4.7 Article

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

Journal

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
Volume 94, Issue -, Pages 121-149

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trb.2016.09.004

Keywords

Supply chain network design; Risk; Disruption; Interruption; Uncertainty; Reliability; Robust optimization; Stochastic optimization; Monte Carlo simulation

Funding

  1. Ministry of Science and Technology of Taiwan, R.O.C. [NSC 102-2410-H-002-166-MY3, MOST 104-2410-H-002-137-MY3]

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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. We 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. Our 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. (C) 2016 Elsevier Ltd. All rights reserved.

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