Processes and markets uncertainties make batch plants a complex environment to manage production activities. Uncertainties may cause deviations and infeasibilities in predefined schedules; this may result in poor planning and inefficient utilization of materials. Consequently, the relevance of explicitly incorporating variability in the scheduling formulation in order to offer more efficient plans and robust decisions to changes has become recognized. This work addresses the batch plants scheduling under exogenous uncertainty. The most widely utilized approach to tackle this problem is stochastic programming; however its solution results in high computational expenses. From another standpoint S-graph, a graph-theoretic approach, has proved to be very efficient to deal with deterministic scheduling. In this work, the S-graph framework is enhanced so that stochastic scheduling problems can be handled. For this purpose, a LP model that is used as performance evaluator has been coupled with S-graph framework. One of the main advantages of the proposed approach is that the search space does not increase according to the number of scenarios considered in the problem. Finally, the potential of the proposed framework is highlighted through two illustrative examples.

Peer Reviewed