Robust multi-objective scheduling in an evaporation network

Considering uncertainty in continuous production processes is key to compute short-time optimal schedules which can be trusted in practice. This paper proposes a two-step stochastic approach to the robust scheduling of several evaporation plants. This approach considers the possibility of reacting in the future once the uncertainty materializes. Each evaporator has different features (capacity, equipment, etc.) and the individual performance is affected by external factors and fouling effects. Moreover, a multi-objective analysis has been carried out to provide a decision support for the operator who must take the concrete decisions about load allocation and cleaning tasks along a time horizon. The problem has been solved by discretizing the time horizon and adapting the general-precedence method to deal with an unknown number of tasks. The nonlinear behavior of each plant is approximated by surrogate linear models obtained experimentally, providing thus solutions in acceptable time.