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Note that this simple example reveals some modeling practices that we regard as good habits in general and that are consistent
with the design of GAMS. First, all the entities of the model are identified (and grouped) by type. Second, the ordering
of entities is chosen so that no symbol is referred to before it is defined. Third, the units of all entities are specified, and,
fourth, the units are chosen to a scale such that the numerical values to be encountered by the optimizer have relatively small
absolute orders of magnitude. (The symbol $K here means thousands of dollars.)
The names of the types of entities may differ among modelers. For example, economists use the terms exogenous variable
and endogenous variable for given data and decision variable, respectively. In GAMS, the terminology adopted is as follows:
indices are called sets, given data are called parameters, decision variables are called variables, and constraints and the
objective function are called equations.
The GAMS representation of the transportation problem closely resembles the algebraic representation above. The most
important difference, however, is that the GAMS version can be read and processed by the computer.
Table 1: Data for the transportation problem (adapted from Dantzig, 1963) illustrates Shipping Distances from Plants to
Markets (1000 miles) as well as Market Demands and Plant Supplies.
Model Library
When architects begin to design a new building, they develop the new structure by using ideas and techniques that have been
tested in previous structures. The same is true in other fields: design elements from previous projects serve as sources of
ideas for new developments.
From the early stages of the development of GAMS we have collected models to be used in a library of examples. Many of
these are standard textbook examples and can be used in classes on problem formulation or to illustrate points about GAMS.
Others are models that have been used in policy or sector analysis and are interesting for both the methods and the data they
use. All the substantive models in the library are described in the open literature. A collection of models is now included
with all GAMS systems, along with a database to help users locate examples that cover countries, sectors, or topics of interest
to them.
The syntax used to introduce features in the various chapters are presented using the Backus-Naur form (BNF) notation
where:
Advanced Topics - This part discusses advanced topics and can be studied as needed. Users with large, complex, or
expensive models will find much useful material in this part.
立的模型和用户界面
面向对象的GAMA API允许GAMS无缝整合到为交互提供适当类别的应用中。这三个面向对象GAMS API是.NET, Java
和Python与.NET framework 4 (Visual Studio 2010)、Java SE 5或更高版本以及Python 3.4, 2.7和2.6。
除了面向对象的GAMA API,还有级别(或级别) 的GAMS API,它们的使用要求有高深知识的GAMS组件库。
除了API, GAMS还提供智能链接到应用程序,如MS Excel, MatLab或R。用户可以在这个环境中继续工作,通过一个
API就可以访问GAMS所有的优化功能。这就允许应用中的模型数据和结果可以可视化和分析了。
超过120多个国家的不同领域的跨国公司、学校、研究机构和使用GAMS,包括能源化工、经济建模、农业规
划或制造业。
Independence of Model and User Interface
The GAMS object-oriented APIs allow the seamless integration of GAMS into an application by providing appropriate classes for the interaction with GAMS. The three versions of the object-oriented GAMS API: .NET, Java, and Python work with .NET framework 4 (Visual Studio 2010), Java SE 5 and up, as well as Python 3.4, 2.7, and 2.6 accordingly.
In addition to the object-oriented GAMS APIs, there are expert-level (or low-level) GAMS APIs whose usage requires advanced knowledge of GAMS component libraries. See our documentation for further information on the APIs.
In addition to the APIs, GAMS offers smart links to applications like MS Excel, MatLab, or R. Through these, the user can keep working in his productive tool environment, while the application accesses all optimization capabilites of GAMS through an API. This allows for example the visualization and analyses of model data and results in the application.
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