haverly.gms : Haverly's pooling problem example
Haverly's pooling problem example. This is a non-convex problem.
Setting initial levels for the nonlinear variables is a good
approach to find the global optimum.
References:
- Haverly, C A, Studies of the Behavior of Recursion for the Pooling Problem. ACM SIGMAP Bull 25 (1978), 19-28.
- Adhya, N, Tawarmalani, M, and Sahinidis, N, A Lagrangian Approach to the Pooling Problem. Industrial and Engineering Chemical Research 38 (1999), 1956-1972.
Small Model of Type: NLP
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$title Haverly's pooling problem example (HAVERLY,SEQ=214)
$ontext
Haverly's pooling problem example. This is a non-convex problem.
Setting initial levels for the nonlinear variables is a good
approach to find the global optimum.
Haverly, C A, Studies of the Behavior of Recursion for the Pooling
Problem. ACM SIGMAP Bull 25 (1987), 29-32.
Adhya, N, Tawaralani, M, and Sahinidis, N, A Lagrangian Approach to
the Pooling Problem. Independent Engineering Chemical Research 38
(1999), 1956-1972.
----- crudeA ------/--- pool --|
/ |--- finalX
----- crudeB ----/ |
|--- finalY
----- crudeC ------------------|
$offtext
sets s supplies (crudes) / crudeA, crudeB, crudeC /
f final products / finalX, finalY /
i intermediate sources for final products / Pool, CrudeC /
poolin(s) crudes going into pool tank / crudeA, crudeB /
table data_S(s,*) supply data summary
price sulfur
crudeA 6 3
crudeB 16 1
crudeC 10 2
table data_f(f,*) final product data
price sulfur demand
finalX 9 2.5 100
finalY 15 1.5 200
parameters sulfur_content(s) supply quality in (percent)
req_sulfur(f) required max sulfur content (percentage)
demand(f) final product demand;
sulfur_content(s) = data_S(s,'sulfur');
req_sulfur(f) = data_F(f,'sulfur');
demand(f) = data_F(f,'demand');
equations
costdef cost equation
incomedef income equation
blend(f) blending of final products
poolbal pool tank balance
crudeCbal balance for crudeC
poolqualbal pool quality balance
blendqualbal quality balance for blending
profitdef profit equation
positive variables
crude(s) amount of crudes being used
stream(i,f) streams
q pool quality
variables
profit total profit
cost total costs
income total income
final(f) amount of final products sold;
profitdef.. profit =e= income - cost;
costdef.. cost =e= sum(s, data_S(s,'price')*crude(s));
incomedef.. income =e= sum(f, data_F(f,'price')*final(f));
blend(f).. final(f) =e= sum(i, stream(i,f));
poolbal.. sum(poolin, crude(poolin)) =e= sum(f, stream('pool',f));
crudeCbal.. crude('crudeC') =e= sum(f, stream('crudeC',f));
poolqualbal.. q*sum(f, stream('pool', f)) =e=
sum(poolin, sulfur_content(poolin)*crude(poolin));
blendqualbal(f)..
q*stream('pool',f) + sulfur_content('CrudeC')*stream('CrudeC',f)
=l= req_sulfur(f)*sum(i,stream(i,f));
final.up(f) = demand(f);
model m /all/;
* Because of the product terms, some local solver may get
* trapped at 0*0, we therefore set an initial value for q.
q.l=1;
solve m maximizing profit using nlp;
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