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
13 - Solution Methods: Network 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
We discuss four solution methods for problems in general network production environments. After presenting some background and motivation, in Section 13.1, we cover (1) preprocessing and tightening methods, in Section 13.2; (2) reformulations, in Section 13.3; (3) an approach to formulate models that employ multiple discrete time grids, in Section 13.4; and (4) a three-stage algorithm that employs both a discrete and continuous time models, in Section 13.5. For simplicity, we do not consider shared utilities nor special processing features such as storage in processing units and multiple material transfers. The methods presented in Section 13.2 and Section 13.3 are applicable to both discrete and continuous time models, but to keep the presentation short, we apply them to discrete time models, though we comment on their application to their continuous counterparts. The reader can study each section, after Section 13.1, independently, that is, Section 13.2 is not prerequisite for Section 13.3, and so on.
Keywords
- Type
- Chapter
- Information
- Chemical Production SchedulingMixed-Integer Programming Models and Methods, pp. 318 - 360Publisher: Cambridge University PressPrint publication year: 2021
References
Velez, S, Sundaramoorthy, A, Maravelias, CT. Valid Inequalities Based on Demand Propagation for Chemical Production Scheduling MIP Models. AlChE J. 2013;59(3):872–887.CrossRefGoogle 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):4461–4467.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):3407–3423.CrossRefGoogle Scholar
Merchan, AF, Maravelias, CT. Preprocessing and Tightening Methods for Time-Indexed MIP Chemical Production Scheduling Models. Comput Chem Eng. 2016;84:516–535.CrossRefGoogle Scholar
Burkard, RE, Hatzl, J. Review, Extensions and Computational Comparison of MILP Formulations for Scheduling of Batch Processes. Comput Chem Eng. 2005;29(8):1752–1769.CrossRefGoogle 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):913–955.CrossRefGoogle Scholar
Velez, S, Merchan, AF, Maravelias, CT. On the Solution of Large-Scale Mixed Integer Programming Scheduling Models. Chem Eng Sci. 2015;136:139–157.CrossRefGoogle Scholar
Chen, Y, Maravelias, CT. Preprocessing Algorithm and Tightening Constraints for Multiperiod Blend Scheduling: Cost Minimization. Journal of Global Optimization. 2020; 77, 603–625.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):398–410.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):28–39.CrossRefGoogle Scholar
Velez, S, Maravelias, CT. Reformulations and Branching Methods for Mixed-Integer Programming Chemical Production Scheduling Models. Ind Eng Chem Res. 2013;52(10):3832–3841.CrossRefGoogle Scholar
Merchan, AF, Maravelias, CT. Reformulations of Mixed-Integer Programming Continuous-Time Models for Chemical Production Scheduling. Ind Eng Chem Res. 2014;53(24):10155–10165.CrossRefGoogle Scholar
Velez, S, Maravelias, CT. Multiple and Nonuniform Time Grids in Discrete-Time MIP Models for Chemical Production Scheduling. Comput Chem Eng. 2013;53:70–85.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;72:233–254.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
Lee, H, Maravelias, CT. Combining the Advantages of Discrete- and Continuous-Time Scheduling Models: Part 1. Framework and Mathematical Formulations. Comput Chem Eng. 2018;116:176–190.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: 557–573.CrossRefGoogle Scholar
Lee, H, Maravelias, CT. Combining the Advantages of Discrete- and Continuous-time Scheduling Models. Part 3: General Algorithm. Comput Chem Eng. 2020;139:106848.CrossRefGoogle Scholar
Lee, H, Gupta, D, Maravelias, CT. Systematic Generation of Alternative Production Schedules. AlChE J. 2020: e16926.CrossRefGoogle Scholar