Quick start - Gams

---- Shipsw Amount Shipped To Warehouse
Shipsw(S1,A)
(.LO, .L, .UP = 0, 0, 1000)
-1 TCOSTEQ
1 SUPPLYEQ(S1)
-1 BALANCE(A)
Shipsw(S1,B)
(.LO, .L, .UP = 0, 0, 1000)
-2 TCOSTEQ
1 SUPPLYEQ(S1)
-1 BALANCE(B)
REMAINING 4 ENTRIES SKIPPED
Equation solution report
The equation solution LST also shows all existing cases grouped under each equation name
when set dependency is present as illustrated below in the context of our core optimization
example (optimize.gms).
---- EQU constraint
LOWER LEVEL UPPER MARGINAL
Land -INF 100.000 100.000 52.000
Labor -INF 500.000 500.000 9.500
Variable solution report
The variable solution LST segment also shows all existing cases grouped under each variable
name when set dependency is present as illustrated below in the context of our core optimization
example (optalgebra.gms).
---- VAR x
LOWER LEVEL UPPER MARGINAL
Corn . 50.000 +INF .
Wheat . 50.000 +INF .
Cotton . . +INF -13.000
Good modeling practices
Above I have covered the essential GAMS features one would employ in any modeling exercise.
However I have not done very good job of exploiting a major GAMS capability involved selfdocumentation.
In any modeling exercise there are an infinite variety of choices that can be made
in naming the variables, equations, parameters, sets etc. and formatting their presentation in the
GMS instruction file. Across these choices that can be large differences in the degree of selfdocumentation
within the GMS code. In particular, as explained in the chapter on Rules for Item
Names, Element names and Explanatory Text, one employ short names like x(j) as in
optalgebra.gms or longer names (up to 31 characters) for the variables like production(products).
I advocate use of longer names to enhance the readability of the document.
The GAMS also permits one to add comments, for example telling what is being done by
Courtesy of B.A. McCarl, October 2002 34