I want to resolve "run configuration 'configuration_name' is invalid" error

Question

We are trying to solve an optimization problem related to the Hub Location Routing Problem with IBM ILOG CPLEX Optimization Studio 22.1.1.

The OPL project configuration is simply Project -> (run configuration) -> configuration(default) -> CSAHLRPSTW3.mod CSAHLRPSTW3.dat. When I run the run configuration, I get an error that "run configuration 'configuration' is invalid."

I also ran "oplrun CSAHLRPSTW3.mod CSAHLRPSTW3.dat" at the command prompt and got an error "Infeasibility row 'ct5(1)': 0 = 1". It seems that constraint expression (5) is wrong. If I comment out (5), constraint expression (8) becomes Infeasibility row 'ct8(1)(1)': 0 >= 8.

I asked ChatGPT-4o and Claude 3.5 Sonnet, but they could not find the cause of the problem, so I would like to know the cause of the error and how to solve it.

Below are the model and data files.

model file

/*********************************************
 * OPL 22.1.1.0 Model
 * Author: 
 * Creation Date: 2025/01/05 at 1:54:46
CSAHLRPSTW3.mod
 *********************************************/
// Data section
int n = ...;  // number of nodes from node.dat
range N = 1..n;
range C = N;   // set of customers (non-hub nodes)
range H = N;   // set of potential hubs

// Data reading from external files
tuple Coordinate {
   float x;
   float y;
}
Coordinate coordinates[N] = ...;

float d[N][N] = ...; // demand matrix
float F[N] = ...;    // fixed costs for hubs
float Qh[N] = ...;   // hub capacities
int K = ...;         // number of vehicles
float t[N][N] = ...; // travel times
tuple TimeWindow {
   float start;
   float end;
}
TimeWindow timewindows[N] = ...;

// Constants
float Qv = 1100;     // vehicle capacity
float M = 1000000;   // big M
float alpha = 0.004; // unit inter-hub transportation cost
float beta = 1.0;    // unit local delivery cost
float zeta = 1.0;    // early arrival penalty coefficient
float gamma = 2.0;      // late arrival penalty coefficient

// Calculate distances between nodes
float c[i in N][j in N] = sqrt(pow(coordinates[i].x - coordinates[j].x, 2) + 
                              pow(coordinates[i].y - coordinates[j].y, 2));

// Decision Variables
dvar boolean x[N][N][H]; // routing variable
dvar boolean y[H];       // hub location variable
dvar float+ z[C][C][H][H]; // flow variable
dvar float+ u[N][H];    // pickup amount
dvar float+ v[N][H];    // delivery amount
dvar float+ s[N];       // service start time
dvar float+ P[N];       // time window penalty
dvar float+ visitOrder[i in N];  // exclude sub-tour 
// Rest of the model remains the same...
// [Previous constraints and objective function code goes here]
// Optimization Model
minimize
    beta * sum(i in N, j in N, m in H) c[i][j] * x[i][j][m] +
    sum(m in H) F[m] * y[m] +
    alpha * sum(m in H, n in H, i in C, j in C) d[i][j] * z[i][j][m][n] * c[m][n] +
    sum(i in N) P[i];

subject to {
    //self roop protection
    forall(i in N, m in H)
    ct0:x[i][i][m] == 0;
    //return to the hubs
    forall(m in H)
    ct1:sum(i in N: i != m) x[i][m][m] == sum(j in N: j != m) x[m][j][m];
    // Constraint (2): All demand must be routed through hubs
    forall(i in C, j in C)
        ct2:sum(m in H, n in H) z[i][j][m][n] == 1;
    
    // Constraint (3): Hub capacity constraints
    forall(m in H)
        ct3:sum(i in C, j in C, n in H) d[i][j] * z[i][j][m][n] +
        sum(i in C, j in C, n in H: n != m) d[i][j] * z[i][j][n][m] <= Qh[m] * y[m];
    
    // Constraint (4): Vehicles can only depart from established hubs
    forall(m in H)
        ct4:sum(j in N) x[m][j][m] <= M * y[m];
    
