Integration of Plant Scheduling Feasibility with Supply Chain Network Under Disruptions Using Machine Learning Surrogates

Integrating supply chain under disruptions with plant scheduling is challenging due to differing time scales. Our proposed approach utilizes a reactive supply chain model with linear model decision tree surrogates, capturing feasibility within scheduling constraints. The surrogate employs an aggrega...

Full description

Saved in:
Bibliographic Details
Published inComputer Aided Chemical Engineering Vol. 53; pp. 1489 - 1494
Main Authors Ovalle, Daniel, Vyas, Javal, Laird, Carl D., Grossmann, Ignacio E.
Format Book Chapter
LanguageEnglish
Published 2024
Subjects
Feasibility
Machine learning surrogates
Supply chain under disruptions
Supply chain under disruptions
Machine learning surrogates
Feasibility
Online AccessGet full text

Cover

More Information
Summary:Integrating supply chain under disruptions with plant scheduling is challenging due to differing time scales. Our proposed approach utilizes a reactive supply chain model with linear model decision tree surrogates, capturing feasibility within scheduling constraints. The surrogate employs an aggregated variable space and an efficient sampling methodology, as demonstrated in a case study. Results highlight the integrated model’s ability to ensure entirely feasible operations without compromising overall profitability within tractable solution time.
ISBN:9780443288241
0443288240
ISSN:1570-7946
DOI:10.1016/B978-0-443-28824-1.50249-0