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14 - Real-Time Scheduling

from Part IV - Special Topics

Published online by Cambridge University Press:  01 May 2021

Christos T. Maravelias
Christos T. Maravelias
Affiliation:
Princeton University, New Jersey
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Summary

The focus of the book so far has been on the development of models and solution methods to obtain high quality predicted schedules. While these two components are necessary towards the implementation of optimization-based scheduling methods, they are not sufficient by themselves. Specifically, the model must be solved repeatedly, in real time, taking into account new information and disturbances. The goal of the present chapter is to provide high-level understanding on how the optimization model should be used to obtain a real-time scheduling algorithm that yields high-quality implemented schedules.In Section 14.1, we motivate why repeated optimization is necessary, introduce necessary notation, and present the overall framework we use. In Section 14.2, we present a state-space model which offers a natural way to formulate the optimization model that is updated and solved in real time. In Section 14.3, we present the basic considerations and a general simulation-based framework for designing real-time scheduling algorithms, and, close, in Section 14.4, with a discussion on how integration with other functions can offer early feedback leading to faster recourse. We use models and examples based on network environments, but all the ideas and methods are directly applicable to problems in sequential environments.

Keywords

Online schedulingoptimization under uncertaintystate-spece modelmodel predictive controlfeedbackclosed-loop solutiondisturbancedesign through simulationreal-time scheduling algorithmrescheduling
Type
Chapter
Information
Chemical Production Scheduling
Mixed-Integer Programming Models and Methods
 , pp. 361 - 400
Publisher: Cambridge University Press
Print publication year: 2021

