Description
Solves a two-stage stochastic linear program of the form: min c'x + E[Q(x,s)] s.t. Ax = b, x >= 0, where Q(x,s) = min q(s)'y(s) s.t. Wy(s) >= h(s) - T(s)x, y >= 0; Data is from Dr. Janos Mayer from Univ of Zurich, documented in the paper: A simulation-based approach to two-stage stochastic programming with recourse by Alexander Sharpiro and Tito Homem-de-Mello, 1998. In the given data, h is a normal vector with mean h_avg and standard devication h_stddev. The model is solved by the LINDO solver. The command-line parameter NSCEN is used to set the number of samples to be generated by the solver. Contributor: Yanchao Liu, January 2012. Can only be solved with Lindo
Small Model of Type : SP
Category : GAMS EMP library
Main file : gen2s.gms
$title Two stage stochastic program in the generic form (GEN2S,SEQ=91)
$ontext
Solves a two-stage stochastic linear program of the form:
min c'x + E[Q(x,s)] s.t. Ax = b, x >= 0, where
Q(x,s) = min q(s)'y(s) s.t. Wy(s) >= h(s) - T(s)x, y >= 0;
Data is from Dr. Janos Mayer from Univ of Zurich, documented in the paper: A
simulation-based approach to two-stage stochastic programming with recourse by
Alexander Sharpiro and Tito Homem-de-Mello, 1998.
In the given data, h is a normal vector with mean h_avg and standard devication
h_stddev.
The model is solved by the LINDO solver. The command-line parameter NSCEN is
used to set the number of samples to be generated by the solver.
Contributor: Yanchao Liu, January 2012.
$offtext
* Can only be solved with Lindo
$ifi not %GAMS.emp%==lindo $exit
$if not set NSCEN $set NSCEN 100
Sets N1R Index of rows in first stage / i1 * i5 /
N1C Index of cols in first stage / j1 * j10 /
N2R Index of rows in second stage / ii1*ii10 /
N2C Index of cols in second stage / jj1*jj15 /;
Table FirstStageData(*,*)
j1 j2 j3 j4 j5 j6 j7 j8 j9 j10 b
c 0.73 -2.16 -0.31 9.00 -5.33 4.30 5.80 6.17 -0.09 2.65
i1 0 -4.19 0 0 0 4.12 0 0 -3.53 0 -1.24
i2 -0.34 -1.88 0 0 0 0 -1.32 0 0 -4.54 -2.79
i3 0 3.04 8.34 3.41 -7.90 0 0 6.45 0 9.80 -8.00
i4 0 0 -9.97 0 0 0 5.26 0 0 -0.89 -1.94
i5 0 -0.92 0 6.57 0 0 2.05 0 2.17 -2.31 2.61;
Parameters
A(N1R,N1C), c(N1C), b(N1R);
A(N1R,N1C) = FirstStageData(N1R,N1C);
c(N1C) = FirstStageData('c',N1C);
b(N1R) = FirstStageData(N1R,'b');
Table SecondStageData(*,*)
jj1 jj2 jj3 jj4 jj5 jj6 jj7 jj8 jj9 jj10 jj11 jj12 jj13 jj14 jj15 h_avg h_stddev
q 0 0 4.80 5.99 9.46 7.01 0 6.46 2.88 0 0 4.95 0 0 1.29
ii1 0 0 0 0 0 -2.3 0 0 0.18 -6.37 8.49 0 -18.45 0 0 -3.88 0.15
ii2 0 0 0 4.36 0 0 5.31 0 0 0 8.96 0 0 0 0 1.12 0.01
ii3 0 9.17 0 0 0 0 0 0 0 0 3.45 -6.02 0 0 -6.6 -4.63 0.21
ii4 -0.07 5.48 0 0 -1.6 5.38 0 -2.74 -5.49 -3.05 0 0.29 0 1.8 0 5.04 0.25
ii5 0 0 0 0 0 2.96 2.15 0 -7.52 0 2.41 0 0 0 0 2.05 0.04
ii6 0 0 7.93 9.69 0 0 1.65 0 -8.92 0 -10.36 0 0 0 0 5.19 0.27
ii7 0 0 -7.41 0 0 0 0 0 0 0 0 7.41 0 0 0 -5.53 0.31
ii8 0 -7.35 0 5.35 0 0 0 0 0 0 2.69 -0.69 0 0 0 3.80 0.14
ii9 0 0 0 0 0 0 -5.73 -2.58 0 0 0 5.27 3.05 0 0 1.81 0.03
ii10 0 0 0 0 7.43 3.74 2.09 0 8.96 -5.88 0 0 -16.36 0 0 -9.29 0.86;
Parameters
q(N2C), W(N2R,N2C), h_avg(N2R), h_stddev(N2R), h(N2R);
W(N2R,N2C) = SecondStageData(N2R,N2C);
q(N2C) = SecondStageData('q',N2C);
h_avg(N2R) = SecondStageData(N2R,'h_avg');
h_stddev(N2R) = SecondStageData(N2R,'h_stddev');
h(N2R) = h_avg(N2R);
table T(N2R,N1C)
j1 j2 j3 j4 j5 j6 j7 j8 j9 j10
ii1 -8.42 0 0 0 0 0 6.91 0 -2.07 0
ii2 -5.23 0 0 0 0 0 0 0 0 0
ii3 -2.14 0 0 0 1.16 0 0 0 0 0
ii4 1.19 0 0 0 0 0 0 -6.05 0 -4.82
ii5 0 0 5.48 0 0 0 0 -4.75 0 0
ii6 2.38 0 2.90 0 0 -0.88 0 0 0 0
ii7 0 0 0 0 0 0 0 1.04 0 0
ii8 2.61 0 0 -0.91 0 0 0 0 -4.93 0
ii9 -5.79 0 0 0 0 0 0 0 9.60 0
ii10 -2.64 0 0 0 0 0 0 0 0 0;
Positive variable x(N1C), y(N2C);
Variable z1, z2;
Equations
o1 first stage objective
c1(N1R) fist stage constraints
o2 second stage objective
c2(N2R) second stage constraints;
o1.. z1 =e= sum(N1C, c(N1C)*x(N1C)) + z2;
c1(N1R).. sum(N1C, A(N1R,N1C)*x(N1C)) =e= b(N1R);
o2.. z2 =e= sum(N2C,q(N2C)*y(N2C));
c2(N2R).. sum(N2C,W(N2R,N2C)*y(N2C)) =g= h(N2R) - sum(N1C, T(N2R,N1C)*x(N1C));
model gen2 /all/;
*solve gen2 min z1 using lp;
file emp / '%emp.info%' /;
put emp 'stage 2 z2 y h o2 c2';
loop(N2R, put / 'randvar ' h.tn(N2R) ' normal ' h_avg(N2R) ' ' h_stddev(N2R));
putclose;
Set s scenarios / s1*s%NSCEN% /
Parameter
s_h(s,N2R) random variable realization
s_y(s,N2C) level of y by scenario
s_z2(s) z2 by scenario;
Set dict / s. scenario.''
h. randvar. s_h
y. level. s_y
z2. level. s_z2 /;
$ifi not %GAMS.emp%==lindo $goto contsolve
$echo STOC_NSAMPLE_STAGE=%NSCEN% > lindo.opt
gen2.optfile = 1;
$label contsolve
solve gen2 min z1 using emp scenario dict;
display s_h, s_y, s_z2, x.l;