Document Type



Doctor of Philosophy


Business Administration

Date of Defense


Graduate Advisor

Haitao Li, PhD


Lisa Dorner

James F. Campbell, Ph.D.

Robert M. Nauss, Ph.D.

Donald C. Sweeney II, Ph.D.


This dissertation studies strategic capacity planning and resource acquisition decisions, including the facility location problem and the technology choice problem. These decisions are modeled in an integrative manner, and the main purpose of the proposed models and numerical experiments is to examine the effects of economies of scale, economies of scope, and the combined effects of scale and scope under uncertain demand realizations using robust optimization. The type of capacities, or technology alternatives, that a firm can acquire can be classified on two basic dimensions. The first dimension relates to the effects of scale via distinction between labor-intensive (less automated) technologies and capital-intensive (more automated) technologies. The second dimension relates to the effects of scope via distinction between product-dedicated and flexible technologies. Moreover, each of the product-dedicated and flexible technologies can have different levels of labor or capital-intensiveness, leading to the joint effects of economies of scale and economies of scope. Each of the technology alternatives possesses certain cost structures. Labor-intensive technologies are characterized by low fixed costs and high variable costs, whereas capital-intensive technologies are characterized by just the opposite cost structure, i.e., high fixed costs and low variable costs. Flexible technologies cost more than product-dedicated technologies, both in terms of fixed and variable costs. Robust optimization methodology is used to investigate how different levels of robustness, and facility and technology costs affect the quantities, types and allocation of technologies to facilities. Results show that specific technology choice patterns emerge depending on various cost structures and different levels of model robustness specified to accommodate uncertain demand realizations. The results obtained by the two-stage robust optimization approach are compared to the results obtained by a non-robust approach and a stochastic programming approach.

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