The NetSci2010 Conference will be taking place next week at the Media Lab at MIT, which is a gorgeous venue. I will be delivering the paper: Supply Chain Network Design for Critical Needs with Outsourcing, which is joint work with Dr. Patrick Qiang and my doctoral student, Min Yu.
The abstract of the paper is below:
Abstract: In this paper we consider the design of supply chain networks in the case of critical needs as may occur, for example, in disasters, emergencies, pending epidemics, and attacks affecting national security. By "critical needs" we mean products that are essential to the survival of the population, which can include, for example, vaccines, medicine, food, water, etc., depending upon the particular application. "Critical" implies that the demand for the product should be met as nearly as possible since otherwise there may be additional loss of life.
The model that we develop captures a single organization, such as the government or a major health organization or corporation that seeks to "produce" the product at several possible manufacturing plants, have it stored, if need be, and distributed to the demand points. We assume that the organization is aware of the total costs associated with the various operational supply chain network activities, knows the existing capacities of the links, and is interested in identifying the additional capacity outlays, the production amounts, and shipment values so that the demand is satisfied with associated penalties if the demand is not met (as well as penalties with oversupply, which are expected to be lower). In addition, the organization has the option of outsourcing the production/storage/delivery of the critical product at a fixed/negotiated price and with the capacities of those entities being fixed and known. The solution of the model provides the optimal capacity enhancements and volumes of product flows so as to minimize the total cost, which we assume to be a generalized cost, and can include time, subject to the demands being satisfied, as nearly as possible, under demand uncertainty.