We consider the problem of scheduling under demand uncertainty a multiproduct batch plant
represented through a state−task network. Given a scheduling horizon consisting of several
time periods in which product demands are placed, the objective is to select a schedule that
maximizes the expected profit. We present a multistage stochastic mixed integer linear
programming (MILP) model, wherein certain decisions are made irrespective of the realization
of the uncertain parameters and some decisions are made upon realization of the uncertainty.
To overcome the computational expense associated with the solution of the large-scale stochastic
multistage MILP for large problems, we examine an approximation strategy based on the solution
of a series of a two-stage models within a shrinking-horizon approach. Computational results
indicate that the proposed approximation strategy provides an expected profit within a few
percent of the multistage stochastic MILP result in a fraction of the computation time and
provides significant improvement in the expected profit over similar deterministic approaches.
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