Our paper, "A bi-criteria indicator to assess supply chain network performance for critical needs under capacity and demand disruptions," Patrick Qiang and Anna Nagurney, appears in this issue, which is volume 46 (5), June 2012.
This paper was also highlighted in a newsletter of the OR Society (thanks). Patrick is a faculty member at the Graduate School of Professional Studies at Penn State University Malvern and I am at the Isenberg School at UMass Amherst.
In this paper, we developed a supply chain network model for critical needs products, which captures disruptions in capacities associated with the various supply chain activities of production, transportation, and storage, as well as those associated with the demands for the product at the various demand points. Critical needs products may be defined as those products and supplies that are essential to human health and life. Examples include food, water, medicines, and vaccines. The demand for critical needs is always present and, hence, the disruption to the production, storage, transportation/distribution, and ultimate delivery of such products can result not only in discomfort and human suffering but also in loss of life. Such supplies are essential in times of disasters and emergencies.
The objective is to minimize the total network costs, which are generalized costs that may include the monetary, risk, time, and social costs. Two different cases of disruption scenarios are considered. In the first case, we assume that the impacts of the disruptions are mild and that the demands can be met. In the second case, the demands cannot all be satisfied. For these two cases, we propose two individual performance indicators.
We showed that the governing optimality conditions can be formulated as a variational inequality problem with nice features for numerical solution.
In addition, we proposed two distinct supply chain network performance indicators for critical needs products. The first indicator considers disruptions in the link capacities but assumes that the demands for the product can be met. The second indicator captures the unsatisfied demand. We then constructed a bi-criteria supply chain network performance indicator and used it for the evaluation of distinct supply chain networks. The bi-criteria indicator allows for the comparison of the robustness of different supply chain networks under a spectrum of real-world scenarios. We illustrated the new concepts in the paper with numerical supply chain network examples in which the supply chains were subject to a spectrum of disruptions involving capacity reductions as well as demand changes.
Given that the number of disasters has been growing globally, we expect that the methodological tools introduced in this paper will be applicable in practice in disaster planning and emergency preparedness.
Thanks also to Wiley for publicizing our first critical needs supply chain network paper, which focused on design, in its Asia blog on What's New in Operations Research.
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