我使用约束编程在 CPLEX 中运行我的模型相当长一段时间，但仍然没有解决方案，所以我必须停止模型。
我的模型有问题吗？我尝试仅使用 Excel 文件中的几个示例来运行该模型，但在运行数小时后它仍然无法为我提供解决方案。提前非常感谢！
我的模组。文件：
using CP;
int Z_val = 8;
int P_val = 5;
int T_val = 20;
int I_val = 4;
int J_val = 5;
int K_val = ...;
int N = ...;// Planning horizon (day);
float alpha = ...;// The average loading rate of delivery trucks;
float gamma = ...;// The maximum of containers waiting in the queue at each block.
int BigM = 1000;
int fact_K_minus_1 = ...;
range Z = 1 .. Z_val;// Number of vessels
range P = 1 .. P_val;// Number of appointment periods
range T = 1 .. T_val;// Number of time intervals
range I = 1 .. I_val;// Number of gate lanes
range J = 1 .. J_val;// Number of yard blocks
range K = 1 .. K_val;// Number of RTGCs
float T_z_A[Z] = ...;
float T_z_D[Z] = ...;
float V_z[Z] = ...;
float beta_zj[Z][J] = ...;
float C_s = ...;
range KSet = 0 .. K_val - 1;
float T_l = ...;
float T_k = ...;
float Y_j[J] = ...;
float c_o = ...;
float c_R = ...;
float factorial[KSet] = ...;
// Define sets
{int} Z_j[J];
execute {
// Iterate over j and z to update Z sets
for ( var j in J) {
for ( var z in Z) {
if (beta_zj[z][j] > 0) {
Z_j[j].add(z); // Add z to the set Z_j[j]
}
}
}
// Print the result (optional)
for ( var j in J) {
writeln("Z_j[", j, "] = ", Z_j[j]);
}
}
int scale = 1000;
dvar int pS[Z] ;// Starting appointment period for delivery trucks related to vessel z
dvar int pE[Z] ;
// Decision variables
dexpr float p_z_S[z in Z] = pS[z] / scale;// Starting appointment period for delivery trucks related to vessel z
dexpr float p_z_E[z in Z] = pE[z] / scale;// Ending appointment period for delivery trucks related to vessel z
// Deived variables
float d = ceil ( T_val / P_val );// Number of time intervals in one appointment period
int m = ftoi ( d );
execute {
writeln(m);
}
dvar int lam_zp[Z][P] ;// Appointment quota of export containers related to vessel z at appointment period p
dvar int lit_it[I][T] ;// Average number of trucks waiting in queue at gate lane i at interval t
dvar int dit_it[I][T] ;// Actual discharge rate of gate lane i at interval t (truck/min)
dvar int pit_it [I][T] ;// Capacity utilization rate of gate lane i at interval t
dvar int wp_p [P] ;// Average waiting time of trucks at terminal gate during appointment period p (min)
dvar int wg_g ;// Average waiting time of trucks at terminal gate during the planning horizon (min)
dvar int djkt_jkt [J][K][T] ;// Discharge rate of RTGC k deployed to block j at interval t (natural container/min)
dvar int pjkt_jkt [J][K][T] ;// Average utilization rate of RTGC k deployed to block j at interval t
dvar int wjp_jp [J][P] ;// Average waiting time of trucks at block j in appointment period p (min)
dvar int wy_y ;// Average waiting time of trucks at yard in the planning horizon (min)
dexpr float lambda_zp[z in Z][p in P] = lam_zp[z][p] / scale;// Appointment quota of export containers related to vessel z at appointment period p
dexpr float l_it_g[i in I][t in T] = lit_it[i][t] / scale;// Average number of trucks waiting in queue at gate lane i at interval t
