Optimal multiperiod scheduling of multiproduct batch plants under demand uncertainty

Purpose - The purpose of this paper is to set up a two-stage stochastic integer-programming model (TSM) for the multiperiod scheduling of multiproduct batch plants under demand uncertainty involving the constraints of material balances and inventory constraints, as well as the penalty for production shortfalls and excess. Design/methodology/approach - Scheduling model is formulated as a discrete-time 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 for a single and multiple product with a given probability level. The stochastic elements of the model are expressed with equivalent deterministic optimization models. Findings - The TSM model not only allows for uncertain product demand correlations, but also gives different processing modes by a range of batch sizes and a task-dependent processing time. The experimental results show that the TSM model is more appropriate than another model for multiperiod scheduling of multiproduct batch plants under correlated uncertain demand. Research limitations/implications - The choice of penalty parameter of demand uncertainty is the main limitation. Practical implications - The paper provides very useful advice for multiperiod scheduling of multiproduct batch plants under demand uncertainty. Originality/value - A stochastic model for the multiperiod scheduling of multiproduct batch plants under demand uncertainty was set up. A test problem involving 12 correlated uncertain product demands and two alternative models verified the availability of the TSM.