Intro
Organization
Preface
Contents
Part I Awards of GOR
1 A Two-Stage Stochastic Optimisation Model for Urban Same-Day Delivery with Micro-hubs
Motivation
Problem Description
The Progressive Hedging Algorithm
Computational Study
Convergence Behaviour of PH
Service Rates on Different Demand Patterns
Conclusion and Future Work
References
2 Computational Linear Bilevel Optimization
Introduction to Linear Bilevel Problems
Branch-and-Bound Methods for Linear Bilevel Problems

The Big-ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (upper M) /StPNE pdfmark [/StBMC pdfmarkto.ps: [/EMC pdfmark [/Artifact > /BDC pdfmark Mps: [/EMC pdfmark [/StBMC pdfmark ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark Approach: Not Your Everyday Friend
The SOS1 Approach: A Lame Duck?
The Game Changer: Valid Inequalities Based on Strong Duality
A Penalty Alternating Direction Method
Conclusion
References
3 Faster Algorithms for Steiner Tree and Related Problems: From Theory to Practice
Introduction

Finding Minimum Steiner Trees by Branch-and-Cut
Computational Results
From Classic Steiner Tree to Related Problems
Conclusion
References
4 Prescriptive Analytics for Data-Driven Capacity Management
Introduction
Prescriptive Analytics Approaches
Kernelized Empirical Risk Minimization
Weighted Sample Average Approximation
Numerical Evaluation Based on a Real-World Problem
Data Set and Feature Engineering
Evaluation Procedure
Results and Discussion
Conclusion
References
5 Resident Scheduling in Teaching Hospitals
Motivation
Methodology

Results
Conclusion
References
6 Solving Customer Order Scheduling Problems with an Iterated Greedy Algorithm
Introduction
General Problem Description
Minimizing the Total Completion Time in a Dedicated Machine Environment
Minimizing the Total Completion Time in a Flow Shop Environment
Minimizing the Earliness-Tardiness in a Dedicated Machine Environment
Conclusion
References
7 The Stochastic Bilevel Selection Problem
Introduction
Problem Analysis
The Selection Probability
Pseudo-polynomial Time Algorithms
Conclusion
References

Part II Analytics and Learning
8 A Combined Measure Based on Diversification and Accuracy Gains for Forecast Selection in Forecast Combination
Introduction
Forecast Combination
Measure Based on Diversification and Accuracy Gains
Experimental Design
Experimental Evaluation
Conclusion and Future Work
References
9 A Decision Support System Including Feedback to Sensitize for Certainty Interval Size
Introduction
Experimental Design
Assumptions Studied
Results
Discussion
Conclusion and Outlook
References
Part III Continuous and Global Optimization