Coping with Incomplete Information in Scheduling — Stochastic and Online Models

Nicole Megow ()
Additional contact information
Nicole Megow: Technische Universität

A chapter in Operations Research Proceedings 2007, 2008, pp 17-22 from Springer

Abstract: 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.

Keywords: Schedule Problem; Incomplete Information; Parallel Machine; Competitive Ratio; Online Algorithm (search for similar items in EconPapers)
Date: 2008
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-540-77903-2_3

Ordering information: This item can be ordered from
http://www.springer.com/9783540779032

DOI: 10.1007/978-3-540-77903-2_3

Access Statistics for this chapter

More chapters in Operations Research Proceedings from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().