Designing a supply chain resilient to major disruptions and supply/demand interruptions

Global supply chains are more than ever under threat of major disruptions caused by devastating natural and man-made disasters as well as recurrent interruptions caused by variations in supply and demand. This paper presents a hybrid robust-stochastic optimization model and a Lagrangian relaxation solution method for designing a supply chain resilient to (1) supply/demand interruptions and (2) facility disruptions whose risk of occurrence and magnitude of impact can be mitigated through fortification investments. The authors study a realistic problem where a disruption can cause either a complete facility shutdown or a reduced supply capacity. The probability of disruption occurrence is expressed as a function of facility fortification investment for hedging against potential disruptions in the presence of certain budgetary constraints. Computational experiments and thorough sensitivity analyses are completed using some of the existing widely-used datasets. The performance of the proposed model is also examined using a Monte Carlo simulation method. To explore the practical application of the proposed model and methodology, a real world case example is discussed which addresses mitigating the risk of facility fires in an actual oil production company. The authors' analysis and investigation focuses on exploring the extent to which supply chain design decisions are influenced by factors such as facility fortification strategies, a decision maker's conservatism degree, demand fluctuations, supply capacity variations, and budgetary constraints.

• Record URL:
  https://doi.org/10.1016/j.trb.2016.09.004
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0191261516306567
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01912615
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Jabbarzadeh, Armin
  • Fahimnia, Behnam
  • Sheu, Jiuh-Biing
  • Moghadam, Hani Shahmoradi
• Publication Date: 2016-12

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 121-149
• Serial:
  • Transportation Research Part B: Methodological
  • Volume: 94
  • Publisher: Elsevier
  • ISSN: 0191-2615
  • Serial URL: http://www.sciencedirect.com/science/journal/01912615

Subject/Index Terms

• TRT Terms: Monte Carlo method; Optimization; Reliability; Risk management; Service disruption; Stochastic processes; Supply chain management; Uncertainty
• Uncontrolled Terms: Resilience (Adaptability)
• Subject Areas: Freight Transportation; Planning and Forecasting;

Filing Info

• Accession Number: 01619170
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 21 2016 11:29AM