I looked at the GAMS python API but the documentation only describing already predefined models (run .gms with some tweaking options). My question is now: Can I somehow build a gams model from scratch with data I have imported in python. When I worked with gurobi everything seemed easy, but my professor forces me to use Gams... It seems to me GAMS is only good for hardcoding and doesn't give me a lot of flexibility.
I found this, but it just looks like it inserts the whole model in a string with the GAMS syntax...
from gams import * import os import sys def get_data_text(): return ''' 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/ ; ''' def get_model_text(): return ''' 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 Scalar f freight in dollars per case per thousand miles; $if not set gdxincname $abort 'no include file name for data file provided' $gdxin %gdxincname% $load i j a b d f $gdxin Parameter c(i,j) transport cost in thousands of dollars per case ; c(i,j) = f * d(i,j) / 1000 ; 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/ ; Solve transport using lp minimizing z ; Display x.l, x.m ; ''' if __name__ == "__main__": if len(sys.argv) > 1: ws = GamsWorkspace(system_directory = sys.argv) else: ws = GamsWorkspace() t3 = ws.add_job_from_string(get_data_text()) t3.run() t3.out_db.export(os.path.join(ws.working_directory, "tdata.gdx")) t3 = ws.add_job_from_string(get_model_text()) opt = ws.add_options() opt.defines["gdxincname"] = "tdata" opt.all_model_types = "xpress" t3.run(opt) for rec in t3.out_db["x"]: print("x(" + rec.key(0) + "," + rec.key(1) + "): level=" + str(rec.level) + " marginal=" + str(rec.marginal)) t3a = ws.add_job_from_string(get_data_text()) t3b = ws.add_job_from_string(get_model_text()) t3a.run() opt.defines["gdxincname"] = t3a.out_db.name t3b.run(opt, databases=t3a.out_db) for rec in t3b.out_db["x"]: print("x(" + rec.key(0) + "," + rec.key(1) + "): level=" + str(rec.level) + " marginal=" + str(rec.marginal)) ```