
7.5.1 Experimental Design
7.5.2 Priority Policies
7.5.3 Simulation Setup
7.5.4 Results for the Preemptive Problem
7.5.4.1 State Space Cardinalities
7.5.4.2 Performance of Optimal Policies
7.5.4.3 Performance of Project State Ordering Priority Policies
7.5.5 Results for the Non-preemptive Problem
7.5.5.1 State Space Cardinalities
7.5.5.2 Performance of Optimal Policies
7.5.5.3 Performance of Project State Ordering Priority Policies
7.5.5.4 Performance of the Value Function Approximation from the Preemptive Problem
7.5.6 Performance of Linear Function Approximation
7.5.6.1 Test of the Approximation Architectures and First Computational Insights
7.5.6.2 Application of Linear Function Approximation to Selected Problem Instances
8 Integrated Dynamic Order Acceptance and Capacity Planning
8.1 Stochastic Dynamic Programming
8.1.1 State Variables
8.1.2 Decision Variables
8.1.2.1 Order Acceptance Decisions
8.1.2.2 Capacity Planning Decisions
8.1.3 Exogenous Information Process
8.1.4 Transition Function
8.1.5 Objective Function
8.2 Solution Methodology
8.3 Computational Investigation
8.3.1 Structure of Optimal Policies
8.3.1.1 Order Acceptance
8.3.1.2 Scheduling
8.3.1.3 Usage of Non-regular Capacity
8.3.2 Benefit of Crashing and Flexible MPP
8.3.2.1 Benefit of Crashing
8.3.2.2 Benefit of MPP
8.3.2.3 Combined Benefit of MPP and Crashing
8.3.2.4 General Insights
9 Conclusions and Future Work
A Abbreviations
B Symbols
B.1 General
B.1.1 System
B.1.2 Markov Decision Processes
B.1.3 Projects and Project Types
B.1.4 Resources and Resource Types
B.2 Generation of Problem Instances
B.2.1 Problem Parameters
B.2.2 Generation Procedure
B.3 Scheduling
B.3.1 General
B.3.2 Scheduling Using Priority Policies
B.3.3 Markov Decision Process for the Non-preemptiveProblem
B.3.4 Markov Decision Process for the Preemptive Problem
B.3.5 Optimal Policy for the Non-preemptive Problem with a Single Resource
B.3.6 Preemptive Project State Ordering Policies
B.3.7 Non-preemptive Project State Ordering Policies
B.3.8 Approximate Dynamic Programming
B.4 Order Acceptance and Capacity Planning
Bibliography
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