lambda :=
0 0   0
0 1   0
0 2   0
0 3   0
1 0   0
1 1   0
1 2   0
1 3   0
2 0   0
2 1   0
2 2   0
2 3   0
3 0   0
3 1   0
3 2   0
3 3   0
;

mu = 0

iteration 1
MINOS 5.51: optimal solution found.
2 iterations, objective 4000
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 4000

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -1
0 1    0
0 2    0
0 3    0
1 0    0
1 1    0
1 2   -1
1 3    0
2 0    0
2 1    0
2 2    0
2 3    0
3 0    0
3 1    0
3 2    0
3 3    0
;

mu = 0

iteration 2
MINOS 5.51: optimal solution found.
1 iterations, objective 3960
Nonlin evals: constrs = 5, Jac = 4.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
2 iterations, objective -9.8e-11
Nonlin evals: constrs = 6, Jac = 5.
Solution du 2nd probleme
:  m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0   0            0        20      20
1   0            0        20      20
2   4.9e-11      0        20      20
3   0            0        20      20
;

Fonction duale à maximiser :
theta_k = 3960

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2  -4.9e-11
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  -4.8999e-11
1   0
2   0
3  -4.8999e-11
;

V_c_s[t] - V_c_s_b[t] [*] :=
0   0
1  -4.8999e-11
2  -4.8999e-11
3   0
;

V_tot - V_tot_b = -4.8999e-11

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -2
0 1    0
0 2    0
0 3    0
1 0    0
1 1    0
1 2   -2
1 3    0
2 0    0
2 1    0
2 2    0
2 3    0
3 0    0
3 1    0
3 2    0
3 3    0
;

mu = 0

iteration 3
MINOS 5.51: optimal solution found.
0 iterations, objective 3920
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 3920

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -3
0 1    0
0 2    0
0 3    0
1 0    0
1 1    0
1 2   -3
1 3    0
2 0    0
2 1    0
2 2    0
2 3    0
3 0    0
3 1    0
3 2    0
3 3    0
;

mu = 0

iteration 4
MINOS 5.51: optimal solution found.
0 iterations, objective 3880
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 3880

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -4
0 1    0
0 2    0
0 3    0
1 0    0
1 1    0
1 2   -4
1 3    0
2 0    0
2 1    0
2 2    0
2 3    0
3 0    0
3 1    0
3 2    0
3 3    0
;

mu = 0

iteration 5
MINOS 5.51: optimal solution found.
0 iterations, objective 3840
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 3840

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -5
0 1    0
0 2    0
0 3    0
1 0    0
1 1    0
1 2   -5
1 3    0
2 0    0
2 1    0
2 2    0
2 3    0
3 0    0
3 1    0
3 2    0
3 3    0
;

mu = 0

iteration 6
MINOS 5.51: optimal solution found.
0 iterations, objective 3800
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 3800

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -6
0 1    0
0 2    0
0 3    0
1 0    0
1 1    0
1 2   -6
1 3    0
2 0    0
2 1    0
2 2    0
2 3    0
3 0    0
3 1    0
3 2    0
3 3    0
;

mu = 0

iteration 7
MINOS 5.51: optimal solution found.
0 iterations, objective 3760
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 3760

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -7
0 1    0
0 2    0
0 3    0
1 0    0
1 1    0
1 2   -7
1 3    0
2 0    0
2 1    0
2 2    0
2 3    0
3 0    0
3 1    0
3 2    0
3 3    0
;

mu = 0

iteration 8
MINOS 5.51: optimal solution found.
0 iterations, objective 3720
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 3720

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -8
0 1    0
0 2    0
0 3    0
1 0    0
1 1    0
1 2   -8
1 3    0
2 0    0
2 1    0
2 2    0
2 3    0
3 0    0
3 1    0
3 2    0
3 3    0
;

mu = 0

iteration 9
MINOS 5.51: optimal solution found.
0 iterations, objective 3680
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 3680

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -9
0 1    0
0 2    0
0 3    0
1 0    0
1 1    0
1 2   -9
1 3    0
2 0    0
2 1    0
2 2    0
2 3    0
3 0    0
3 1    0
3 2    0
3 3    0
;

mu = 0

iteration 10
MINOS 5.51: optimal solution found.
0 iterations, objective 3640
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 3640

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -10
0 1     0
0 2     0
0 3     0
1 0     0
1 1     0
1 2   -10
1 3     0
2 0     0
2 1     0
2 2     0
2 3     0
3 0     0
3 1     0
3 2     0
3 3     0
;

mu = 0

iteration 11
MINOS 5.51: optimal solution found.
0 iterations, objective 3600
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 3600

