admin管理员组文章数量:1123775
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.
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.
Share Improve this question asked yesterday 八木澤友輔八木澤友輔 1 New contributor 八木澤友輔 is a new contributor to this site. Take care in asking for clarification, commenting, and answering. Check out our Code of Conduct.1 Answer
Reset to default 0There were some strange characters. If I run
.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 sustomer should be visited only 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;
}
and
.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 get a relaxed solution
版权声明：本文标题：cplex - I want to resolve "run configuration 'configuration_name' is invalid" error - Stack Ov 内容由网友自发贡献，该文观点仅代表作者本人， 转载请联系作者并注明出处：http://www.betaflare.com/web/1736595841a1945147.html， 本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌抄袭侵权/违法违规的内容，一经查实，本站将立刻删除。
发表评论