Preface
Introduction
Reference
Acknowledgments
Disclaimer
Contents
Abbreviations
Chapter 1: Practical Tips
1.1 Master the Subject Area: Embrace Functional Requirements-Think Outside the Box
1.2 Think Beyond Rigorous Optimization: Develop Heuristic Algorithms
1.3 Craft Intelligent Models for Real-World Problems
1.4 Navigate Infeasibility: Blend Hard´´ andSoft´´ Constraint Formulations
1.5 Practice Multi-Criteria Models: Leverage Lexicographic Optimization
1.6 Expose Multiple Optimal and Near-Optimal Solutions
1.7 Decompose a Problem into Manageable Interconnected Modules
1.8 Unveil Hidden Symmetry: Explore New Problem Counterparts
1.9 Delve into Multi-Model Problem Resolution
1.10 Deliberate Choice Deterministic vs. Stochastic Formulation
1.11 Avoid Direct Connection of the Optimization Model to a Database
1.12 Implement Your Model with Maximum Flexibility
1.13 Provide Consistency in the Results
Chapter 2: Real-World Problems
2.1 Cutting Stock Problem
2.1.1 Classical Bin-Packing Model
Sets
Input Data
Variables
Model
2.1.2 Pattern-Based Model
Number of Sets Minimization
Trim Loss Minimization Model
Sequential Heuristic Procedure
Metrics
2.1.3 Conclusions
2.2 Slitter Moves Minimization Problem
2.2.1 Heuristic Algorithm
2.2.2 Conclusions
2.3 Skiving Stock Problem
2.3.1 Covering Model
2.3.2 Pattern-Based Model
2.3.3 Conclusions
2.4 Two-Stage Cutting Stock Problem
2.4.1 Mathematical Model
2.4.2 Row-and-Column Generation
2.4.3 A Modified Column Selection in the Revised Simplex Algorithm
2.4.4 Heuristic Algorithm
2.4.5 Conclusions
2.5 Warehouse Storage Space Problem
2.5.1 Continuous Warehouse Storage Space Model
Mathematical Formulation
Time Discretization
Time and Phase Shifts Discretization
Model Discussion
Planning Horizon Reduction
Periods Reduction
Lower Bound Estimate
Model-Specific Cuts
Model Implementation
2.5.2 Discrete Warehouse Storage Space Model
Input Data
Variables
Preprocessing
Constraints
Model Discussion
Model Implementation
Model Testing
2.5.3 Heuristic Algorithm
2.5.4 Conclusions
2.6 Unit Commitment Problem
2.6.1 Mathematical Model
Sets and Parameters
Variables
Objective Function
Constraints
2.6.2 Model-Specific Cuts
Demand-Related Cover Cuts
Ramping-Related Cover Cuts
2.6.3 Avoiding Infeasibility
Mathematical Model
Input Data
Variables
Constraints
Objective Function
2.6.4 Conclusions
Concluding Remarks
Appendices
Appendix A: Bin-Packing Model Solution
Appendix B: The Theorem Proofs
Proof of Theorem 2.1
Proof of Theorem 2.2
Proof of Theorem 2.3
Proof of Theorem 2.4
Proof of Theorem 2.5
Proof of Theorem 2.6
Appendix C: Discrete Warehouse Storage Space Model in Python 3
References