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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Vydáno v:AIChE journal Ročník 71; číslo 7
Hlavní autoři: Ovalle, Daniel, Pulsipher, Joshua L., Ye, Yixin, Harshbarger, Kyle, Bury, Scott, Laird, Carl D., Grossmann, Ignacio E.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken, USA John Wiley & Sons, Inc 01.07.2025
American Institute of Chemical Engineers
Témata:
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
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Shrnutí: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.
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ISSN:0001-1541
1547-5905
DOI:10.1002/aic.18833