Reactive Scheduling by a Multiparametric Programming Rolling Horizon Framework: A Case of a Network of Combined Heat and Power Units

Abstract

In this work, we introduce an approach for the reactive scheduling of production systems with bounded uncertain parameters. The proposed method follows a state-space representation for the scheduling problem, and relies on the use of a rolling horizon framework and multiparametric programming techniques. We show that by considering as uncertain parameters the set of variables that describe the state of the system at the beginning of the prediction horizon, we can effectively formulate a set of state-space multiparametric programming problems that are solved just once and off-line. In contrast to existing approaches, the repetitive solution of a new multiparametric problem after each disruptive event is avoided. The results of the parametric optimization are used in a rolling horizon basis without the need for online optimization. The proposed multiparametric programming rolling horizon (mp-RH) approach is applied in the scheduling problem of a network of combined heat and power units (i.e., a unit commitment problem type). Several case studies are solved, potential extensions of the proposed method are provided, and challenging areas wherein research is necessary are discussed.

Model-based multi-parametric programming strategies towards the integration of design, control and operational optimization

“…However, the recently developed representation of scheduling problems in a state-space model [335] has allowed for the applicability of the concepts of mp-MPC in scheduling [183]. This has led to the idea of integration of scheduling and control within a multi-parametric framework [371].…”

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A meta-multiparametric framework: Application to the operation of bio-based energy supply chains

“…Additionally, the network management is directly affected by several types of uncertainty in which we can highlight the demand uncertainty. Therefore, in order to ensure the reliability of the system, uncertainty must be considered due to the fact that the deterministic solution may become suboptimal or even infeasible (Kopanos and Pistikopoulos, 2013). Different methods and tools have been proposed to consider uncertainty in the formulation of optimization models at different supply chain levels (demand, weather conditions, biomass supply, price variability across the time, etc).…”