| dc.description.abstract | The Stochastic Programming that deals with optimization of decision maker
with available uncertainty in problem data from the following times, The objeck type
of discuses is random optimization problems in wich out come of random data
inplicite on going time, and dicisions will be optimal doesn’t have to anticipative next
result (non-antisipative). This relates to optimization ‘real time’ that shaw as needs
of optimal decisions * here-and-now’ in uncertainty incomplete in virontment data.
If probabilistics information it available, the suitable operational models of
optimization ‘real time’ can be formulated as multi-stage stochastic programming.
Essentially this model is proposed to change deterministic, in which the uncertain
parameters coefisien is random independent distribution probability of decisions
variable. Under two-stage stochastic programming paradigm, the decision variables
are portioned into two set. The finit-stage variables are those that have to be decided
before the actual realizations of the uncertain parameters. Subsequently, once the
random events have presented themselves, futher design or operasional policy
improvements can be made by selection, at certain cost, the values of the second
stage or recourse variable, | en_US |
