Although this Helpfile will provide many valuable hints for making effective use of the Solver, it does not attempt to teach you how to formulate Solver models or apply linear and quadratic programming, nonlinear programming or integer programming techniques. To make the most of the Solver, we strongly recommend that you consult one of the books cited below, or discuss your problem with someone in your firm or at your local university with a background in operations research and/or management science. There is a vast literature on problems of various types and for various industries and business situations which have been solved successfully with the methods available in the Solver. Don't reinvent the wheel -- find out how others have solved problems similar to yours!
Spreadsheet Modeling and Decision Analysis by Cliff T. Ragsdale, published by Course Technology. ISBN 1-56527-277-3. This book uses the Microsoft Excel Solver, which is quite similar to the 1-2-3 97 Solver, for all of its examples. You'll find a discussion of linear, nonlinear and integer programming; an explanation of sensitivity analysis and how to use the Solver's reports; topics like goal programming and multi-objective optimization; and additional coverage of regression, time series analysis, queuing, project management, decision analysis, and other topics.
Practical Management Science: Spreadsheet Modeling and Applications by S. Christian Albright and Wayne L. Winston, published by Duxbury Press. ISBN 0-534-21774-5. This is a new book based on Winston's well-regarded textbook Operations Research: Applications and Algorithms (see below), updated to use spreadsheet Solvers throughout. It is notable for its extensive, in-depth coverage of classic optimization problems, including many in the exercises. It also includes coverage of decision making under uncertainty, inventory models, queuing, simulation and forecasting.
Managerial Spreadsheet Modeling and Analysis by Rick Hesse, published by Richard D. Irwin. ISBN 0-256-21530-8. This book teaches you how to formulate a model from a complex business situation, using a four-step process: Picture and paraphrase, verbal model, algebraic model and spreadsheet model. It covers types of models ranging from simple goalseeking and unconstrained problems to linear, nonlinear and integer programming problems.
Management Science: Modeling, Analysis and Interpretation by Jeffrey D. Camm and James R. Evans, published by South-Western College Publishing. ISBN 0-538-82738-6. This book focuses on modeling and covers both optimization and simulation. It uses the Excel Solver and the (earlier) Lotus Solver in many examples, though some examples use the older LINDO optimizer which has its own language for expressing LP models. It covers multiobjective LP models, integer models and network models, but does not cover nonlinear optimization models.
Model Building in Mathematical Programming, Third Edition by H.P. Williams, published by John Wiley. ISBN 0-471-92580-2 (HC), 0-471-94111-5 (SC). Written before the advent of spreadsheet optimizers, this book is still valuable for its explanation of model-building methods, especially if you are building larger-scale optimization models. It focuses on linear and integer programming, mentioning nonlinear models only briefly, but it offers a unique treatment of large-scale model structure and decomposition methods. It also includes a complete discussion of 20 models drawn from various industries.
Operations Research: Applications and Algorithms, Third Edition by Wayne L. Winston, published by Duxbury Press. ISBN 0-534-20971-8 (with DOS software), 0-534-20973-4 (with Mac software). This popular textbook, also written before the advent of spreadsheet optimizers, covers many classic optimization problems and also includes a discussion of some of the algorithms used in the Solver, such as the Simplex method for linear programming, the Branch & Bound method for integer programming, and selected methods for nonlinear programming -- as well as many other topics in operations research. An edition with (non-spreadsheet-based) Windows software is due in the Fall of 1996, with ISBN 0-534-52020-0.
Management Science by Andrew S. Shogan, published by Prentice-Hall, Inc. ISBN 0-13-551219-0. Another popular textbook written before the advent of spreadsheet optimizers, with in-depth coverage of the Simplex and Branch & Bound methods as well as many classic optimization problems. Also includes a detailed discussion of sensitivity analysis, goal programming, and piecewise linear programming -- as well as many other management science topics -- but does not cover nonlinear programming.
For a technical description of the nonlinear GRG solver included with the standard and enhanced Solvers, please consult the following academic papers:
L.S. Lasdon, A. Waren, A. Jain and M. Ratner. Design and Testing of a Generalized Reduced Gradient Code for Nonlinear Programming. ACM Transactions on Mathematical Software 4:1 (1978), pp. 34-50.
L.S. Lasdon and S. Smith. Solving Sparse Nonlinear Programs Using GRG. ORSA Journal on Computing 4:1 (1992), pp. 2-15.