Class dates, topics, and homework assignments (from class notes), readings (text is Sarker; [xx] refers to notes bibliography entry):

1. 1/21: Course introduction, LP formulations.
   Homework assignment #1, due next class: problems 2.4, 2.5, and 2.18. Use BLP for answering 2.18. (See: Instructions for submitting homework assignments and BLP documentation in section 1.2 of class notes.)
   Reading assignment: Sarker & Newton, pp. 1-39.
2. 1/28 LP formulation, con’t
   Assignment #2: problems 2.7, 2.10, 2.22
3. 2/4 LP interpretation
   Assignment 3: problems 2.6, 2.21; install GAMS and OPL from the course CD onto your PC and verify that they work.
   Reading assignment: [Pid99]; Sarker: 11.2, 12.2, Appendix 9-C (GAMS overview)
4. 2/11 Parametric programming, GAMS
   Assignment 4: problems 2.23, 3.10, and 2.25 (small problem with many parts that follow the philosophy of [Pid99]).
   Reading assignment: [BKMR98] Chap.2. (GAMS tutorial)
5. 2/18 GAMS, multiobjective optimization, goal programming
   Assignment 5: problems 3.1, 3.12 (assignment part 1), complete 2.25 from assignment 4, if you need to.
   Reading assignment: 95Powell (see Blackboard)
6. 2/25 Preemptive priorities, modeling heuristics
   Assignment 6: problems 3.8, 3.12.4 (see BB for model), 3.14
   Reading assignment:
7. 3/4 Network optimization
   Reading assignment: Chapter 4 in notes
   Assignment 7: 5.3, 5.7 parts 1-3, and 5.40. [If asked to formulate as a network, draw the network diagram, labeling all nodes and showing the arc costs and any bounds. New: if you want to solve the network models, see the OPTNET documentation in the notes or the GAMS netflow.gms file on the class Blackboard.]
   3.11 Spring break - no class
8. 3/18 Dynamic, generalized, and constrained network models
   Assignment 8: Draw the network formulation pictorially in your answers to 5.10, 5.7, and 5.18. Also solve 5.34, for which GAMS data file bayDat.gams is available on Blackboard
9. 3/25 Integer programming
   Assignment 9: 5.25, 6.2, 6.14, and 5.35
   Reading assignment: TBA
10. 4/1 Optimization-based data mining
    Assignment 10: 3.13, 6.11, 6.22, work on term project.
11. 4/8 Constraint programming
    Assignment 11: 6.3, 6.23, 6.24
12. 4/15 Uncertainty modeling
    Assignment 12: 7.1, 7.5, continue on 6.24 and projects.
13. 4/22 Multi-stage stochastic programming
    Assignment 13: 7.2, 7.7. (For a tutorial on stochastic programming see pdf of article [SH99] on class CD.)
14. 4/29 Nonlinear programming
15. 5/6 (final exam period) Project presentations