Integration of control theory and scheduling methods for supply chain management

A model predictive control framework for centralised management of a supply chain dynamical system

“…The latest development in the application of MPC to SCM is the distributed implementation. Subramanian, Rawlings, Maravelias, Flores-Cerrillo, and Megan (2013) proposed cooperative MPC scheme with closed-loop stability and used the method in a two-node supply chain as an example. A distributed MPC is presented by Ferramosca, Limona, Alvarado, and Camacho (2013) to track the changing non-zero setpoints and this strategy is applicable to any finite number of subsystems.…”

“…A distributed MPC is presented by Ferramosca, Limona, Alvarado, and Camacho (2013) to track the changing non-zero setpoints and this strategy is applicable to any finite number of subsystems. The reader is referred to several proper review papers (Sarimveis, Patrinos, Tarantilis, & Kiranoudis, 2008;Subramanian et al, 2013) on the application of control engineering techniques to the SCM problems.…”

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

“…In [183] the procedure for deriving and solving scheduling problems via multiparametric programming, using a state-space model representation [336] and a mp-MILP reformulation is presented. The models used for scheduling in [183] and [336] are based on a stochastic model approach as they do not consider information regarding the process at hand or its dynamics.…”

“…Shobrys and White [323] Interactions of planning scheduling and control and their impact to decision making in process industry operations Mahadevan et al [225] Robust control for the targeting of transition times in scheduling of polymerization operation Chatzidoukas et al [64] Impact of control structure on process operability, product quality optimization and time optimal grade transition Chatzidoukas et al [63] Integration of production scheduling and optimal grade transition profiles with a MIDO approach Nystrom et al [254] Production optimization through determination of transition trajectories, operating points and manufacturing sequence Flores-Tlacuahuac and Grossmann [120,121] Simulataneous cyclic scheduling and control via reformulating an MIDO problem into an MINLP Harjunkoski et al [153] Discussion on the problems arising from the integration of production scheduling and control and its implementation Biegler and Zavala [44] Real-time optimization and control for decision making via the formulation and e cient solution of NLP Subramanian et al [336] Distributed MPC and Cooperative MPC to integrate scheduling objectives with process operation constraints Subramanian et al [335] MI scheduling problem formulation based on state space models Zhuge and Ierapetritou [371] Continuous-time event-point formulation scheduling incorporating explicit constraints derived from mp-MPC to target complexity Kopanos and Pistikopoulos [183] Reactive scheduling of a state space representation based system using multi-parametric programming Baldea and Harjunkoski [19] A comprehensive systematic review of the integration of scheduling and control You and Grossmann [365] Supply chain optimization under uncertainty via multi-period MINLP…”

“…[70,119] Simultaneous/Decomposition (MI)DO schedule and P-PI-PID control [58,93,225,269] Simultaneous/Decomposition algorithms using control/dynamics aware scheduling models [42,122,370] Simultaneous/Decomposition algorithms via (MI)DO reformulation to (MI)NLP [161,183,184,277,335,336] Control theory in scheduling problems [146,147,277,362] Advanced control and (MI)NLP scheduling schemes [75,158,166,204,270] Scheduling under uncertainty [19,29,102,126,142,153] Review articles on scheduling and control and methodologies…”