ISE-OPTIMIZATION MODELING WITHSPREADSHEETS

Ken Baker is a faculty member at Dartmouth College. He is currently Nathaniel Leverone Professor of Management at the Tuck School of Business and also Adjunct Professor at the Thayer School of Engineering. He teaches courses relating to mathematical modeling and operations management. At present, he is a principal investigator on the Spreadsheet Engineering research project, and co-author (with Steve Powell) of The Art of Modeling with Spreadsheets. Over the years, much of Professor Baker's teaching and research has dealt with operations planning and control, and he is widely known for his textbook, Elements of Sequencing and Scheduling, in addition to a variety of technical articles. Professor Baker has served as Tuck School's Associate Dean and Associate Dean of Faculty, and he has directed the Tuck School's management development programs in the manufacturing area. Currently, he serves as a Co-director of Thayer School's Master's program in Engineering Management.

• 1. Introduction to Spreadsheet Modeling for Optimization. Elements of a Model. Spreadsheet Models. A Hierarchy for Analysis. Optimization Software. Using Solver. Summary. Homework.2. Linear Programming Formulations: Allocation, Covering, and Blending Models.Linear Models. Allocation Models. Blending Models. Modeling Errors in Linear Programming. Summary. Homework.3. Linear Programming Formulations: Network Models. The Transportation Model. The Assignment Model. The Transshipment Model. Features of Special Network Models. Building Network Models with Balance Equations. General Network Models with Expanding Flows. General Network Models with Transformed Flows. Summary. Homework. 4. Sensitivity Analysis Linear Programs.Sensitivity Analysis Transportation Example. Sensitivity Analysis in the Allocation Example. The Sensitivity Report and the Transportation Example. The Sensitivity Report and the Allocation Example. Degeneracy and Alternative Optima. Patterns in Linear Programming Solutions.Summary. Homework.5. Linear Programming Formulations: Data Envelopment Analysis.A Graphical Perspective on DEA. An Algebraic Perspective on DEA. A Spreadsheet Model for DEA. Indexing. Finding Reference Sets and HCUs. Assumptions and Limitations of DEA. Summary. Homework.6. Integer Programming.Using Solver with Integer Requirements. Models with Binary Choice. Models with Qualitative Constraints. The Facility Location Model. The Algorithm for Solving Integer Programs. Summary. Homework.7. Nonlinear Programming.One-Variable Models. Local Optima and Search for an Optimum. Two-Variable Models. Nonlinear Models with Constraints. Linearizations. Summary. Homework. 8. Heuristic Solutions with the Evolutionary Solver.Features of the Evolutionary Solver. An Illustrative Example: Nonlinear Regression. The Machine-Sequence Problem Revisited. The Traveling Salesperson Problem Revisited. Multi-Machine Scheduling. Two-Dimensional Location. Group Assignment. Summary. Homework.Appendix I. Software.Appendix II. Graphical Methods in Linear Programming.Appendix III. The Simplex Method.Appendix IV. Stochastic Programming.

1. Introduction to Spreadsheet Modeling for Optimization. Elements of a Model. Spreadsheet Models. A Hierarchy for Analysis. Optimization Software. Using Solver. Summary. Homework. 2. Linear Programming Formulations: Allocation, Covering, and Blending Models. Linear Models. Allocation Models. Blending Models. Modeling Errors in Linear Programming. Summary. Homework. 3. Linear Programming Formulations: Network Models. The Transportation Model. The Assignment Model. The Transshipment Model. Features of Special Network Models. Building Network Models with Balance Equations. General Network Models with Expanding Flows. General Network Models with Transformed Flows. Summary. Homework. 4. Sensitivity Analysis Linear Programs. Sensitivity Analysis Transportation Example. Sensitivity Analysis in the Allocation Example. The Sensitivity Report and the Transportation Example. The Sensitivity Report and the Allocation Example. Degeneracy and Alternative Optima. Patterns in Linear Programming Solutions. Summary. Homework. 5. Linear Programming Formulations: Data Envelopment Analysis. A Graphical Perspective on DEA. An Algebraic Perspective on DEA. A Spreadsheet Model for DEA. Indexing. Finding Reference Sets and HCUs. Assumptions and Limitations of DEA. Summary. Homework. 6. Integer Programming. Using Solver with Integer Requirements. Models with Binary Choice. Models with Qualitative Constraints. The Facility Location Model. The Algorithm for Solving Integer Programs. Summary. Homework. 7. Nonlinear Programming. One-Variable Models. Local Optima and Search for an Optimum. Two-Variable Models. Nonlinear Models with Constraints. Linearizations. Summary. Homework. 8. Heuristic Solutions with the Evolutionary Solver. Features of the Evolutionary Solver. An Illustrative Example: Nonlinear Regression. The Machine-Sequence Problem Revisited. The Traveling Salesperson Problem Revisited. Multi-Machine Scheduling. Two-Dimensional Location. Group Assignment. Summary. Homework. Appendix I. Software. Appendix II. Graphical Methods in Linear Programming. Appendix III. The Simplex Method. Appendix IV. Stochastic Programming.