    // Constraint (5): Each customer must be visited exactly once　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　
    forall(i in C)
        ct5:sum(j in N: j != i, m in H) x[i][j][m] == 1;
    
    // Constraint (6): Flow conservation
    forall(i in N, m in H)
        ct6:sum(j in N) x[i][j][m] == sum(j in N) x[j][i][m];
    
    // Constraint (7): No inter-hub routes in local delivery
    forall(m in H)
        ct7:sum(j in N, n in H: n != m) x[n][j][m] == 0;
    
    // Constraint (8): Link between flow and routing variables
    forall(i in C, m in H)
        ct8:M * sum(j in N) x[i][j][m] >= 
        sum(j in C, n in H) z[i][j][m][n] + sum(j in C, n in H) z[j][i][n][m];
    
    // Constraint (9): Pickup load continuity
    forall(i in N, j in C, m in H: i != j)
        ct9:u[i][m] + sum(t in C, n in H) d[j][t] * z[j][t][m][n] - 
        M * (1 - x[i][j][m]) <= u[j][m];
    
    // Constraint (10): Delivery load continuity
    forall(i in C, j in N, m in H: i != j)
        ct10:v[i][m] - sum(t in C, n in H) d[t][i] * z[t][i][n][m] + 
        M * (1 - x[i][j][m]) >= v[j][m];
    
    // Constraint (11): Vehicle capacity constraint for delivery
    forall(i in C, m in H)
        ct11:v[i][m] <= Qv;
    
    // Constraint (12): Vehicle capacity constraint considering loading/unloading
    forall(i in C, m in H)
        ct12:u[i][m] + v[i][m] - sum(t in C, n in H) d[t][i] * z[t][i][n][m] <= Qv;
    
    // Constraint (13): Number of vehicles limit
    ct13:sum(m in H) sum(j in N) x[m][j][m] <= K;
    
    // Constraint (14): Time consistency
    forall(i in N, j in N, m in H)
        ct14:s[i] + t[i][j] - s[j] <= M * (1 - x[i][j][m]);
    
    // Constraint (15): Time window penalties
    forall(i in C) {
        ct15:P[i] >= zeta * (timewindows[i].start - s[i]);
        P[i] >= gamma * (s[i] - timewindows[i].end);
        P[i] >= 0;};
    //Constraint (16): prohibit subtour
    forall(i in N, j in N, m in H: i != j && i != m && j != m)
       ct16: visitOrder[i] - visitOrder[j] + n * x[i][j][m] <= n - 1;

};

// Solver configuration
execute {
    cp.tilim = 10800; // Time limit: 10800 seconds
}

data file

/*********************************************
 * OPL 22.1.1.0 Data
 * Author: 
 * Creation Date: 2025/01/05 at 1:55:06
 CSAHLRPSTW3.dat
 *********************************************/

n = 4;

coordinates = [
<12944.330389, 19522.690462>,
<23487.769950, 14749.226168>,
<31004.306839, 21458.349354>,
<49728.492698, 17946.318136>
];

d = [
[5.705460, 9.801600, 5.028110, 3.105340], 
[21.242070, 38.706030, 18.462480, 10.396320],
[5.763190, 9.909430, 9.328980, 8.665890],
[3.820190, 6.300870, 9.807810, 10.572880]
];

F = [
28766.736921,
28376.761527,
29774.238965,
24301.334212
];

Qh = [
2198.532635,
6106.711881,
2226.593104,
4515.466789
];

K = 4;

timewindows = [
<0,10000>,
<0,10000>,
<0,10000>,
<0,10000>

];

t = [
[0.00 233.68 19.41 529.92],
[233.68 0.00 436.25 352.77],
[19.41 436.25 0.00 339.71], 
[529.92 352.77 339.71 0.00]

];

I changed n=4 in the data file to n=8 and set the other data accordingly, but the same error occurred.

I want to resolve "run configuration 'configuration_name' is invalid" error

model file

data file

Answers (1)

Related Questions

I want to resolve &quot;run configuration &#39;configuration_name&#39; is invalid&quot; error

model file

data file

Answers (1)

Related Questions

I want to resolve "run configuration 'configuration_name' is invalid" error