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Part IV - Special Topics

Published online by Cambridge University Press:  01 May 2021

Christos T. Maravelias
Affiliation:
Princeton University, New Jersey
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Chemical Production Scheduling
Mixed-Integer Programming Models and Methods
, pp. 287 - 434
Publisher: Cambridge University Press
Print publication year: 2021

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References

References

Maravelias, CT. A Decomposition Framework for the Scheduling of Single- and Multi-stage Processes. Comput Chem Eng. 2006;30(3):407420.Google Scholar
Prasad, P, Maravelias, CT. Batch Selection, Assignment and Sequencing in Multi-stage Multi-product Processes. Comput Chem Eng. 2008;32(6):11061119.Google Scholar
Sundaramoorthy, A, Maravelias, CT. Simultaneous Batching and Scheduling in Multistage Multiproduct Processes. Ind Eng Chem Res. 2008;47(5):15461555.Google Scholar
Castro, PM, Grossmann, IE, Novais, AQ. Two New Continuous-Time Models for the Scheduling of Multistage Batch Plants with Sequence Dependent Changeovers. Ind Eng Chem Res. 2006;45(18):62106226.Google Scholar
Merchan, AF, Lee, H, Maravelias, CT. Discrete-Time Mixed-Integer Programming Models and Solution Methods for Production Scheduling in Multistage Facilities. Comput Chem Eng. 2016;94:387410.Google Scholar
Althaus, E, Bockmayr, A, Elf, M, Junger, M, Kasper, T, Mehlhorn, K. SCIL – Symbolic Constraints in Integer Linear Programming. Lect Notes Comput Sc. 2002;2461:7587.CrossRefGoogle Scholar
Marriott, K, Stuckey, PJ. Programming with Constraints: An Introduction. Cambridge: MIT Press; 1998. xiv, 467 p. p.Google Scholar
Van Hentenryck, P, Michel, L. Constraint-Based Local Search. Cambridge: MIT Press; 2005. xix, 422 p. p.Google Scholar
Hooker, J. Logic-Based Methods for Optimization: Combining Optimization and Constraint Satisfaction. New York: John Wiley & Sons; 2000. xvi, 495 p. p.Google Scholar
Hooker, J. Integrated Methods for Optimization. New York: Springer; 2007. xiv, 486 p. p.Google Scholar
Bockmayr, A, Pisaruk, N. Detecting Infeasibility and Generating Cuts for Mixed Integer Programming Using Constraint Programming. Computers & Operations Research. 2006;33(10):27772786.CrossRefGoogle Scholar
Jain, V, Grossmann, IE. Algorithms for Hybrid MILP/CP Models for a Class of Optimization Problems. INFORMS Journal on Computing. 2001;13(4):258276.CrossRefGoogle Scholar
Hooker, JN. Planning and Scheduling by Logic-Based Benders Decomposition. Oper Res. 2007;55(3):588602.Google Scholar
Sadykov, R, Wolsey, LA. Integer Programming and Constraint Programming in Solving a Multimachine Assignment Scheduling Problem with Deadlines and Release Dates. INFORMS Journal on Computing. 2006;18(2):209217.Google Scholar
Harjunkoski, I, Grossmann, IE. Decomposition Techniques for Multistage Scheduling Problems Using Mixed-Integer and Constraint Programming Methods. Comput Chem Eng. 2002;26(11):1533–1552.Google Scholar
Roe, B, Papageorgiou, LG, Shah, N. A Hybrid MILP/CLP Algorithm for Multipurpose Batch Process Scheduling. Comput Chem Eng. 2005;29(6):12771291.Google Scholar
Balas, E, Lancia, G, Serafini, P, Vazacopoulos, A. Job Shop Scheduling with Deadlines. J Comb Optim. 1998;1(4):329353.Google Scholar
Balas, E, Vazacopoulos, A. Guided Local Search with Shifting Bottleneck for Job Shop Scheduling. Manage Sci. 1998;44(2):262275.CrossRefGoogle Scholar
Castro, PM, Aguirre, AM, Zeballos, LJ, Mendez, CA. Hybrid Mathematical Programming Discrete-Event Simulation Approach for Large-Scale Scheduling Problems. Ind Eng Chem Res. 2011;50(18):1066510680.Google Scholar
Chu, Y, Wassick, JM, You, F. Efficient Scheduling Method of Complex Batch Processes with General Network Structure via Agent-Based Modeling. AlChE J. 2013;59(8):28842906.Google Scholar
Chu, Y, You, F, Wassick, JM. Hybrid Method Integrating Agent-Based Modeling and Heuristic Tree Search for Scheduling of Complex Batch Processes. Comput Chem Eng. 2014;60:277296.Google Scholar
Castro, PM, Harjunkoski, I, Grossmann, IE. Optimal Short-Term Scheduling of Large-Scale Multistage Batch Plants. Ind Eng Chem Res. 2009;48(24):1100211016.CrossRefGoogle Scholar
Castro, PM, Harjunkoski, I, Grossmann, IE. Greedy Algorithm for Scheduling Batch Plants with Sequence-Dependent Changeovers. AlChE J. 2011;57(2):373387.Google Scholar
Kopanos, GM, Mendez, CA, Puigjaner, L. MIP-Based Decomposition Strategies for Large-Scale Scheduling Problems in Multiproduct Multistage Batch Plants: A Benchmark Scheduling Problem of the Pharmaceutical Industry. Eur J Oper Res. 2010;207(2):644655.Google Scholar