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References

Gupta, D, Maravelias, CT, Wassick, JM. From Rescheduling to Online Scheduling. Chem Eng Res Des. 2016;116:83–97.Google Scholar
Gupta, D, Maravelias, CT. On Deterministic Online Scheduling: Major Considerations, Paradoxes and Remedies. Comput Chem Eng. 2016;94:312–330.CrossRefGoogle Scholar
Subramanian, K, Maravelias, CT, Rawlings, JB. A State-Space Model for Chemical Production Scheduling. Comput Chem Eng. 2012;47:97–110.Google Scholar
Gupta, D, Maravelias, CT. A General State-Space Formulation for Online Scheduling. Processes. 2017;5(4):69.CrossRefGoogle Scholar
Gupta, D, Maravelias, CT. On the Design of Online Production Scheduling Algorithms. Comput Chem Eng. 2019:106517.Google Scholar
Rawlings, BC, Avadiappan, V, Lafortune, S, Maravelias, CT, Wassick, JM. Incorporating Automation Logic in Online Chemical Production Scheduling. Comput Chem Eng. 2019;128:201–215.Google Scholar
Risbeck, MJ, Maravelias, CT, Rawlings, JB. Unification of Closed-Loop Scheduling and Control: State-Space Formulations, Terminal Constraints, and Nominal Theoretical Properties. Comput Chem Eng. 2019; 129: 106496,CrossRefGoogle Scholar
Mignon, DJ, Honkomp, SJ, Reklaitis, GV. A Framework for Investigating Schedule Robustness under Uncertainty. Comput Chem Eng. 1995;19:615–620.Google Scholar
Sanmartí, E, Espuña, A, Puigjaner, L. Effects of Equipment Failure Uncertainty in Batch Production Scheduling. Comput Chem Eng. 1995;19:565–570.Google Scholar
Vin, JP, Ierapetritou, MG. Robust Short-Term Scheduling of Multiproduct Batch Plants under Demand Uncertainty. Ind Eng Chem Res. 2001;40(21):4543–4554.Google Scholar
Lin, X, Janak, SL, Floudas, CA. A New Robust Optimization Approach for Scheduling under Uncertainty: I. Bounded Uncertainty. Comput Chem Eng. 2004;28(6):1069–1085.Google Scholar
Janak, SL, Lin, X, Floudas, CA. A New Robust Optimization Approach for Scheduling under Uncertainty: II. Uncertainty with Known Probability Distribution. Comput Chem Eng. 2007;31(3):171–195.Google Scholar
Bonfill, A, Espuna, A, Puigjaner, L. Addressing Robustness in Scheduling Batch Processes with Uncertain Operation Times. Ind Eng Chem Res. 2005;44(5):1524–1534.Google Scholar
Shi, H, You, F. A Computational Framework and Solution Algorithms for Two-Stage Adaptive Robust Scheduling of Batch Manufacturing Processes under Uncertainty. AlChE J. 2016;62(3):687–703.Google Scholar
Lappas, NH, Gounaris, CE. Multi-stage Adjustable Robust Optimization for Process Scheduling under Uncertainty. AlChE J. 2016;62(5):1646–1667.Google Scholar
Orçun, S, Kuban Altinel, İ, Hortaçsu, Ö. Scheduling of Batch Processes with Operational Uncertainties. Comput Chem Eng. 1996;20:S1191–S1196.Google Scholar
Petkov, SB, Maranas, CD. Multiperiod Planning and Scheduling of Multiproduct Batch Plants under Demand Uncertainty. Ind Eng Chem Res. 1997;36(11):4864–4881.Google Scholar
Balasubramanian, J, Grossmann, IE. A Novel Branch and Bound Algorithm for Scheduling Flowshop Plants with Uncertain Processing Times. Comput Chem Eng. 2002;26(1):41–57.Google Scholar
Balasubramanian, J, Grossmann, IE. Approximation to Multistage Stochastic Optimization in Multiperiod Batch Plant Scheduling under Demand Uncertainty. Ind Eng Chem Res. 2004;43(14):3695–3713.Google Scholar
Bonfill, A, Bagajewicz, M, Espuña, A, Puigjaner, L. Risk Management in the Scheduling of Batch Plants under Uncertain Market Demand. Ind Eng Chem Res. 2004;43(3):741–750.CrossRefGoogle Scholar
Bonfill, A, Espuña, A, Puigjaner, L. Proactive Approach to Address the Uncertainty in Short-Term Scheduling. Comput Chem Eng. 2008;32(8):1689–1706.Google Scholar
Sand, G, Engell, S. Modeling and Solving Real-Time Scheduling Problems by Stochastic Integer Programming. Comput Chem Eng. 2004;28(6):1087–1103.Google Scholar
Ryu, J-H, Pistikopoulos, EN. A Novel Approach to Scheduling of Zero-Wait Batch Processes under Processing Time Variations. Comput Chem Eng. 2007;31(3):101–106.Google Scholar
J-h, Ryu, Dua, V, Pistikopoulos, EN. Proactive Scheduling under Uncertainty:  A Parametric Optimization Approach. Ind Eng Chem Res. 2007;46(24):8044–8049.Google Scholar
Li, Z, Ierapetritou, MG. Process Scheduling under Uncertainty Using Multiparametric Programming. AlChE J. 2007;53(12):3183–3203.CrossRefGoogle Scholar
Li, Z, Ierapetritou, MG. Reactive Scheduling Using Parametric Programming. AlChE J. 2008;54(10):2610–2623.Google Scholar
Kopanos, GM, Pistikopoulos, EN. Reactive Scheduling by a Multiparametric Programming Rolling Horizon Framework: A Case of a Network of Combined Heat and Power Units. Ind Eng Chem Res. 2014;53(11):4366–4386.Google Scholar