dexpr float d_it_g[i in I][t in T] = dit_it[i][t] / scale;// Actual discharge rate of gate lane i at interval t (truck/min)
dexpr float p_it_g[i in I][t in T] = pit_it [i][t] / scale;// Capacity utilization rate of gate lane i at interval t
dexpr float w_p_g[p in P] = wp_p[p] / scale;// Average waiting time of trucks at terminal gate during appointment period p (min)
dexpr float w_g = wg_g/ scale;// Average waiting time of trucks at terminal gate during the planning horizon (min)
dexpr float d_jkt_y[j in J][k in K][t in T] = djkt_jkt[j][k][t] / scale;// Discharge rate of RTGC k deployed to block j at interval t (natural container/min)
dexpr float p_jkt_y[j in J][k in K][t in T] = pjkt_jkt [j][k][t] / scale;// Average utilization rate of RTGC k deployed to block j at interval t
dexpr float w_jp_y[j in J][p in P] = wjp_jp [j][p] / scale;// Average waiting time of trucks at block j in appointment period p (min)
dexpr float w_y = wy_y / scale;// Average waiting time of trucks at yard in the planning horizon (min)
dvar boolean d_zp[Z][P];// Binary variable, 1 if vessel z has departed at appointment period p, 0 otherwise
dvar int lambda_zt_g[Z][T] ;// Number of trucks related to vessel z arriving at terminal gate at interval t
dvar int lambda_it_g[I][T] ;// Number of trucks arriving at gate lane i at interval t
dvar int lambda_t_y[ T] ;// Number of export containers arriving at yard at interval t
dvar int lambda_jt_y[J][T] ;// Number of export containers arriving at block j at interval t
dvar int l_jt_y[J][T] ;// Number of export containers waiting at block j at interval t
// Objective function
minimize
( sum ( t in T ) sum ( i in I ) l_it_g[i][t] + sum ( t in T ) sum ( j in J )
l_jt_y[j][t] ) * ( 24 * N * c_o / T_val ) + sum ( j in J ) sum ( k in K )
sum ( t in T ) ( 1 - p_jkt_y[j][k][t] ) * 24 * N * c_R / T_val;
subject to {
// Constraints 1
forall ( z in Z )//(1)
( p_z_E[z] - p_z_S[z] + 1 ) * 24 * N / P_val >= T_l;
forall ( z in Z )//(2)
( p_z_E[z] - p_z_S[z] + 1 ) * 24 * N / P_val <= T_k;
forall ( z in Z )//(3)
p_z_E[z] * 24 * N / P_val <= T_z_A[z];
forall ( z in Z, p in P )//(4)
lambda_zp[z][p] * ( p_z_E[z] - p_z_S[z] + 1 ) - V_z[z]<=0.1;
forall (z in Z, p in P) {
if ((p - 1) * 24 * N / P_val >= T_z_D[z]) {
d_zp[z][p] == 1;
} else {
d_zp[z][p] == 0;
}
}
forall ( j in J, p in P )//(6)
sum ( z in Z_j[j] ) ( sum ( a in 1 .. p )
( lambda_zp[z][a] - V_z[z] * d_zp[z][a] ) ) * beta_zj[z][j] <= Y_j[j];
// Constraints at gate
forall ( z in Z, p in P, t in ( ( p - 1 ) * m + 1 ) .. p * m )//(7)
lambda_zt_g[z][t] -(lambda_zp[z][p] / ( m * alpha ))<=0.1;
forall ( i in I, t in T )//(8)
lambda_it_g[i][t] -( sum ( z in Z ) lambda_zt_g[z][t] / I_val)<=0.1;
forall ( i in I, t in 1 .. T_val - 1 )//(9)
l_it_g[i][t + 1] - (maxl ( l_it_g[i][t] + lambda_it_g[i][t] -
( 24 * 60 * N * d_it_g[i][t] / T_val ), 0 ))<=0.1;
forall ( i in I, t in T )//(10)
d_it_g[i][t] - ( 19 / 60 ) * p_it_g[i][t]<=0.1;
forall ( i in I, t in T )//(11)
//l_it_g[i][t] == p_it_g[i][t] / ( 1 - p_it_g[i][t] );
l_it_g[i][t] * ( 1 - p_it_g[i][t] ) - p_it_g[i][t] <=0.1;
forall ( p in P )//(12)
w_p_g[p] * sum ( t in ( ( p - 1 ) * m + 1 ) .. p * m ) sum ( i in I )
