Hello,
I have a question regarding eliminating some invalid decision variable commbinations in the preprocessing.
I have a 2-dimensional decision variable X indexed with [commodities][arcs].Please see the code below.The index "commodities" is based on the tuple "Data".The index "arcs" is based on the tuple "Arc".Both tuples "Data" and "Arc" are based on a tuple "TimeNode".
I dont'want the program iterate all X combinations. I want to remove some of the decision variables in the preprocessing, by considering a condition where I compare Origin time in the Data tuple and FromNode time in the Arc tuple and if "Origin.time>FromNode.time", X equals 0,thus eliminated from iteration and not regarded. Thus, the size of the problem will be reduced and some memory saved. I made some examplary code below, with execute. If i have, for example, {Data} commodities= {<1 <1 3><2 8>>...}, 1 is an id, <1 3> - Origin with 1 as a starting node and 3 is a start time. I do not need the problem to regard all Xs earlier than start time 3.
However, the execute does not work as X is a decision variable. What could I possibly do? Formulate the condition as a constraint? Thank you. I would appriciate any advice.
tuple TimeNode {
int node;
int time;}
{TimeNode} tnodes = { <n,t> | n in Nodes, t in Time };
tuple Data{
int id;
TimeNode Origin;
TimeNode Destination; }
{Data} commodities= {<1 <1 3><2 8>>, <2 <2 5><3 10>>};
tuple Arc {
TimeNode FromNode
TimeNode ToNode; }
{Arc} arcs={<<i,s>,<j,e>> | <i,j> in st_arcs, s in Time , e in Time };
dvar float+ x[commodities][arcs];
execute {
for(var c in commodities){
for(var a in arcs){
if(c.Origin.time>a.FromNode.time)
x[c][a]=0;
}
}
}
"ERROR:Assignment to decision variable in "x # 0 # 0" not possible."
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Irina
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#DecisionOptimization