Re: Multi-objective vs Stochastic!

Gus Gassmann <[EMAIL PROTECTED]> wrote in message news:<[EMAIL PROTECTED]>...
> Michael wrote:
> 
> > Hi everyone,
> >
> > Suppose a model is LP. However all decision variables are BINARY since
> > the problem structure does impose that.
> >
> > It is also a multi-objective one since we have several conflicting
> > objectives (criteria). That means we need to generate Pareto solutions
> > by varying the objective weights, and Pareto frontiers by performing
> > sensitivity and scenario analysis.
> >
> > If uncertainty is to be considered and Monte Carlo or Stochastic is to
> > be used.
> >
> > Question: Is there a need to do simulation/stochastic since many (most
> > or all) of the solutions/frontiers obtained from the sensitivity and
> > scenario analysis would/could be the same as the ones obtained from
> > simulation/stochastic?
> 
> Stochastic programming is emphatically not the same as
> scenario analysis, as a good stochastic solution hedges
> against a variety of possible futures. So a better way
> to approach your problem is to set up a full stochastic
> program with multiple (carefully selected) scenarios for
> each set of objective weights.
> 
> I think you should start by reading a book on stochastic
> programming, such as Birge and Louveaux or Kall and Wallace.
> 
> Hope that helps.

Thanks! Okay, so if uncertainty is to be considered, what are the
pluses and minuses when it comes to using Monte-Carlo or Stochastic
Programming?