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Introduction
1 Motivation
Substantial progress was made in the 1950s and 1960s with the development of algorithms and computer codes to solve
large mathematical programming problems. The number of applications of these tools in the 1970s was less then expected,
however, because the solution procedures formed only a small part of the overall modeling effort. A large part of the time
required to develop a model involved data preparation and transformation and report preparation. Each model required many
hours of analyst and programming time to organize the data and write the programs that would transform the data into the
form required by the mathematical programming optimizers. Furthermore, it was difficult to detect and eliminate errors
because the programs that performed the data operations were only accessible to the specialist who wrote them and not to the
analysts in charge of the project.
Equations
The power of algebraic modeling languages like GAMS is most apparent in the creation of the equations and inequalities that
comprise the model under construction. This is because whenever a group of equations or inequalities has the same algebraic
structure, all the members of the group are created simultaneously, not individually.
The use of the relational data model requires that the allocation of computer resources be automated. This means that
large and complex models can be constructed without the user having to worry about details such as array sizes and
scratch storage.
There are optional input components, such as edit checks for bad data and requests for customized reports of results. Other
optional advanced features include saving and restoring old models, and creating multiple models in a single run, but this
tutorial will discuss only the basic components.
Before treating the individual components, we give a few general remarks.
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