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -11
0 1     0
0 2     0
0 3     0
1 0     0
1 1     0
1 2   -11
1 3     0
2 0     0
2 1     0
2 2     0
2 3     0
3 0     0
3 1     0
3 2     0
3 3     0
;

mu = 0

iteration 12
MINOS 5.51: optimal solution found.
0 iterations, objective 3560
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 3560

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -12
0 1     0
0 2     0
0 3     0
1 0     0
1 1     0
1 2   -12
1 3     0
2 0     0
2 1     0
2 2     0
2 3     0
3 0     0
3 1     0
3 2     0
3 3     0
;

mu = 0

iteration 13
MINOS 5.51: optimal solution found.
0 iterations, objective 3520
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 3520

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -13
0 1     0
0 2     0
0 3     0
1 0     0
1 1     0
1 2   -13
1 3     0
2 0     0
2 1     0
2 2     0
2 3     0
3 0     0
3 1     0
3 2     0
3 3     0
;

mu = 0

iteration 14
MINOS 5.51: optimal solution found.
0 iterations, objective 3480
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 3480

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -14
0 1     0
0 2     0
0 3     0
1 0     0
1 1     0
1 2   -14
1 3     0
2 0     0
2 1     0
2 2     0
2 3     0
3 0     0
3 1     0
3 2     0
3 3     0
;

mu = 0

iteration 15
MINOS 5.51: optimal solution found.
0 iterations, objective 3440
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 3440

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -15
0 1     0
0 2     0
0 3     0
1 0     0
1 1     0
1 2   -15
1 3     0
2 0     0
2 1     0
2 2     0
2 3     0
3 0     0
3 1     0
3 2     0
3 3     0
;

mu = 0

iteration 16
MINOS 5.51: optimal solution found.
0 iterations, objective 3400
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 3400

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -16
0 1     0
0 2     0
0 3     0
1 0     0
1 1     0
1 2   -16
1 3     0
2 0     0
2 1     0
2 2     0
2 3     0
3 0     0
3 1     0
3 2     0
3 3     0
;

mu = 0

iteration 17
MINOS 5.51: optimal solution found.
0 iterations, objective 3360
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 3360

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -17
0 1     0
0 2     0
0 3     0
1 0     0
1 1     0
1 2   -17
1 3     0
2 0     0
2 1     0
2 2     0
2 3     0
3 0     0
3 1     0
3 2     0
3 3     0
;

mu = 0

iteration 18
MINOS 5.51: optimal solution found.
0 iterations, objective 3320
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 3320

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -18
0 1     0
0 2     0
0 3     0
1 0     0
1 1     0
1 2   -18
1 3     0
2 0     0
2 1     0
2 2     0
2 3     0
3 0     0
3 1     0
3 2     0
3 3     0
;

mu = 0

iteration 19
MINOS 5.51: optimal solution found.
0 iterations, objective 3280
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 3280

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -19
0 1     0
0 2     0
0 3     0
1 0     0
1 1     0
1 2   -19
1 3     0
2 0     0
2 1     0
2 2     0
2 3     0
3 0     0
3 1     0
3 2     0
3 3     0
;

mu = 0

iteration 20
MINOS 5.51: optimal solution found.
0 iterations, objective 3240
Nonlin evals: constrs = 3, Jac = 2.
Solution du 1er probleme
: m_load m_unload Q_load Q_unload V_h_s V_c_s    :=
0    20       0     1000       0    20    20
1     0       0        0       0    20    20
2     0      20        0    1000    20    20
3     0       0        0       0    20    20
;

V_tot = 40
T_h_s = 45
T_c_s = 35

MINOS 5.51: optimal solution found.
0 iterations, objective 0
Nonlin evals: constrs = 3, Jac = 2.
Solution du 2nd probleme
: m_load_b m_unload_b V_h_s_b V_c_s_b    :=
0     0         0        20      20
1     0         0        20      20
2     0         0        20      20
3     0         0        20      20
;

Fonction duale à maximiser :
theta_k = 3240

Valeur des gradients:
:m_load[t] - m_load_b[t] [*] :=
0  20
1   0
2   0
3   0
;

m_unload[t] - m_unload_b[t] [*] :=
0   0
1   0
2  20
3   0
;

V_h_s[t] - V_h_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_c_s[t] - V_c_s_b[t] [*] :=
0  0
1  0
2  0
3  0
;

V_tot - V_tot_b = 0

Valeur des nouveaux lambda_t et mu
lambda :=
0 0   -20
0 1     0
0 2     0
0 3     0
1 0     0
1 1     0
1 2   -20
1 3     0
2 0     0
2 1     0
2 2     0
2 3     0
3 0     0
3 1     0
3 2     0
3 3     0
;

mu = 0