It makes sense to split a large GAMS model into different files in order to have a better overview of the GAMS program. A natural separation is to have the model algebra, input data and solution reporting in separate files. For example, it might be comfortable to know that the model algebra remains unchanged regardless of the modifications to data input file(s) and solution report file(s). Below you can find one suggestion that demonstrates the idea (download).

File main.gms:

$ontext
 A large model can be split up into parts in order to make the program
 easier to maintain and debug. This can be done in different ways and in here
 we use model library example trnsport.gms to illustrate one way.

 Master file "main.gms" divides the execution into steps:
 (1) model.gms : Define algebraic model independently from data
 (2) input.gms : Prepare input data
 (3) report.gms: Report solution
$offtext

* GLOBAL SETTINGS
*) Set command line parameters
$set clp lo=%gams.lo%

*) Compile model algebra.
*) Note that you can distribute the workfile .g00 instead of source file .gms
$call gams model.gms a=c s=model %clp%
*) abort on errors in all three "call gams" statements
$if errorlevel 1 $abort errors in model.gms

* PREPARE INPUT DATA
*) Remove intermediate and old files (input.gdx, report.xlsx, ...)
$call rm -f solution.gdx
*) Read data (database, Excel, ...), run model and save workfile
$call gams input.gms r=model s=wf1 %clp%
$if errorlevel 1 $abort errors in input.gms

* REPORT SOLUTION
$call gamside report.gms r=wf1 %clp%
$if errorlevel 1 $abort errors in report.gms
$log
$log --------- EXECUTION SUCCESSFUL ---------
$log

File model.gms

* We need to define the data (empty data is ok) to allow data checks etc..
$ onempty
  Sets
       i(*)   canning plants   /  /
       j(*)   markets          /  / ;
  Parameters
       a(i)   capacity of plant i in cases                    /    /
       b(j)   demand at market j in cases                     /    /
       d(i,j) distance in thousands of miles                  /    /
       c(i,j) transport cost in thousands of dollars per case /    /;

  Scalar f  freight in dollars per case per thousand miles    / na / ;

  Variables
       x(i,j)  shipment quantities in cases
       z       total transportation costs in thousands of dollars ;

  Positive Variable x ;

  Equations
       cost        define objective function
       supply(i)   observe supply limit at plant i
       demand(j)   satisfy demand at market j ;

  cost ..        z  =e=  sum((i,j), c(i,j)*x(i,j)) ;

  supply(i) ..   sum(j, x(i,j))  =l=  a(i) ;

  demand(j) ..   sum(i, x(i,j))  =g=  b(j) ;

  Model transport /all/ ;

* Data check: verify that input is acceptable, for example:
  abort$(smin(i, a(i))<=0) 'Invalid input: Plant capacity is negative or zero';

  c(i,j) = f * d(i,j) / 1000 ;

  solve transport min z using lp;

File input.gms:

* We allow redefinition of sets, parameters, etc..
$onmulti

  Sets
       i   canning plants   / seattle, san-diego /
       j   markets          / new-york, chicago, topeka / ;

  Parameters
       a(i)  capacity of plant i in cases
         /    seattle     350
              san-diego   600  /
       b(j)  demand at market j in cases
         /    new-york    325
              chicago     300
              topeka      275  / ;

  Table d(i,j)  distance in thousands of miles
                    new-york       chicago      topeka
      seattle          2.5           1.7          1.8
      san-diego        2.5           1.8          1.4  ;

  Scalar f  freight in dollars per case per thousand miles  /90/ ;

* alternatively we could read the data from a GDX file

File report.gms:

* use command line parameter r=wf1 to execute only file report.gms

 Display x.l, x.m ;

 Execute_unload 'solution';