Li, ZK, Ierapetritou, MG. Reactive Scheduling Using Parametric Programming. AlChE J. 2008;54(10):2610–2623.Google Scholar
Balasubramanian, J, Grossmann, IE. Scheduling Optimization under Uncertainty – an Alternative Approach. Comput Chem Eng. 2003;27(4):469–490.CrossRefGoogle Scholar
Petrovic, D, Duenas, A. A Fuzzy Logic Based Production Scheduling/Rescheduling in the Presence of Uncertain Disruptions. Fuzzy Sets and Systems. 2006;157(16):2273–2285.Google Scholar
Cott, BJ, Macchietto, S. Minimizing the Effects of Batch Process Variability Using Online Schedule Modification. Comput Chem Eng. 1989;13(1):105–113.Google Scholar
Kanakamedala, KB, Reklaitis, GV, Venkatasubramanian, V. Reactive Schedule Modification in Multipurpose Batch Chemical Plants. Ind Eng Chem Res. 1994;33(1):77-90.CrossRefGoogle Scholar
Huercio, A, Espuña, A, Puigjaner, L. Incorporating On-Line Scheduling Strategies in Integrated Batch Production Control. Comput Chem Eng. 1995;19:609–614.Google Scholar
Sanmartí, E, Huercio, A, Espuña, A, Puigjaner, L. A Combined Scheduling/Reactive Scheduling Strategy to Minimize the Effect of Process Operations Uncertainty in Batch Plants. Comput Chem Eng. 1996;20:S1263–S1268.Google Scholar
Ko, D, Moon, I. Rescheduling Algorithms in Case of Unit Failure for Batch Process Management. Comput Chem Eng. 1997;21:S1067–S1072.CrossRefGoogle Scholar
Panek, S, Engell, S, Subbiah, S, Stursberg, O. Scheduling of Multi-product Batch Plants Based upon Timed Automata Models. Comput Chem Eng. 2008;32(1):275–291.Google Scholar
Henning, GP, Cerdá, J. Knowledge-Based Predictive and Reactive Scheduling in Industrial Environments. Comput Chem Eng. 2000;24(9):2315–2338.Google Scholar
Palombarini, J, Martínez, E. SmartGantt – an Interactive System for Generating and Updating Rescheduling Knowledge Using Relational Abstractions. Comput Chem Eng. 2012;47:202–216.CrossRefGoogle Scholar
Novas, JM, Henning, GP. Reactive Scheduling Framework Based on Domain Knowledge and Constraint Programming. Comput Chem Eng. 2010;34(12):2129–2148.Google Scholar
Elkamel, ALI, Mohindra, A. A Rolling Horizon Heuristic for Reactive Scheduling of Batch Process Operations. Engineering Optimization. 1999;31(6):763–792.Google Scholar
Vin, JP, Ierapetritou, MG. A New Approach for Efficient Rescheduling of Multiproduct Batch Plants. Ind Eng Chem Res. 2000;39(11):4228–4238.Google Scholar
Sand, G, Engell, S, Märkert, A, Schultz, R, Schulz, C. Approximation of an Ideal Online Scheduler for A Multiproduct Batch Plant. Comput Chem Eng. 2000;24(2):361–367.Google Scholar
Méndez, CA, Cerdá, J. Dynamic Scheduling in Multiproduct Batch Plants. Comput Chem Eng. 2003;27(8):1247–1259.Google Scholar
Munawar, SA, Gudi, RD. A Multilevel, Control-Theoretic Framework for Integration of Planning, Scheduling, and Rescheduling. Ind Eng Chem Res. 2005;44(11):4001–4021.Google Scholar
Janak, SL, Floudas, CA, Kallrath, J, Vormbrock, N. Production Scheduling of a Large-Scale Industrial Batch Plant. II. Reactive Scheduling. Ind Eng Chem Res. 2006;45(25):8253–8269.Google Scholar
Goel, V, Grossmann, IE. A stochastic Programming Approach to Planning of Offshore Gas Field Developments under Uncertainty in Reserves. Comput Chem Eng. 2004;28(8):1409–1429.Google Scholar
Goel, V, Grossmann, IE, El-Bakry, AS, Mulkay, EL. A Novel Branch and Bound Algorithm for Optimal Development of Gas Fields under Uncertainty in Reserves. Comput Chem Eng. 2006;30(6-7):1076–1092.Google Scholar
Tarhan, B, Grossmann, IE, Goel, V. Stochastic Programming Approach for the Planning of Offshore Oil or Gas Field Infrastructure under Decision-Dependent Uncertainty. Ind Eng Chem Res. 2009;48(6):3078–3097.Google Scholar
Colvin, M, Maravelias, CT. A Stochastic Programming Approach for Clinical Trial Planning in New Drug Development. Comput Chem Eng. 2008;32(11):2626–2642.CrossRefGoogle Scholar
Colvin, M, Maravelias, CT. Scheduling of Testing Tasks and Resource Planning in New Product Development Using Stochastic Programming. Comput Chem Eng. 2009;33(5):964–976.Google Scholar
Colvin, M, Maravelias, CT. Modeling Methods and a Branch and Cut Algorithm for Pharmaceutical Clinical Trial Planning Using Stochastic Programming. Eur J Oper Res. 2010;203(1):205–215.Google Scholar
Gupta, D, Maravelias, CT. Framework for Studying Online Production Scheduling under Endogenous Uncertainty. Comput Chem Eng. 2019:135, 106670.Google Scholar
Rawlings, JB, Mayne, DQ. Model Predictive Control: Theory and Design. Madison: Nob Hill Pub., 2009.Google Scholar
Rawlings, JB, Risbeck, MJ. Model Predictive Control with Discrete Actuators: Theory and Application. Automatica. 2017;78:258–265.CrossRefGoogle Scholar
