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M assively parallel distributed-memory multicomputers can achieve the high performance levels required to solve the Grand Challenge computational science problems (a class of computational applications, identified by the 1992 US Presidential Initiative in High-Performance Computing and Communications, that would require a significant increase in computing power). Multicomputers such as the Intel Paragon, the IBM SP-l/SP-2 (Scalable PowerParallel 1 and 2) and the Thinking Machines CM-5 (Connection Machine 5) offer significant cost and scalability advantages over shared-memory multiprocessors. However, to harness these machines' computational power, users must write efficient software. This process is laborious because of the absence of global address space. The programmer must manually distribute computations and data across processors and explicitly manage communication. The Paradigm (Parallelizing Compiler for Distributed-Memory, General-Purpose Multicomputers) project at the University of Illinois addresses this problem by developing automatic methods for efficient parallelization of sequential programs.

Airline planning consists of several problems that are currently solved separately. We address a partial integration of schedule planning, aircraft routing, and crew scheduling. In particular, we provide more flexibility for crew scheduling while maintaining the feasibility of aircraft routing by adding plane-count constraints to the crew-scheduling problem. In addition, we assume that the departure times of flights have not yet been fixed and we are allowed to move the departure time of a flight as long as it is within a given time window. We demonstrate that such a model yields solutions to the crew-scheduling problem with significantly lower costs than those obtained from the traditional model.

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