Description
This is an alternative formulation of the model PAPERCO found in Computational Economics, Chapter 9. This version introduces several sets to partition the equation and variable space into four groups. This example further shows how to implement the suggested scenarios by using a LOOP statement.
Small Model of Type : LP
Category : GAMS Model library
Main file : paperco.gms
$title Vertically Integrated Company (PAPERCO,SEQ=102)
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This is an alternative formulation of the model PAPERCO found in
Computational Economics, Chapter 9. This version introduces
several sets to partition the equation and variable space into
four groups. This example further shows how to implement the
suggested scenarios by using a LOOP statement.
Thompson, G, and Thor, S, Computational Economics: Economic Modeling
with Optimization Software. The Scientific Press, San Francisco, 1991.
Keywords: linear programming, production planning, pulp and paper industry,
scenario analysis, manufacturing
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$eolCom //
Set
l 'log suppliers' / company, farmer /
w 'wood products' / ground, chips /
p 'pulp types' / pulp-1, pulp-2 /
q 'paper types' / kraft, newsprint, printing /;
Table ap(w,p) 'pulp manufacturing input requirements'
pulp-1 pulp-2
ground .6 .3
chips .4 .7;
Table aq(p,q) 'paper manufacturing input requirements'
kraft newsprint printing
pulp-1 .68 .45 .25
pulp-2 .32 .55 .75;
Table cw(w,p) 'wood shipment cost'
pulp-1 pulp-2
(ground
chips ) 40 55;
Table cp(p,q) 'pulp shipment cost'
kraft newsprint printing
pulp-1 40 60 70
pulp-2 55 50 45;
Table sdat(q,*) 'sales data'
lower upper
kraft 18 25
newsprint 12 15
printing 0 7;
Parameter
pq(q) 'sales price' / kraft 265, newsprint 275, printing 310 /
pp(p) 'price of pulp'
pc(w) 'price of wood products' / ground 18, chips 16 /;
Scalar plog / 65 /;
Positive Variable
logs(l) 'purchases of logs (tons)'
xw(w,p) 'shipments of wood products (tons)'
pulp(p) 'production of pulp (tons)'
xp(p,q) 'shipments of pulp (tons)'
paper(q) 'production and sales of paper products (tons)'
sales(p) 'sales of pulp (tons)'
purchase(p) 'purchase of pulp (tons)';
Variable profit 'net operating income';
Equation
logbal
wbal(w,p)
pbal(p)
qbal(p,q)
obj;
logbal.. .97*sum(l, logs(l)) =e= sum((w,p), xw(w,p));
wbal(w,p).. xw(w,p) =e= ap(w,p)*pulp(p);
pbal(p).. sum(q, xp(p,q)) =e= purchase(p) - sales(p) + pulp(p);
qbal(p,q).. xp(p,q) =e= aq(p,q)*paper(q);
obj.. profit =e= sum(p, pp(p)*sales(p)) // sales of pulp
+ sum(q, pq(q)*paper(q)) // sales of paper
- sum(l, plog*logs(l)) // cost of logs
- sum((p,q), cp(p,q)*xp(p,q)) // transport cost of pulp
- sum((w,p), (cw(w,p)+pc(w))*xw(w,p)) // transport cost of wood
- sum(p, pp(p)*purchase(p));
paper.lo(q) = sdat(q,'lower');
paper.up(q) = sdat(q,'upper');
Model wood / all /;
Set scenario 'scenario identifier' / scenario-1*scenario-3 /;
Table psdat(scenario,p,*) 'bounds on pulp trade (tons)'
pulp-1.s pulp-1.p pulp-2.s pulp-2.p
scenario-1
scenario-2 3 5 3 5
scenario-3 6 6 10 10;
Table ppdat(scenario,p) 'price data for pulp trade ($ per tons)'
pulp-1 pulp-2
scenario-1 120 140
scenario-2 120 140
scenario-3 120 150;
loop(scenario,
purchase.up(p) = psdat(scenario,p,'p');
sales.up(p) = psdat(scenario,p,'s');
pp(p) = ppdat(scenario,p);
solve wood maximizing profit using lp;
option limCol = 0, limRow = 0;
);