References

Velez, S, Sundaramoorthy, A, Maravelias, CT. Valid Inequalities Based on Demand Propagation for Chemical Production Scheduling MIP Models. AlChE J. 2013;59(3):872887.Google Scholar
Merchan, AF, Velez, S, Maravelias, CT. Tightening Methods for Continuous-Time Mixed-Integer Programming Models for Chemical Production Scheduling. AlChE J. 2013;59(12):44614467.CrossRefGoogle Scholar
Velez, S, Maravelias, CT. Mixed-Integer Programming Model and Tightening Methods for Scheduling in General Chemical Production Environments. Ind Eng Chem Res. 2013;52(9):34073423.Google Scholar
Merchan, AF, Maravelias, CT. Preprocessing and Tightening Methods for Time-Indexed MIP Chemical Production Scheduling Models. Comput Chem Eng. 2016;84:516535.Google Scholar
Burkard, RE, Hatzl, J. Review, Extensions and Computational Comparison of MILP Formulations for Scheduling of Batch Processes. Comput Chem Eng. 2005;29(8):17521769.Google Scholar
Janak, SL, Floudas, CA. Improving Unit-Specific Event Based Continuous-Time Approaches for Batch Processes: Integrality Gap and Task Splitting. Comput Chem Eng. 2008;32(4–5):913955.Google Scholar
Velez, S, Merchan, AF, Maravelias, CT. On the Solution of Large-Scale Mixed Integer Programming Scheduling Models. Chem Eng Sci. 2015;136:139157.Google Scholar
Chen, Y, Maravelias, CT. Preprocessing Algorithm and Tightening Constraints for Multiperiod Blend Scheduling: Cost MinimizationJournal of Global Optimization. 2020; 77, 603625.CrossRefGoogle Scholar
Ferris, MC, Maravelias, CT, Sundaramoorthy, A. Simultaneous Batching and Scheduling Using Dynamic Decomposition on a Grid. INFORMS Journal on Computing. 2009;21(3):398410.CrossRefGoogle Scholar
Velez, S, Maravelias, CT. A Branch-and-Bound Algorithm for the Solution of Chemical Production Scheduling MIP Models Using Parallel Computing. Comput Chem Eng. 2013;55(0):2839.Google Scholar
Velez, S, Maravelias, CT. Reformulations and Branching Methods for Mixed-Integer Programming Chemical Production Scheduling Models. Ind Eng Chem Res. 2013;52(10):38323841.Google Scholar
Merchan, AF, Maravelias, CT. Reformulations of Mixed-Integer Programming Continuous-Time Models for Chemical Production Scheduling. Ind Eng Chem Res. 2014;53(24):1015510165.Google Scholar
Velez, S, Maravelias, CT. Multiple and Nonuniform Time Grids in Discrete-Time MIP Models for Chemical Production Scheduling. Comput Chem Eng. 2013;53:7085.CrossRefGoogle Scholar
Velez, S, Maravelias, CT. Theoretical Framework for Formulating MIP Scheduling Models with Multiple and Non-Uniform Discrete-Time Grids. Comput Chem Eng. 2015;72:233254.Google Scholar
Merchan, AF, Lee, H, Maravelias, CT. Discrete-Time Mixed-Integer Programming Models and Solution Methods for Production Scheduling in Multistage Facilities. Comput Chem Eng. 2016;94:387410.CrossRefGoogle Scholar
Lee, H, Maravelias, CT. Combining the Advantages of Discrete- and Continuous-Time Scheduling Models: Part 1. Framework and Mathematical Formulations. Comput Chem Eng. 2018;116:176190.CrossRefGoogle Scholar
Lee, H, Maravelias, CT. Combining the Advantages of Discrete- and Continuous-Time Scheduling Models: Part 2. Systematic Methods for Determining Model Parameters. Comput Chem Eng. 2019; 128: 557573.Google Scholar
Lee, H, Maravelias, CT. Combining the Advantages of Discrete- and Continuous-time Scheduling Models. Part 3: General AlgorithmComput Chem Eng. 2020;139:106848.Google Scholar
Lee, H, Gupta, D, Maravelias, CT. Systematic Generation of Alternative Production Schedules. AlChE J. 2020: e16926.Google Scholar

