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Coping with Incomplete Information in Scheduling — Stochastic and Online Models

In: Operations Research Proceedings 2007

Author

Listed:
  • Nicole Megow

    (Technische Universität)

Abstract

Incomplete information is an omnipresent issue when dealing with real-world optimization problems. Typically, such limitations concern the uncertainty of given data or the complete lack of knowledge about future parts of a problem instance. Our work is devoted to investigations on how to cope with incomplete information when solving scheduling problems. The particular problem class we consider is the class of machine scheduling problems which plays an important role within combinatorial optimization. These problems involve the temporal allocation of limited resources (machines) for executing activities so as to optimize some objective. Scheduling problems are apparent in many applications including, for example, manufacturing and service industries but also compiler optimization and parallel computing.

Suggested Citation

  • Nicole Megow, 2008. "Coping with Incomplete Information in Scheduling — Stochastic and Online Models," Operations Research Proceedings, in: Jörg Kalcsics & Stefan Nickel (ed.), Operations Research Proceedings 2007, pages 17-22, Springer.
    • Handle: RePEc:spr:oprchp:978-3-540-77903-2_3
    DOI: 10.1007/978-3-540-77903-2_3
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