Supply Chain Disruptions is an interface with a multiperiod mixed-integer linear programming model that integrates production, scheduling, shipping, and order management to minimize the financial impact of some disruptions from unplanned events. Optimal responses should consider factors such as product allocation, delayed shipments, and price renegotiation , among other factors. 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.
Supply Chain Disruptions is based on the model developed by Daniel Ovalle, Joshua L. Pulsipher, Yixin Ye, Kyle Harshbarger, Scott Bury, Carl D. Laird, and Ignacio E. Grossmann, "Optimal Reactive Operation of General Topology Supply Chain and Manufacturing Networks under Disruptions", AIChE Journal. Volume 71, Issue 7, July 2025; e18833 ; and has been developed by Rosanna Franco under the supervision of Ignacio E. Grossmann.
Comparison of a traditional feed forward multi-echelon topology an a potential arbitrary topology with similar number of components.
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