How to Solve Real-world Optimization Problems: From Theory to Practice (SpringerBriefs in Operations Research)

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´´ and ``Soft´´ 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