Degree Granting Department
Industrial and Management Systems Engineering
Alex Savachkin, Ph.D.
Kingsley Reeves, Ph.D.
Bo Zeng, Ph.D.
Kaushal Chari, Ph.D.
Alex Volinsky, Ph.D.
Lagrangian relaxation, disruption, fortification, heuristics, nonlinear
Lean distribution networks have been facing an increased exposure to the risk of unpredicted disruptions causing significant economic forfeitures. At the same time, the existing literature contains very few studies that examine the impact of fortification of facilities for improving network reliability. This dissertation presents three related classes of models that support the design of reliable distribution networks. The models extend the uncapacitated P-median and fixed-charge location models by considering heterogeneous facility failure probabilities, supplier backups, and facility fortification within a finite budget. The first class of models considers binary fortification via linear fortification functions. The second class of models extends binary fortification to partial (continuous) reliability improvement with linear fortification. This extension allows a more efficient utilization of limited fortification resources. The third class of models generalizes linear fortification to nonlinear to reflect the effect of diminishing marginal reliability improvement from fortification investment. For each of the models, we develop solution algorithms and demonstrate their computational efficiency. We present a detailed discussion on the novelty of the proposed models. The models are intended to support corporate decisions on the design of robust distribution networks using limited fortification resources.
Scholar Commons Citation
Li, Qingwei, "Decision Support Models for Design of Fortified Distribution Networks" (2011). Graduate Theses and Dissertations.