Discrete Time Formulation for the Integration of Scheduling and Dynamic Optimization

Nie, Yisu; Biegler, Lorenz T.; Villa, Carlos; Wassick, John M.

doi:10.1021/ie502960p

Cited by 77 publications

(44 citation statements)

References 46 publications

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“…Thus, being able to generate and revise schedules in an online fashion, so as to account for new information and disturbances, is very much a reality now. The Dow Chemical Company has already adopted online scheduling in many of its production facilities [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A General State-Space Formulation for Online Scheduling

Gupta

Maravelias

2017

Processes

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Abstract:We present a generalized state-space model formulation particularly motivated by an online scheduling perspective, which allows modeling (1) task-delays and unit breakdowns; (2) fractional delays and unit downtimes, when using discrete-time grid; (3) variable batch-sizes; (4) robust scheduling through the use of conservative yield estimates and processing times; (5) feedback on task-yield estimates before the task finishes; (6) task termination during its execution; (7) post-production storage of material in unit; and (8) unit capacity degradation and maintenance. Through these proposed generalizations, we enable a natural way to handle routinely encountered disturbances and a rich set of corresponding counter-decisions. Thereby, greatly simplifying and extending the possible application of mathematical programming based online scheduling solutions to diverse application settings. Finally, we demonstrate the effectiveness of this model on a case study from the field of bio-manufacturing.

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Section: Introductionmentioning

confidence: 99%

A General State-Space Formulation for Online Scheduling

Gupta

Maravelias

2017

Processes

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“…Economic benefit from integrating process dynamics into batch and semi-batch scheduling has also been demonstrated via mixed-logic dynamic optimization in state equipment networks and solution with Benders decomposition in resource task networks [7,39]. Potential for economic benefit from integrating process dynamics into design, scheduling, and control problems has also been demonstrated [41,44,52].…”

Section: Previous Workmentioning

confidence: 99%

Economic Benefit from Progressive Integration of Scheduling and Control for Continuous Chemical Processes

et al. 2017

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Performance of integrated production scheduling and advanced process control with disturbances is summarized and reviewed with four progressive stages of scheduling and control integration and responsiveness to disturbances: open-loop segregated scheduling and control, closed-loop segregated scheduling and control, open-loop scheduling with consideration of process dynamics, and closed-loop integrated scheduling and control responsive to process disturbances and market fluctuations. Progressive economic benefit from dynamic rescheduling and integrating scheduling and control is shown on a continuously stirred tank reactor (CSTR) benchmark application in closed-loop simulations over 24 h. A fixed horizon integrated scheduling and control formulation for multi-product, continuous chemical processes is utilized, in which nonlinear model predictive control (NMPC) and continuous-time scheduling are combined.

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“…21,25,36,49 Efficient solution methods are needed to reduce the computational time. 20,50 In this work, we apply the flexible recipe method and a bilevel decomposition method to increase the computational efficiency. 18 …”

Section: Equation 48 Determines the Initial Value Of The State Variabmentioning

confidence: 99%

Novel Optimization Model and Efficient Solution Method for Integrating Dynamic Optimization with Process Operations of Continuous Manufacturing Processes

Shi

Chu

You

2015

Ind. Eng. Chem. Res.

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In this paper, we propose a novel mathematical model for the integrated optimization for production planning, scheduling, and dynamic optimization of continuous manufacturing processes. With a novel approach to estimate the inventory cost, the integrated problem is first formulated as a mixed-integer dynamic optimization (MIDO) problem, which is then reformulated into a large scale mixed-integer nonlinear program (MINLP). By using metamodeling to characterize the detailed linking information between different decision layers, we develop an efficient solution method to decompose the integrated MINLP into a mixed-integer linear program (MILP) for an integrated planning and scheduling problem with flexible recipes, and a set of dynamic optimization problems for generating metamodels. We further apply a bilevel decomposition algorithm to improve the computational efficiency of solving the integrated planning and scheduling problem with flexible recipes. The proposed models and algorithms are demonstrated through case studies of methyl methacrylate (MMA) polymerization processes. The results show that the proposed methods reduce the computational time by more than 2 orders of magnitude compared with the approach of solving the entire integrated optimization problem directly.

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Discrete Time Formulation for the Integration of Scheduling and Dynamic Optimization

Cited by 77 publications

References 46 publications

A General State-Space Formulation for Online Scheduling

A General State-Space Formulation for Online Scheduling

Economic Benefit from Progressive Integration of Scheduling and Control for Continuous Chemical Processes

Novel Optimization Model and Efficient Solution Method for Integrating Dynamic Optimization with Process Operations of Continuous Manufacturing Processes

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