Date of Award
Doctor of Philosophy (PhD)
Dr. Scott J. Mason, Committee Chair
Dr. William G. Ferrell
Dr. Mary E. Kurz
Dr. Amin Khademi
Companies that are considering an acquisition are generally concerned with combining businesses successfully. Acquisitions are strategic decisions, aimed at increasing the current operations as well as posi-tioning themselves for future success. The aim of this dissertation research reﬂects that ambition: providing decision support models and methodologies for making network design decisions that enable successful current and future supply chain operations. These businesses operate in an uncertain world, where de-cisions regarding supply chain network design must be made despite the possibility of unforeseen future events that may disrupt or damage the supply chain. In an effort to aid decision makers in designing sup-ply chain networks that can operate well in the uncertain future, we offer both a deterministic and a robust optimization model that consider both cost and network connectivity as objective functions. Decision mak-ers are able to evaluate several solutions with different cost and connectivity values, choosing the levels that best serve the needs of their newly merged company. The proposed robust optimization model also allows decision makers to control the conservativeness of the model while making good decisions despite uncertain or incomplete data, which is not uncommon in acquisition scenarios.
Magagnotti, Mariah S., "Modeling Supply Chain Acquisitions" (2014). All Dissertations. 1770.