d_it_g[i][t] - (sum ( t in ( ( p - 1 ) * m + 1 ) .. p * m ) sum
( i in I ) l_it_g[i][t])<=0.1;
w_g * sum ( t in T ) sum ( i in I ) d_it_g[i][t] - (sum ( t in T ) sum
( i in I ) l_it_g[i][t])<=10;//(13)
// Constraints at yard
forall ( t in T )//(14)
lambda_t_y[t] -( ( 24 * 60 * N / T_val ) * sum ( i in I ) d_it_g[i][t])<=0.1;
forall ( j in J, t in T )//(15)
lambda_jt_y[j][t] - (lambda_t_y[t] * alpha * sum ( z in Z_j[j] )
beta_zj[z][j] * ( lambda_zt_g[z][t] / sum ( i in I ) lambda_it_g[i][t] ))<=0.1;
forall ( j in J, t in 1 .. T_val - 1 )//(16)
l_jt_y[j][t + 1] - ( maxl ( l_jt_y[j][t] + lambda_jt_y[j][t] -
( 24 * 60 * N / T_val ) * sum ( k in K ) d_jkt_y[j][k][t], 0 ))<=0.1;
forall ( j in J, t in T, k in K //(17)
)
d_jkt_y[j][k][t] - ( 9 / 60 ) * p_jkt_y[j][k][t] <= 0.1;
forall ( j in J, t in T, k in K //(18)
)
l_jt_y[j][t] - (( ( p_jkt_y[j][k][t] * ( 1 + C_s * C_s ) ) / ( 2 *
( K_val - p_jkt_y[j][k][t] ) ) ) * ( 1 + ( fact_K_minus_1 *
( K_val - p_jkt_y[j][k][t] ) ) * ( sum ( n in KSet ) ( 1 /
( factorial[n] * ( p_jkt_y[j][k][t] ^ ( K_val - n ) ) ) ) ) ) ^ ( - 1 )
+ p_jkt_y[j][k][t] )<= 0.1;
forall ( j in J, t in T )//(19)
l_jt_y[j][t] <= gamma;
forall ( j in J, p in P )//(20)
w_jp_y[j][p] * sum ( t in ( ( p - 1 ) * m + 1 ) .. p * m, k in K )
d_jkt_y[j][k][t] -( sum ( t in ( ( p - 1 ) * m + 1 ) .. p * m )
l_jt_y[j][t])<=0.1;
w_y * sum ( t in T, j in J, k in K ) d_jkt_y[j][k][t] -( sum ( t in T ) sum
( j in J ) l_jt_y[j][t])<=0.1;//(21)
}
我的数据。文件：
SheetConnection my_sheet("eee.xlsx");
N = 7;
alpha = 1.4;
gamma = 8;
C_s = 0.42687;
T_l= 0.6;
T_k= 2.4;
c_o = 5.728;
c_R = 15.48;
K_val from SheetRead (my_sheet,"Sheet1!B4");
fact_K_minus_1 from SheetRead (my_sheet,"Sheet1!D4");
T_z_A from SheetRead (my_sheet,"Sheet1!B7:B14");
T_z_D from SheetRead (my_sheet,"Sheet1!C7:C14");
V_z from SheetRead (my_sheet,"Sheet1!D7:D14");
Y_j from SheetRead (my_sheet,"Sheet1!L7:L11");
factorial from SheetRead(my_sheet,"Sheet1!I7:I10");
beta_zj from SheetRead (my_sheet,"Sheet1!:B22:I26");
我的 Excel 文件在这里 https://docs.google.com/spreadsheets/d/1j4R45RKG53Gaz68DnBP36OLT3vitgcAd/edit#gid=1448362513
我怀疑你的优化是无限制的。您可以通过限制最大迭代次数、记下目标值、然后将最大迭代次数加倍并将新获得的目标值与之前记录的目标值进行比较来检查这一点。
但是只要看看你的约束，我猜优化器可以通过以下方式禁用其约束来将
l_it_g// maxl chooses 0
forall ( i in I, t in 1 .. T_val - 1 )//(9)
l_it_g[i][t + 1] - (maxl ( l_it_g[i][t] + lambda_it_g[i][t] -
( 24 * 60 * N * d_it_g[i][t] / T_val ), 0 ))<=0.1;
// p_it_g stays between 0 and 1
forall ( i in I, t in T )//(11)
//l_it_g[i][t] == p_it_g[i][t] / ( 1 - p_it_g[i][t] );
l_it_g[i][t] * ( 1 - p_it_g[i][t] ) - p_it_g[i][t] <=0.1;
// w_p_g goes to negative infinity as l_it_g and d_it_g stays around 1
forall ( p in P )//(12)
w_p_g[p] * sum ( t in ( ( p - 1 ) * m + 1 ) .. p * m ) sum ( i in I )
d_it_g[i][t] - (sum ( t in ( ( p - 1 ) * m + 1 ) .. p * m ) sum
( i in I ) l_it_g[i][t])<=0.1;
// w_g goes to negative infinity as l_it_g and d_it_g stays around 1
w_g * sum ( t in T ) sum ( i in I ) d_it_g[i][t] - (sum ( t in T ) sum
( i in I ) l_it_g[i][t])<=10;//(13)
简化模型是调试的良好初始步骤。下一步是强制优化器提前停止（例如通过限制最大迭代次数）并尝试解释部分结果是否有意义......
根据您问题的性质，我猜您通过使用
int+float+