TY - JOUR
T1 - On the solution of large-scale mixed integer programming scheduling models
AU - Velez, Sara
AU - Merchan, Andres F.
AU - Maravelias, Christos T.
N1 - Funding Information:
The authors thank Yisu Nie, Lorenz T. Biegler, John Wassick, and Carlos Villa for providing the data for the Dow example (Section 4.3). The authors acknowledge financial support from the National Science Foundation under Grant CΒΕΤ-1066206 , and from the American Chemical Society—Petroleum Research Fund under Grant PRF-53313ND9 .
Funding Information:
The authors thank Yisu Nie, Lorenz T. Biegler, John Wassick, and Carlos Villa for providing the data for the Dow example (Section 4.3). The authors acknowledge financial support from the National Science Foundation under Grant C???-1066206, and from the American Chemical Society?Petroleum Research Fund under Grant PRF-53313ND9.
Publisher Copyright:
© 2015 Elsevier Ltd
PY - 2015/11/2
Y1 - 2015/11/2
N2 - In this paper, we show how four recently developed modeling and solution methods can be integrated to address mixed integer programs for the scheduling of large-scale chemical production systems. The first method uses multiple discrete time grids. The second adds tightening constraints that lower bound the total production and number of batches for each task and material based on the customer demand, while the third generates upper bounding constraints based on inventory and resource availability. The final method is a reformulation that introduces a new integer variable representing the total number of batches of a task. We apply the aforementioned methods to large-scale problems with a variety of processing features, including variable conversion coefficients, changeovers, various storage policies, continuous processing tasks, setups, and utilities, using a discrete-time model. We illustrate how these methods lead to significant improvements in computational performance.
AB - In this paper, we show how four recently developed modeling and solution methods can be integrated to address mixed integer programs for the scheduling of large-scale chemical production systems. The first method uses multiple discrete time grids. The second adds tightening constraints that lower bound the total production and number of batches for each task and material based on the customer demand, while the third generates upper bounding constraints based on inventory and resource availability. The final method is a reformulation that introduces a new integer variable representing the total number of batches of a task. We apply the aforementioned methods to large-scale problems with a variety of processing features, including variable conversion coefficients, changeovers, various storage policies, continuous processing tasks, setups, and utilities, using a discrete-time model. We illustrate how these methods lead to significant improvements in computational performance.
KW - Optimization
KW - Process operations
KW - Reformulations
KW - Solution methods
KW - Supply chain optimization
KW - Tightening constraints
UR - http://www.scopus.com/inward/record.url?scp=84930335068&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84930335068&partnerID=8YFLogxK
U2 - 10.1016/j.ces.2015.05.021
DO - 10.1016/j.ces.2015.05.021
M3 - Article
AN - SCOPUS:84930335068
SN - 0009-2509
VL - 136
SP - 139
EP - 157
JO - Chemical Engineering Science
JF - Chemical Engineering Science
ER -