This collaborative research project addresses a new large-scale optimization model, called Network Design with Service Guarantees (NDSG), that simultaneously incorporates revenue, cost, and service (end-to-end delay) considerations. Though of immense practical significance, the NDSG model is very difficult to solve both from theoretical and computational perspectives. The goal is to develop and test effective solution procedures using state-of-the-art optimization techniques that can exploit the NDSG problem's special mathematical structure. Accordingly, the project entails both theoretical and empirical investigations, including developing and characterizing alternative problem formulations, analyzing their structural properties, designing specialized solution algorithms, and testing these methods using realistic data. To solve the problem, a new family of optimization algorithms that dynamically reformulates the problem, and correspondingly adapts the solution approach by combining decomposition and model strengthening techniques will be investigated. It is expected that the core research contributions-a new modeling paradigm, a novel solution approach, and algorithmic implementation-will also extend to other large-scale optimization models. Communication and physical distribution capabilities have become critical in the new networked economy. Organizations have come to rely on these capabilities to establish tightly integrated partnerships that can effectively serve diverse market needs for products and services. Delays or failures in the movement of information or goods can have debilitating consequences not only for supply chains but also for services such as emergency operations, air traffic control, and financial systems. Decisions regarding the configuration of communication and distribution networks needed to support coordination and collaboration have strategic importance both because they entail massive investments of billions of dollars and because a network's topological design largely determines the level of service it can provide. Consequently, optimization tools are widely used in practice to design product and service fulfillment networks, and network design continues to be a very active research area.   Designing optimal network configurations entails complex tradeoffs between conflicting objectives such as maximizing profitability, ensuring adequate resource utilization, improving service levels, and so on. Classical optimization models, which focus primarily on cost minimization, tend to design sparse networks that exploit economies of scale but are not robust in terms of service and reliability. The project will provide opportunities for graduate students to conduct thesis research, and enrich graduate courses in optimization and operations modeling.