References

Gupta, D, Maravelias, CT, Wassick, JM. From Rescheduling to Online Scheduling. Chem Eng Res Des. 2016;116:8397.Google Scholar
Gupta, D, Maravelias, CT. On Deterministic Online Scheduling: Major Considerations, Paradoxes and Remedies. Comput Chem Eng. 2016;94:312330.Google Scholar
Subramanian, K, Maravelias, CT, Rawlings, JB. A State-Space Model for Chemical Production Scheduling. Comput Chem Eng. 2012;47:97110.Google Scholar
Gupta, D, Maravelias, CT. A General State-Space Formulation for Online Scheduling. Processes. 2017;5(4):69.Google 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;128:201215.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,Google Scholar
Mignon, DJ, Honkomp, SJ, Reklaitis, GV. A Framework for Investigating Schedule Robustness under Uncertainty. Comput Chem Eng. 1995;19:615620.Google Scholar
Sanmartí, E, Espuña, A, Puigjaner, L. Effects of Equipment Failure Uncertainty in Batch Production Scheduling. Comput Chem Eng. 1995;19:565570.Google Scholar
Vin, JP, Ierapetritou, MG. Robust Short-Term Scheduling of Multiproduct Batch Plants under Demand Uncertainty. Ind Eng Chem Res. 2001;40(21):45434554.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):10691085.CrossRefGoogle 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):171195.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):15241534.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):687703.Google Scholar
Lappas, NH, Gounaris, CE. Multi-stage Adjustable Robust Optimization for Process Scheduling under Uncertainty. AlChE J. 2016;62(5):16461667.Google Scholar
Orçun, S, Kuban Altinel, İ, Hortaçsu, Ö. Scheduling of Batch Processes with Operational Uncertainties. Comput Chem Eng. 1996;20:S1191S1196.Google Scholar
Petkov, SB, Maranas, CD. Multiperiod Planning and Scheduling of Multiproduct Batch Plants under Demand Uncertainty. Ind Eng Chem Res. 1997;36(11):48644881.CrossRefGoogle 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):4157.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):36953713.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):741750.Google Scholar
Bonfill, A, Espuña, A, Puigjaner, L. Proactive Approach to Address the Uncertainty in Short-Term Scheduling. Comput Chem Eng. 2008;32(8):16891706.Google Scholar
Sand, G, Engell, S. Modeling and Solving Real-Time Scheduling Problems by Stochastic Integer Programming. Comput Chem Eng. 2004;28(6):10871103.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):101106.Google Scholar
J-h, Ryu, Dua, V, Pistikopoulos, EN. Proactive Scheduling under Uncertainty:  A Parametric Optimization Approach. Ind Eng Chem Res. 2007;46(24):80448049.Google Scholar
Li, Z, Ierapetritou, MG. Process Scheduling under Uncertainty Using Multiparametric Programming. AlChE J. 2007;53(12):31833203.Google Scholar
Li, Z, Ierapetritou, MG. Reactive Scheduling Using Parametric Programming. AlChE J. 2008;54(10):26102623.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):43664386.Google Scholar
Li, ZK, Ierapetritou, MG. Reactive Scheduling Using Parametric Programming. AlChE J. 2008;54(10):26102623.Google Scholar
Balasubramanian, J, Grossmann, IE. Scheduling Optimization under Uncertainty – an Alternative Approach. Comput Chem Eng. 2003;27(4):469490.Google 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):22732285.Google Scholar
Cott, BJ, Macchietto, S. Minimizing the Effects of Batch Process Variability Using Online Schedule Modification. Comput Chem Eng. 1989;13(1):105113.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.Google Scholar
Huercio, A, Espuña, A, Puigjaner, L. Incorporating On-Line Scheduling Strategies in Integrated Batch Production Control. Comput Chem Eng. 1995;19:609614.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:S1263S1268.Google Scholar
Ko, D, Moon, I. Rescheduling Algorithms in Case of Unit Failure for Batch Process Management. Comput Chem Eng. 1997;21:S1067S1072.Google 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):275291.Google Scholar
