DocumentCode
1167738
Title
RAPS: a rule-based language for specifying resource allocation and time-tabling problems
Author
Solotorevsky, Gadi ; Gudes, Ehud ; Meisels, Amnon
Author_Institution
Dept. of Math. & Comput. Sci., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel
Volume
6
Issue
5
fYear
1994
fDate
10/1/1994 12:00:00 AM
Firstpage
681
Lastpage
697
Abstract
A general language for specifying resource allocation and time-tabling problems is presented. The language is based on an expert system paradigm that was developed previously by the authors and that enables the solution of resource allocation problems by using experts´ knowledge and heuristics. The language enables the specification of a problem in terms of resources, activities, allocation rules, and constraints, and thus provides a convenient knowledge acquisition tool. The language syntax is powerful and allows the specification of rules and constraints that are very difficult to formulate with traditional approaches, and it also supports the specification of various control and backtracking strategies. We constructed a generalized inference engine that runs compiled resource allocation problem specification language (RAPS) programs and provides all necessary control structures. This engine acts as an expert system shell and is called expert system for resource allocation (ESRA). The performance of RAPS combined with ESRA is demonstrated by analyzing its solution of a typical resource allocation problem
Keywords
expert systems; formal specification; inference mechanisms; knowledge acquisition; resource allocation; scheduling; specification languages; ESRA; Prolog; RAPS; allocation rules; backtracking; compiled resource allocation problem specification; expert system paradigm; generalized inference engine; heuristics; knowledge acquisition tool; language syntax; resource allocation problems; resource allocation specification; rule specification; rule-based language; specification language; time-tabling problems; Educational institutions; Engines; Expert systems; Humans; Job shop scheduling; Knowledge acquisition; Performance analysis; Production; Resource management; Specification languages;
fLanguage
English
Journal_Title
Knowledge and Data Engineering, IEEE Transactions on
Publisher
ieee
ISSN
1041-4347
Type
jour
DOI
10.1109/69.317700
Filename
317700
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