| Supply chain design models have traditionally treated the world as if we knew everything about it with certainty. In reality, however, parameter estimates may be inaccurate due to poor forecasts, measurement errors, changing demand patterns, or other factors. Moreover, even if all of the parameters of the supply chain are known with certainty, the system may face disruptions from time to time, for example, due to inclement weather, labor actions, or sabotage. This dissertation studies models for designing supply chains that are robust (i.e., perform well with respect to uncertainties in the data, such as demand) and reliable (i.e., perform well when parts of the system fail).; The first half of this dissertation is concerned with models for robust supply chain design. The first of these models minimizes the expected systemwide cost, including costs for facility location, transportation, and inventory. The second model adds a constraint that restricts the regret in any scenario to be within a pre-specified limit. Both models are solved using Lagrangian relaxation. The second model presents an additional challenge since feasible solutions cannot always be found easily, and it may even be difficult to determine whether a given problem is feasible. We present strategies for overcoming these difficulties. We also discuss regret-constrained versions of two classical facility location problems and suggest algorithms for these problems based on variable-splitting. The algorithms presented here can be used (heuristically) to solve minimax-regret versions of the corresponding problems.; In the second half of the dissertation, we present a new approach to supply chain optimization that attempts to choose facility locations so that if a distribution center becomes unavailable, the resulting cost of operating the system (called the "failure cost") is not excessive. We discuss two types of reliability models, one that considers the maximum failure cost and one that considers the expected failure cost. We propose several formulations of the maximum failure cost problem and discuss relaxations for them. We also present a tabu search heuristic for solving these problems. The expected failure cost problem is solved using Lagrangian relaxation. Computational results from both models demonstrate empirically that large improvements in reliability are often possible with small increases in cost. |
