transvi.gms : VI version of the transport model

Description

```Example showing how to write a VI as an MCP

We want to write the VI using EMP to avoid manual translation to MCP. We use
the definitions of VI and MCP from

Steven P. Dirkse,  Ph.D. Dissertation
Robust Solution of Mixed Complementarity Problems.
Mathematical Programming Technical Report 94-12, August 1994.
ftp://ftp.cs.wisc.edu/math-prog/tech-reports/94-12.ps
Pages 4-6

In this case, the VI to start with is what we get by letting F(x) = df/dx,
where f is the objective in the transport model.

We adjusted the data to get nonzero supply marginals.

Contributor: Steven Dirkse and Jan-H. Jagla, January 2009
```

Small Model of Type : VI

Category : GAMS EMP library

Main file : transvi.gms

``````\$title VI version of the transport model (TRANSVI,SEQ=2)

\$ontext

Example showing how to write a VI as an MCP

We want to write the VI using EMP to avoid manual translation to MCP. We use
the definitions of VI and MCP from

Steven P. Dirkse,  Ph.D. Dissertation
Robust Solution of Mixed Complementarity Problems.
Mathematical Programming Technical Report 94-12, August 1994.
ftp://ftp.cs.wisc.edu/math-prog/tech-reports/94-12.ps
Pages 4-6

In this case, the VI to start with is what we get by letting F(x) = df/dx,
where f is the objective in the transport model.

We adjusted the data to get nonzero supply marginals.

Contributor: Steven Dirkse and Jan-H. Jagla, January 2009

\$offtext

Sets
i   canning plants   / seattle, san-diego /
j   markets          / new-york, chicago, topeka / ;

Parameters

a(i)  capacity of plant i in cases
/    seattle     350
san-diego   600  /

b(j)  demand at market j in cases
/    new-york    325
chicago     300
topeka      275  / ;

Table d(i,j)  distance in thousands of miles
new-york       chicago      topeka
seattle          2.2           1.7          1.8
san-diego        2.5           1.8          1.4  ;

Scalar f  freight in dollars per case per thousand miles  /90/ ;

Parameter c(i,j)  transport cost in thousands of dollars per case ;

c(i,j) = f * d(i,j) / 1000 ;

Variables
x(i,j)  shipment quantities in cases
z       total transportation costs in thousands of dollars ;

Positive Variable x ;

Equations
cost        define objective function
supply(i)   observe supply limit at plant i
demand(j)   satisfy demand at market j ;

cost ..        z  =e=  sum((i,j), c(i,j)*x(i,j)) ;

supply(i) ..   sum(j, x(i,j))  =l=  a(i) ;

demand(j) ..   sum(i, x(i,j))  =g=  b(j) ;

Model transport /all/ ;

* Solve the LP
option lp = pathnlp;
Solve transport using lp minimizing z ;
abort\$[transport.modelstat <> %MODELSTAT.OPTIMAL%] 'LP not solved';

*-------------------------------------------------------------------------------
* That's how the VI looks like

positive variable
dPrice(j)   'demand price';
negative variable
sPrice(i)   'supply price*(-1)';
equations

ggrad(i,j)..  c(i,j) - sPrice(i) - dPrice(j) =N= 0;

model mcpTransport / ggrad.x, supply.sPrice, demand.dPrice /;

*Adopt solution from LP solve and verify it is a solution of the MCP
sPrice.l(i) = supply.m(i);
dPrice.l(j) = demand.m(j);
mcpTransport.iterlim = 0;
solve mcpTransport using mcp;
abort\$[mcpTransport.objVal > 1e-6] 'Input for model mcpTransport should be optimal, was not';

*-------------------------------------------------------------------------------
* Now use EMP to this reformulation

* F(x) = c for our VI: LP models yield a linear VI
equations

model viTransport / grad, supply, demand /;

file myinfo / '%emp.info%' /;
put myinfo '* complementarity pairs for grad.x' /;