**Topics Covered:**

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1. Introduction to SP

2. SP Models and Applications

3. Deterministic Large-Scale Decomposition Methods

4. Stochastic Linear Programming Methods

5. Computational Experimentation

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**Detailed Topical Outline:**

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**1. Introduction **

1.1 Decision-making in an uncertain world

1.1.1 Applications

1.1.2 Decision stagesand decision trees

1.1.3 Decision models

1.2 Probability spaces and random variables

1.3 Numerical example: From LP to SP

*Class-notes, Chapters1-2 in Birge and Louveaux, 1997.*

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**2. SP Models and Applications**

2.1 Recourse models and chance-constrained models

2.2 Applications

2.2.1 Facility location under uncertainty

- Facility location in an aging society
- Nursing home location

2.2.2 Transportation problems under uncertainty

- Bus/shuttle scheduling for the elderly

*Class notes; Chapter 3in Birge and Louveaux, 1997; Chapter 1 in Ruszczynski and Shapiro, 2003.*

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**3. Deterministic Large-Scale Decomposition Methods**

3.1 Basic theory - convexity, separation, convergence

3.2 Benders decomposition

3.3 Regularized Benders

3.4 Dantzig-Wolfe decomposition

* Class-notes, Chapters10-11 in Martin, 1999*

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**4. Stochastic Linear Programming Methods**

4.1 The L-shaped method

4.2 Multi-cut L-shaped method

4.3 Regularized L-shaped method

*Class-notes, Chapter 5 in Birge and Louveaux, 1997; Chapter 3 in Ruszczynski and Shapiro, 2003.*

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**5. Computational Experimentation**

5.1 Empirical testing

5.2 SMPS format, sparse matrix representation, etc.

5.3 Introduction to the CPLEX Callable Library

*Class-notes*