Henning, GP, Cerdá, J. Knowledge-Based Predictive and Reactive Scheduling in Industrial Environments. Comput Chem Eng. 2000;24(9):23152338.Google Scholar
Palombarini, J, Martínez, E. SmartGantt – an Interactive System for Generating and Updating Rescheduling Knowledge Using Relational Abstractions. Comput Chem Eng. 2012;47:202216.Google Scholar
Novas, JM, Henning, GP. Reactive Scheduling Framework Based on Domain Knowledge and Constraint Programming. Comput Chem Eng. 2010;34(12):21292148.Google Scholar
Elkamel, ALI, Mohindra, A. A Rolling Horizon Heuristic for Reactive Scheduling of Batch Process Operations. Engineering Optimization. 1999;31(6):763792.Google Scholar
Vin, JP, Ierapetritou, MG. A New Approach for Efficient Rescheduling of Multiproduct Batch Plants. Ind Eng Chem Res. 2000;39(11):42284238.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):361367.Google Scholar
Méndez, CA, Cerdá, J. Dynamic Scheduling in Multiproduct Batch Plants. Comput Chem Eng. 2003;27(8):12471259.CrossRefGoogle Scholar
Munawar, SA, Gudi, RD. A Multilevel, Control-Theoretic Framework for Integration of Planning, Scheduling, and Rescheduling. Ind Eng Chem Res. 2005;44(11):40014021.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):82538269.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):14091429.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):10761092.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):30783097.Google Scholar
Colvin, M, Maravelias, CT. A Stochastic Programming Approach for Clinical Trial Planning in New Drug Development. Comput Chem Eng. 2008;32(11):26262642.Google 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):964976.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):205215.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;78:258265.Google 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):21632179.Google Scholar
Flores-Tlacuahuac, A, Grossmann, IE. Simultaneous Cyclic Scheduling and Control of a Multiproduct CSTR. Ind Eng Chem Res. 2006;45(20):66986712.Google Scholar
Terrazas-Moreno, S, Flores-Tlacuahuac, A, Grossmann, IE. Simultaneous Cyclic Scheduling and Optimal Control of Polymerization Reactors. AlChE J. 2007;53(9):23012315.Google Scholar
Zhuge, J, Ierapetritou, MG. Integration of Scheduling and Control with Closed Loop Implementation. Ind Eng Chem Res. 2012;51(25):85508565.Google 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;47:248268.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):1467914694.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;79:5969.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):1711217123.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):43034315.Google Scholar
Engell, S, Harjunkoski, I. Optimal Operation: Scheduling, Advanced Control and Their Integration. Comput Chem Eng. 2012;47:121133.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):367372.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;114:211220.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):325334.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):639651.Google Scholar
Touretzky, CR, Harjunkoski, I, Baldea, M. Dynamic Models and Fault Diagnosis-Based Triggers for Closed-Loop Scheduling. AlChE J. 2017;63(6):19591973.Google 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):161193.Google Scholar
Blackburn, JD, Kropp, DH, Millen, RA. A Comparison of Strategies to Dampen Nervousness in MRP Systems. Manage Sci. 1986;32(4):413429.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):541558.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):114.Google Scholar
Kazan, O, Nagi, R, Rump, CM. New Lot-Sizing Formulations for Less Nervous Production Schedules. Computers & Operations Research. 2000;27(13):13251345.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):87858795.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):22142228.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

