TY - JOUR
T1 - Combining the advantages of discrete- and continuous-time scheduling models
T2 - Part 3. General algorithm
AU - Lee, Hojae
AU - Maravelias, Christos T.
N1 - Funding Information:
H. Lee acknowledges support from the Kwanjeong Educational Foundation, South Korea. We would also like to thank Dr. Cabada Amaya from Heineken Mexico for sharing valuable insights and data on their brewing process.
Publisher Copyright:
© 2020
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/8/4
Y1 - 2020/8/4
N2 - One of the main challenges in applying optimization-based scheduling techniques in process industries stems from the different process characteristics and constraints that need to be taken into account when generating a schedule. For instance, consideration of sequence-dependent changeovers may easily make the resulting optimization model computationally expensive. Accordingly, building upon the recently proposed Discrete-Continuous Algorithm (Lee and Maravelias, 2018), we propose generalized algorithm that enables accurate and fast solution of difficult instances while efficiently handling a wide range of process characteristics. The algorithm combines modeling versatility with computational tractability while guaranteeing the feasibility and accuracy of the final solution. Through a case study inspired by a real-world brewing process, we show that our algorithm provides accurate and high quality solutions to industrial-scale instances in reasonable time.
AB - One of the main challenges in applying optimization-based scheduling techniques in process industries stems from the different process characteristics and constraints that need to be taken into account when generating a schedule. For instance, consideration of sequence-dependent changeovers may easily make the resulting optimization model computationally expensive. Accordingly, building upon the recently proposed Discrete-Continuous Algorithm (Lee and Maravelias, 2018), we propose generalized algorithm that enables accurate and fast solution of difficult instances while efficiently handling a wide range of process characteristics. The algorithm combines modeling versatility with computational tractability while guaranteeing the feasibility and accuracy of the final solution. Through a case study inspired by a real-world brewing process, we show that our algorithm provides accurate and high quality solutions to industrial-scale instances in reasonable time.
KW - Chemical production scheduling
KW - Mixed-integer programming
KW - Process operations
KW - Solution algorithm
UR - http://www.scopus.com/inward/record.url?scp=85085199384&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085199384&partnerID=8YFLogxK
U2 - 10.1016/j.compchemeng.2020.106848
DO - 10.1016/j.compchemeng.2020.106848
M3 - Article
AN - SCOPUS:85085199384
SN - 0098-1354
VL - 139
JO - Computers and Chemical Engineering
JF - Computers and Chemical Engineering
M1 - 106848
ER -