Optimal reactive operation of general topology supply chain and manufacturing networks under disruptions

Supply and manufacturing networks in the chemical industry involve diverse processing steps across different locations, rendering their operation vulnerable to disruptions from unplanned events. Optimal responses should consider factors such as product allocation, delayed shipments, and price renego...

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Published in:	AIChE journal Vol. 71; no. 7
Main Authors:	Ovalle, Daniel, Pulsipher, Joshua L., Ye, Yixin, Harshbarger, Kyle, Bury, Scott, Laird, Carl D., Grossmann, Ignacio E.
Format:	Journal Article
Language:	English
Published:	Hoboken, USA John Wiley & Sons, Inc 01.07.2025 American Institute of Chemical Engineers
Subjects:	Chemical industry Disruption disruptions Integer programming Linear programming Manufacturing mixed‐integer linear programming Networks operation scheduling Production scheduling Shipments supply chain optimization Supply chains Topology
ISSN:	0001-1541, 1547-5905
Online Access:	Get full text
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Abstract	Supply and manufacturing networks in the chemical industry involve diverse processing steps across different locations, rendering their operation vulnerable to disruptions from unplanned events. Optimal responses should consider factors such as product allocation, delayed shipments, and price renegotiation, among other factors. In such context, we propose a multiperiod mixed‐integer linear programming model that integrates production, scheduling, shipping, and order management to minimize the financial impact of such disruptions. The model accommodates arbitrary supply chain topologies and incorporates various disruption scenarios, offering adaptability to real‐world complexities. A case study from the chemical industry demonstrates the scalability of the model under finer time discretization and explores the influence of disruption types and order management costs on optimal schedules. This approach provides a tractable, adaptable framework for developing responsive operational plans in supply chain and manufacturing networks under uncertainty.
AbstractList	Supply and manufacturing networks in the chemical industry involve diverse processing steps across different locations, rendering their operation vulnerable to disruptions from unplanned events. Optimal responses should consider factors such as product allocation, delayed shipments, and price renegotiation, among other factors. In such context, we propose a multiperiod mixed‐integer linear programming model that integrates production, scheduling, shipping, and order management to minimize the financial impact of such disruptions. The model accommodates arbitrary supply chain topologies and incorporates various disruption scenarios, offering adaptability to real‐world complexities. A case study from the chemical industry demonstrates the scalability of the model under finer time discretization and explores the influence of disruption types and order management costs on optimal schedules. This approach provides a tractable, adaptable framework for developing responsive operational plans in supply chain and manufacturing networks under uncertainty.
Author	Laird, Carl D. Grossmann, Ignacio E. Ovalle, Daniel Harshbarger, Kyle Pulsipher, Joshua L. Bury, Scott Ye, Yixin
Author_xml	– sequence: 1 givenname: Daniel orcidid: 0000-0002-9337-521X surname: Ovalle fullname: Ovalle, Daniel organization: Carnegie Mellon University – sequence: 2 givenname: Joshua L. surname: Pulsipher fullname: Pulsipher, Joshua L. organization: University of Waterloo – sequence: 3 givenname: Yixin surname: Ye fullname: Ye, Yixin organization: Core R&D – sequence: 4 givenname: Kyle surname: Harshbarger fullname: Harshbarger, Kyle organization: Supply Chain Innovation – sequence: 5 givenname: Scott surname: Bury fullname: Bury, Scott organization: Core R&D – sequence: 6 givenname: Carl D. surname: Laird fullname: Laird, Carl D. organization: Carnegie Mellon University – sequence: 7 givenname: Ignacio E. surname: Grossmann fullname: Grossmann, Ignacio E. email: grossmann@cmu.edu organization: Carnegie Mellon University
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Snippet	Supply and manufacturing networks in the chemical industry involve diverse processing steps across different locations, rendering their operation vulnerable to...
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SubjectTerms	Chemical industry Disruption disruptions Integer programming Linear programming Manufacturing mixed‐integer linear programming Networks operation scheduling Production scheduling Shipments supply chain optimization Supply chains Topology
Title	Optimal reactive operation of general topology supply chain and manufacturing networks under disruptions
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