References

Maravelias, CT, Sung, C. Integration of Production Planning and Scheduling: Overview, Challenges and Opportunities. Comput Chem Eng. 2009;33(12):19191930.Google Scholar
Miller, AJ, Nemhauser, GL, Savelsbergh, MWP. A Multi-item Production Planning Model with Setup Times: Algorithms, Reformulations, and Polyhedral Characterizations for a Special Case. Math Program. 2003;95(1):7190.Google Scholar
Pochet, Y, Wolsey, LA. Production Planning by Mixed Integer Programming. New York; Berlin: Springer; 2006. xxiii, 499 p. p.Google Scholar
Sung, C, Maravelias, CT. A Mixed-Integer Programming Formulation for the General Capacitated Lot-Sizing Problem. Comput Chem Eng. 2008;32(1–2):244259.Google Scholar
Suerie, C, Stadtler, H. The Capacitated Lot-Sizing Problem with Linked Lot Sizes. Manage Sci. 2003;49(8):10391054.Google Scholar
Suerie, C. Modeling of Period Overlapping Setup Times. Eur J Oper Res. 2006;174(2):874886.Google Scholar
Kopanos, GM, Puigjaner, L, Maravelias, CT. Production Planning and Scheduling of Parallel Continuous Processes with Product Families. Ind Eng Chem Res. 2011;50(3):13691378.Google Scholar
Erdirik-Dogan, M, Grossmann, IE. A Decomposition Method for the Simultaneous Planning and Scheduling of Single-Stage Continuous Multiproduct Plants. Ind Eng Chem Res. 2006;45(1):299315.Google Scholar
Castro, PM, Erdirik-Dogan, M, Grossmann, IE. Simultaneous Batching and Scheduling of Single Stage Batch Plants with Parallel Units. AlChE J. 2008;54(1):183193.Google Scholar
Chen, P, Papageorgiou, LG, Pinto, JM. Medium-Term Planning of Single-Stage Single-Unit Multiproduct Plants Using a Hybrid Discrete/Continuous-Time MILP Model. Ind Eng Chem Res. 2008;47(6):19251934.Google Scholar
Liu, SS, Pinto, JM, Papageorgiou, LG. A TSP-Based MILP Model for Medium-Term Planning of Single-Stage Continuous Multiproduct Plants. Ind Eng Chem Res. 2008;47(20):77337743.Google Scholar
Sung, C, Maravelias, CT. An Attainable Region Approach for Production Planning of Multiproduct Processes. AlChE J. 2007;53(5):12981315.CrossRefGoogle Scholar
Sung, C, Maravelias, CT. A Projection-Based Method for Production Planning of Multiproduct Facilities. AlChE J. 2009;55(10):26142630.Google Scholar
Glasser, D, Crowe, C, Hildebrandt, D. A Geometric Approach to Steady Flow Reactors: The Attainable Region and Optimization in Concentration Space. Ind Eng Chem Res. 1987;26(9):18031810.Google Scholar
Hildebrandt, D, Glasser, D. The Attainable Region and Optimal Reactor Structures. Chem Eng Sci. 1990;45(8):21612168.Google Scholar
Benders, JF. Partitioning Procedures for Solving Mixed-Variables Programming Problems. Numerische Mathematik. 1962;4(1):238252.Google Scholar
Sahinidis, NV, Grossmann, IE. Reformulation of Multiperiod MILP Models for Planning and Scheduling of Chemical Processes. Comput Chem Eng. 1991;15(4):255272.Google Scholar
Karimi, IA, McDonald, CM. Planning and Scheduling of Parallel Semicontinuous Processes. 2. Short-Term Scheduling. Ind Eng Chem Res. 1997;36(7):27012714.Google Scholar
McDonald, CM, Karimi, IA. Planning and Scheduling of Parallel Semicontinuous Processes. 1. Production Planning. Ind Eng Chem Res. 1997;36(7):26912700.CrossRefGoogle Scholar
Shah, NK, Ierapetritou, MG. Integrated Production Planning and Scheduling Optimization of Multisite, Multiproduct Process Industry. Comput Chem Eng. 2012;37:214226.Google Scholar
Li, ZK, Ierapetritou, MG. Integrated Production Planning and Scheduling Using a Decomposition Framework. Chem Eng Sci. 2009;64(16):35853597.Google Scholar
Dias, LS, Ierapetritou, MG. Data-Driven Feasibility Analysis for the Integration of Planning and Scheduling Problems. Optimization and Engineering. 2019;20(4):10291066.Google Scholar
Brunaud, B, Amaran, S, Bury, S, Wassick, J, Grossmann, IE. Novel Approaches for the Integration of Planning and Scheduling. Ind Eng Chem Res. 2019;58(43):1997319984.Google Scholar

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  • Special Topics
  • Christos T. Maravelias, Princeton University, New Jersey
  • Book: Chemical Production Scheduling
  • Online publication: 01 May 2021
  • Chapter DOI: https://doi.org/10.1017/9781316650998.016
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  • Special Topics
  • Christos T. Maravelias, Princeton University, New Jersey
  • Book: Chemical Production Scheduling
  • Online publication: 01 May 2021
  • Chapter DOI: https://doi.org/10.1017/9781316650998.016
Available formats
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  • Special Topics
  • Christos T. Maravelias, Princeton University, New Jersey
  • Book: Chemical Production Scheduling
  • Online publication: 01 May 2021
  • Chapter DOI: https://doi.org/10.1017/9781316650998.016
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