即使代码没有错误，我也无法模拟 CPLEX 模型

Question

i have some doubts related to a CPLEX code that i'm trying to write. The code itself (model) seems to be well written, but when it comes to fill the data i have an error. NOTE: there are no constraints in the model because i'm trying to make a trial of the model just to see that it works.

Here is the code:

 using CP; // NETWORK+PARAMETERS int trucks=...; // set of trucks range truck= 1..trucks; int capacity [truck]=...; // capacity of a truck tuple nodeinfo { string name; // name of a node int starttime; // available start time int endtime; // available end time float demand; // demand from a node } {nodeinfo} departurenode=...; //size=4 {nodeinfo} arrivalnode=...; //size=4 {nodeinfo} startingnode=...; //size=4 // OPTION 2: vector of nodes //each node has tuple structure nodeinfo //length of the vector is length of dataset //read data from dataset //void* node = new {nodeinfo} [k]; tuple arc { nodeinfo departurenode; //departure node of an arc nodeinfo arrivalnode; // arrival node of an arc nodeinfo startingnode; // starting node of an arc int traveltime; // travel time of an arc } {arc} arcs=...; //number of arcs (24) float cost [arcs][truck]; //cost of using an arc by a truck // option2: int arc[i in departurenode,j in arrivalnode,k in startingnode]=...; //arcs (size=24) // how can i create a setof arcs taking into account the info from each arc?? // VARIABLES dvar boolean x [arcs,truck]; // 1 if truck uses the arc, 0 otherwise (array of size 24x7) dvar int+ arrivaltime [arrivalnode,truck]; //arrival time of a truck at a node (array size of 4x7) // OBJECTIVE FUNCTION dexpr float totalcost = sum (i in arcs, j in truck) x [i,j] * cost [i,j]; minimize totalcost; // CONSTRAINTS subject to {} execute { writeln(arcs); };

Here is the data:

 trucks= 2; departurenode= [[A,0,10000,0],[B,0,10000,0],[C,0,10000,10],[D,0,10000,10]]; arrivalnode= [[A,0,10000,0],[B,0,10000,0],[C,0,10000,10],[D,0,10000,10]]; startingnode=[[A,0,10000,0],[B,0,10000,0],[C,0,10000,10],[D,0,10000,10]]; arcs= [[<A,0,10000,0>,<A,0,10000,0>,<C,0,10000,10>,5], [<A,0,10000,0>,<A,0,10000,0>,<D,0,10000,10>,5], [<B,0,10000,0>,<B,0,10000,0>,<C,0,10000,10>,5], [<B,0,10000,0>,<B,0,10000,0>,<D,0,10000,10>,5], [<C,0,10000,0>,<C,0,10000,0>,<A,0,10000,10>,5], [<C,0,10000,0>,<C,0,10000,0>,<B,0,10000,10>,5], [<D,0,10000,0>,<D,0,10000,0>,<A,0,10000,10>,5], [<D,0,10000,0>,<D,0,10000,0>,<B,0,10000,10>,5]] cost= [<1,1>,<1,1>,<1,1>,<1,1>,<1,1>,<1,1>,<1,1>,<1,1>]

*NOTE: in each gap of the arc before the travel time (last gap value=5) must be the data from departurenode, arrival node and startingnode but it's not shown and i don't know why *

Another doubt: regarding the constraints of the model i don't know how to write them into CPLEX.

1) starttime <= arrivaltime <= endtime //(for each arrivalnode)

2) x * (arrivaltime (node i) + traveltime) <= arrivaltime (node j)

3) Initialize the variable arrivaltime for each truck to 0. (at the beginning of the simulation)

4)The demand for each arrival node has to be equal to the sum of (the arcs chosen * capacity of the truck)

Thank you so much.

Answer 1

Let me help you with the syntax.

In the .mod

Comment:

//int capacity [truck]=...; // capacity of a truck

since that's not defined in the . dat , and write:

float cost [arcs][truck]=...;

since that one is in the .dat .

The .dat should be changed into:

trucks= 2;

 departurenode= {<A,0,10000,0>,<B,0,10000,0>,<C,0,10000,10>,<D,0,10000,10>};

 arrivalnode= {<A,0,10000,0>,<B,0,10000,0>,<C,0,10000,10>,<D,0,10000,10>};

 startingnode={<A,0,10000,0>,<B,0,10000,0>,<C,0,10000,10>,<D,0,10000,10>};

 arcs= {<<A,0,10000,0>,<A,0,10000,0>,<C,0,10000,10>,5>, <<A,0,10000,0>,<A,0,10000,0>,<D,0,10000,10>,5>,
 <<B,0,10000,0>,<B,0,10000,0>,<C,0,10000,10>,5>, <<B,0,10000,0>,<B,0,10000,0>,<D,0,10000,10>,5>,
 <<C,0,10000,0>,<C,0,10000,0>,<A,0,10000,10>,5>, <<C,0,10000,0>,<C,0,10000,0>,<B,0,10000,10>,5>,
 <<D,0,10000,0>,<D,0,10000,0>,<A,0,10000,10>,5>, <<D,0,10000,0>,<D,0,10000,0>,<B,0,10000,10>,5>};

 cost= [[1,1],[1,1],[1,1],[1,1],[1,1],[1,1],[1,1],[1,1]];

and then you will be able to run and then improve.