$Title Long Range Forest Planning (STOCFOR3,SEQ=87)
$Ontext
The job of a long range forest planner is to decide what parts of the forest
will be harvested when. Important criteria for such a decision are the age
of the trees, and the likelihood that trees left standing will be destroyed by
fire.
Gassmann creates a set of K age classifications of equal length (e.g.
20 years), and places each portion of the forest into one of the classes,
according to the age of the trees within. He also divides the future planning
horizon into T rounds, each with a time length equal to that of each age
classification. That is, in one time round, any trees that are not destroyed
or harvested will move to the next age class.
Gassmann, H I, Optimal harvest of a forest in the presence of uncertainty.
Canadian Journal of Forest Research, 19, 1267-1274, 1989
Ariyawansa, K A, and Felt, A J, On a New Collection of Stochastic
Linear Programming Test Problems, INFORMS Journal on Computing 16(3),
291-299, 2004
$Offtext
Set t time steps / t1*t7 /
k age class / c1*c8 /
kn(k) age class that is not harvested / c1,c2 /
Alias (k,kk)
Parameter
d(t) discouting rate
dlast discouting rate after last period
f(t) probability of fire / #t 0.06258 /
a /0.9/, b limit on change from period to period / 1.1 /
p penalty for violating limit for yield change / 50 /
si(k) area of forest in first period
/ c1 0.241, c2 0.125, c3 1.404, c4 2.004
c5 9.768, c6 16.385, c7 2.815, c8 61.995 /
y(k) yield / c1 0, c2 0, c3 16, c4 107
c5 217, c6 275, c7 298, c8 306 /
v(k) value / c1 320.342, c2 356.187, c3 398.437, c4 448.235
c5 506.929, c6 564.929, c7 587.929, c8 595.929 /;
* 20 years and 0.005 inflation
d(t) = (1-0.004975124)**(20*(ord(t)-1));
dlast = (1-0.004975124)**(20*card(t));
Variables
obj objective
x(t,k) harvested forest
s(t,k) area of forest
rf(t,k) remaining forest
z(t) yield
slack(t) slack for violating defchglo
Positive variables x, s, rf, slack;
Equations
defobj objective
defbnd(t,k) limit harvest by area
defbal(t,k) material balance
defyield(t) yield
defchglo(t) limit change on yield
defchgup(t) limit change on yield;
defobj.. obj =e= sum(t, d(t)*(z(t) - p*slack(t))) + dlast*sum(k, v(k)*rf('t7',k));
defbnd(t,k).. rf(t,k) =e= s(t,k) - x(t,k);
defbal(t-1,k).. s(t,k) =e= (1-f(t))*(rf(t-1,k-1) + rf(t-1,k)$sameas('c8',k))
+ sum(kk, x(t-1,kk) + f(t)*rf(t-1,kk))$sameas(k,'c1');
defyield(t).. z(t) =e= sum(k, y(k)*x(t,k));
defchglo(t-1).. a*z(t-1) =l= z(t) + slack(t-1);
defchgup(t-1).. b*z(t-1) =g= z(t);
model forest /all/;
s.fx('t1',k) = si(k);
x.fx(t,kn) = 0;
file emp / '%emp.info%' /; put emp '* problem %gams.i%' /;
$onput
randvar f('t2') discrete .1736 .00000 .0299 .20268 .5128 .06258 .2837 .08612
randvar f('t3') discrete .1736 .00000 .0299 .20268 .5128 .06258 .2837 .08612
randvar f('t4') discrete .1736 .00000 .0299 .20268 .5128 .06258 .2837 .08612
randvar f('t5') discrete .6912 .00000 .3088 .20268
randvar f('t6') discrete .6912 .00000 .3088 .20268
randvar f('t7') discrete .6912 .00000 .3088 .20268
$offput
loop(t$(ord(t)>1),
put / 'stage ' ord(t):2:0 z(t) slack(t-1) defyield(t) defchglo(t-1) defchgup(t-1);
loop(k, put x(t,k) s(t,k) rf(t,k) defbnd(t,k) defbal(t-1,k)));
emp.pc=0; loop(t$(ord(t)>1), put / 'stage ' ord(t):2:0 ' f(' t.tl:0 ')');
putclose;
Set scen scenarios / s1*s512 /;
Parameter
s_f(scen,t) probability of fire by scenario;
Set dict / scen .scenario.''
f .randvar. s_f /;
solve forest max obj using emp scenario dict;