Book contents
- Chemical Production Scheduling
- Cambridge Series in Chemical Engineering
- Chemical Production Scheduling
- Copyright page
- Dedication
- Contents
- Preface
- Part I Background
- Part II Basic Methods
- Part III Advanced Methods
- Part IV Special Topics
- 12 Solution Methods: Sequential Environments
- 13 Solution Methods: Network Environments
- 14 Real-Time Scheduling
- 15 Integration of Production Planning and Scheduling
- Index
- References
12 - Solution Methods: Sequential Environments
from Part IV - Special Topics
Published online by Cambridge University Press: 01 May 2021
Book contents
- Chemical Production Scheduling
- Cambridge Series in Chemical Engineering
- Chemical Production Scheduling
- Copyright page
- Dedication
- Contents
- Preface
- Part I Background
- Part II Basic Methods
- Part III Advanced Methods
- Part IV Special Topics
- 12 Solution Methods: Sequential Environments
- 13 Solution Methods: Network Environments
- 14 Real-Time Scheduling
- 15 Integration of Production Planning and Scheduling
- Index
- References
Summary
The goal of the present chapter, as well as Chapter 13, is to illustrate how problem features can be exploited to develop more efficient models and/or specialized algorithms. We start, in the present chapter, with solution methods for problems in sequential environments. Specifically, we discuss four methods: (1) a decomposition approach, in Section 12.1; (2) preprocessing algorithms and tightening constraints, in Section 12.2; (3) a reformulation and tightening constraints based on time windows, in Section 12.3; and (4) a two-step algorithm, combining the advantages of discrete and continuous time models, in Section 12.4. While all presented methods can be applied to a wide range of problems, we present them for a subset of problems for the sake of brevity. Also, all methods can be applied to problems under different processing features, but to keep the presentation simple, we discuss problems with no shared utilities and no storage constraints.
Keywords
- Type
- Chapter
- Information
- Chemical Production SchedulingMixed-Integer Programming Models and Methods, pp. 289 - 317Publisher: Cambridge University PressPrint publication year: 2021
References
Maravelias, CT. A Decomposition Framework for the Scheduling of Single- and Multi-stage Processes. Comput Chem Eng. 2006;30(3):407–420.CrossRefGoogle Scholar
Prasad, P, Maravelias, CT. Batch Selection, Assignment and Sequencing in Multi-stage Multi-product Processes. Comput Chem Eng. 2008;32(6):1106–1119.CrossRefGoogle Scholar
Sundaramoorthy, A, Maravelias, CT. Simultaneous Batching and Scheduling in Multistage Multiproduct Processes. Ind Eng Chem Res. 2008;47(5):1546–1555.CrossRefGoogle 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):6210–6226.CrossRefGoogle 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;94:387–410.CrossRefGoogle 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;2461:75–87.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):2777–2786.Google Scholar
Jain, V, Grossmann, IE. Algorithms for Hybrid MILP/CP Models for a Class of Optimization Problems. INFORMS Journal on Computing. 2001;13(4):258–276.CrossRefGoogle Scholar
Hooker, JN. Planning and Scheduling by Logic-Based Benders Decomposition. Oper Res. 2007;55(3):588–602.CrossRefGoogle 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):209–217.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.CrossRefGoogle Scholar
Roe, B, Papageorgiou, LG, Shah, N. A Hybrid MILP/CLP Algorithm for Multipurpose Batch Process Scheduling. Comput Chem Eng. 2005;29(6):1277–1291.Google Scholar
Balas, E, Lancia, G, Serafini, P, Vazacopoulos, A. Job Shop Scheduling with Deadlines. J Comb Optim. 1998;1(4):329–353.Google Scholar
Balas, E, Vazacopoulos, A. Guided Local Search with Shifting Bottleneck for Job Shop Scheduling. Manage Sci. 1998;44(2):262–275.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):10665–10680.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):2884–2906.CrossRefGoogle 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;60:277–296.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):11002–11016.CrossRefGoogle Scholar
Castro, PM, Harjunkoski, I, Grossmann, IE. Greedy Algorithm for Scheduling Batch Plants with Sequence-Dependent Changeovers. AlChE J. 2011;57(2):373–387.CrossRefGoogle 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):644–655.CrossRefGoogle Scholar