Nystrom, RH, Franke, R, Harjunkoski, I, Kroll, A. Production Campaign Planning Including Grade Transition Sequencing and Dynamic Optimization. Comput Chem Eng. 2005;29(10):2163–2179.Google Scholar
Flores-Tlacuahuac, A, Grossmann, IE. Simultaneous Cyclic Scheduling and Control of a Multiproduct CSTR. Ind Eng Chem Res. 2006;45(20):6698–6712.Google Scholar
Terrazas-Moreno, S, Flores-Tlacuahuac, A, Grossmann, IE. Simultaneous Cyclic Scheduling and Optimal Control of Polymerization Reactors. AlChE J. 2007;53(9):2301–2315.CrossRefGoogle Scholar
Zhuge, J, Ierapetritou, MG. Integration of Scheduling and Control with Closed Loop Implementation. Ind Eng Chem Res. 2012;51(25):8550–8565.CrossRefGoogle Scholar
Chu, Y, You, F. Integration of Scheduling and Control with Online Closed-Loop Implementation: Fast Computational Strategy and Large-Scale Global Optimization Algorithm. Comput Chem Eng. 2012;47:248–268.Google Scholar
Gutiérrez-Limón, MA, Flores-Tlacuahuac, A, Grossmann, IE. MINLP Formulation for Simultaneous Planning, Scheduling, and Control of Short-Period Single-Unit Processing Systems. Ind Eng Chem Res. 2014;53(38):14679–14694.Google Scholar
Du, J, Park, J, Harjunkoski, I, Baldea, M. A Time Scale-Bridging Approach for Integrating Production Scheduling and Process Control. Comput Chem Eng. 2015;79:59–69.Google Scholar
Nie, Y, Biegler, LT, Wassick, JM, Villa, CM. Extended Discrete-Time Resource Task Network Formulation for the Reactive Scheduling of a Mixed Batch/Continuous Process. Ind Eng Chem Res. 2014; 53(44):17112–17123.Google Scholar
Nie, Y, Biegler, LT, Villa, CM, Wassick, JM. Discrete Time Formulation for the Integration of Scheduling and Dynamic Optimization. Ind Eng Chem Res. 2015;54(16):4303–4315.Google Scholar
Engell, S, Harjunkoski, I. Optimal Operation: Scheduling, Advanced Control and Their Integration. Comput Chem Eng. 2012;47:121–133.Google Scholar
Kim, J, Kim, J, Moon, I. Error-Free Scheduling for Batch Processes Using Symbolic Model Verifier. Journal of Loss Prevention in the Process Industries. 2009;22(4):367–372.Google Scholar
Rawlings, BC, Wassick, JM, Ydstie, BE. Application of Formal Verification and Falsification to Large-Scale Chemical Plant Automation Systems. Comput Chem Eng. 2018;114:211–220.Google Scholar
Suresh, P, Wassick, JM, Ferrio, J, editors. Real Time Performance Measurement for Batch Chemical Plants. Wint Simul C Proc; 2011;12325–2335.Google Scholar
Faggian, A, Facco, P, Doplicher, F, Bezzo, F, Barolo, M. Multivariate Statistical Real-Time Monitoring of an Industrial Fed-Batch Process for the Production of Specialty Chemicals. Chemical Engineering Research and Design. 2009;87(3):325–334.Google Scholar
Pattison, RC, Touretzky, CR, Harjunkoski, I, Baldea, M. Moving Horizon Closed-Loop Production Scheduling Using Dynamic Process Models. AlChE J. 2017;63(2):639–651.Google Scholar
Touretzky, CR, Harjunkoski, I, Baldea, M. Dynamic Models and Fault Diagnosis-Based Triggers for Closed-Loop Scheduling. AlChE J. 2017;63(6):1959–1973.CrossRefGoogle Scholar
Harjunkoski, I, Maravelias, CT, Bongers, P, Castro, PM, Engell, S, Grossmann, IE, et al. Scope for Industrial Applications of Production Scheduling Models and Solution Methods. Comput Chem Eng. 2014;62(0):161–193.CrossRefGoogle Scholar
Blackburn, JD, Kropp, DH, Millen, RA. A Comparison of Strategies to Dampen Nervousness in MRP Systems. Manage Sci. 1986;32(4):413–429.Google Scholar
Sridharan, V, Berry, WL. Master Production Scheduling Make-to-Stock Products: A Framework for Analysis. International Journal of Production Research. 1990;28(3):541–558.Google Scholar
Wu, SD, Storer, RH, Pei-Chann, C. One-Machine Rescheduling Heuristics with Efficiency and Stability as Criteria. Computers & Operations Research. 1993;20(1):1–14.Google Scholar
Kazan, O, Nagi, R, Rump, CM. New Lot-Sizing Formulations for Less Nervous Production Schedules. Computers & Operations Research. 2000;27(13):1325–1345.Google Scholar
Kopanos, GM, Capon-Garcia, E, Espuna, A, Puigjaner, L. Costs for Rescheduling Actions: A Critical Issue for Reducing the Gap between Scheduling Theory and Practice. Ind Eng Chem Res. 2008;47(22):8785–8795.Google Scholar
McAllister, RD, Rawlings, JB, Maravelias, CT. Rescheduling Penalties for Economic Model Predictive Control and Closed-Loop Scheduling. Ind Eng Chem Res. 2019; 59(6):2214–2228.Google Scholar
Lee, H, Gupta, D, Maravelias, CT. Systematic Generation of Alternative Production Schedules. AlChE J. 2020; 66(5):e16926.Google Scholar
Mathur, P, Swartz, CLE, Zyngier, D, Welt, F. Uncertainty Management via Online Scheduling for Optimal Short-Term Operation of Cascaded Hydropower Systems. Comput Chem Eng. 2020;134:106677.Google Scholar

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