Description

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.

Small Model of Type : NLP

Category : GAMS Model library

Main file : haverly.gms

$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 (1978), 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 ------------------|

Keywords: nonlinear programming, chemical engineering, pooling problem
$offText

Set
   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;

Parameter
   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');

Equation
   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 Variable
   crude(s)     'amount of crudes being used'
   stream(i,f)  'streams'
   q            'pool quality';

Variable
   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;