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Application of Uncertain Variables to Knowledge-Based Resource Distribution

Application of Uncertain Variables to Knowledge-Based Resource Distribution

Donat Orski
Copyright: © 2012 |Pages: 23
ISBN13: 9781609608187|ISBN10: 1609608186|EISBN13: 9781609608194
DOI: 10.4018/978-1-60960-818-7.ch412
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MLA

Orski, Donat. "Application of Uncertain Variables to Knowledge-Based Resource Distribution." Machine Learning: Concepts, Methodologies, Tools and Applications, edited by Information Resources Management Association, IGI Global, 2012, pp. 928-950. https://doi.org/10.4018/978-1-60960-818-7.ch412

APA

Orski, D. (2012). Application of Uncertain Variables to Knowledge-Based Resource Distribution. In I. Management Association (Ed.), Machine Learning: Concepts, Methodologies, Tools and Applications (pp. 928-950). IGI Global. https://doi.org/10.4018/978-1-60960-818-7.ch412

Chicago

Orski, Donat. "Application of Uncertain Variables to Knowledge-Based Resource Distribution." In Machine Learning: Concepts, Methodologies, Tools and Applications, edited by Information Resources Management Association, 928-950. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-60960-818-7.ch412

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Abstract

The chapter concerns a class of systems composed of operations performed with the use of resources allocated to them. In such operation systems, each operation is characterized by its execution time depending on the amount of a resource allocated to the operation. The decision problem consists in distributing a limited amount of a resource among operations in an optimal way, that is, in finding an optimal resource allocation. Classical mathematical models of operation systems are widely used in computer supported projects or production management, allowing optimal decision making in deterministic, well-investigated environments. In the knowledge-based approach considered in this chapter, the execution time of each operation is described in a nondeterministic way, by an inequality containing an unknown parameter, and all the unknown parameters are assumed to be values of uncertain variables characterized by experts. Mathematical models comprising such two-level uncertainty are useful in designing knowledge-based decision support systems for uncertain environments. The purpose of this chapter is to present a review of problems and algorithms developed in recent years, and to show new results, possible extensions and challenges, thus providing a description of a state-of-the-art in the field of resource distribution based on the uncertain variables.

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