TRAPS - A Time Dependent Resource Allocation Language

A general language for specifying resource allocation in Scheduling problems is presented. TRAPS is a superclass of the RAPS (Resource Allocation Problem Specication) language developed by the authors [31]. TRAPS enables the speci cation of a scheduling problem by adding built in time operators, on top of existing terms for resources, activities, allocation rules and constraints. In this way TRAPS provides a convenient knowledge acquisition tool. The language syntax is powerful and allows the speci cation of rules and constraints which are di cult to formulate with traditional approaches, and it also supports the speci cation of various control and backtracking strategies. The generalized inference engine that runs compiled TRAPS programs is enhanced to provide all needed operations for a typical PERT/CPM calculations on a schedule. This engine acts as an expert system shell and is called ESRA (Expert System for Resource Allocation). The performance of TRAPS combined with ESRA is demonstrated by analyzing its solution of a typical scheduling problem, that of a software management project. The analysis shows that certain heuristics which formed the basis of our strategy for resource allocation (cf. [31]) perform very well also in the (time dependent) scheduling domain. keywords: expert systems, scheduling, resource allocation, Prolog, knowledge acquisition.

[1]  Angelo Monfroglio,et al.  Timetabling through a Deductive Database: A Case Study , 1988, Data Knowl. Eng..

[2]  James F. Allen.  Maintaining knowledge about temporal intervals , 1983, CACM.

[3]  Michael W. Carter,et al.  OR Practice - A Survey of Practical Applications of Examination Timetabling Algorithms , 1986, Oper. Res..

[4]  Stephen F. Smith,et al.  Slack-Based Heuristics for Constraint Satisfaction Scheduling , 1993, AAAI.

[5]  Vice President,et al.  An Introduction to Expert Systems , 1989 .

[6]  Patrick Prosser,et al.  A Reactive Scheduling Agent , 1989, IJCAI.

[7]  Eric Bensana.  Advanced job-shop scheduling in aeronautical manufacturing , 1993 .

[8]  Edward W. Davis,et al.  A Comparison of Heuristic and Optimum Solutions in Resource-Constrained Project Scheduling , 1975 .

[9]  Gilbert Laporte,et al.  The problem of assigning students to course sections in a large engineering school , 1986, Comput. Oper. Res..

[10]  J. D. Uiiman,et al.  Principles of Database Systems , 2004, PODS 2004.

[11]  Jacques Cohen,et al.  Constraint logic programming languages , 1990, CACM.

[12]  S. Marcus,et al.  Understanding decision ordering from a piece-meal collection of knowledge , 1989 .

[13]  Debra Anderson,et al.  AALPS A Knowledge-Based System for Aircraft Loading , 1987, IEEE Expert.

[14]  Rina Dechter,et al.  Network-Based Heuristics for Constraint-Satisfaction Problems , 1987, Artif. Intell..

[15]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[16]  E. W. Davis,et al.  Project Management With Cpm, Pert and Precedence Diagramming , 1983 .

[17]  Frederick Hayes-Roth,et al.  Building expert systems , 1983, Advanced book program.

[18]  James R. Slagle,et al.  An expert system for a resource allocation problem , 1985, CACM.

[19]  Kathleen M. Swigger,et al.  GATES: an airline gate assignment and tracking expert system , 1988, IEEE Expert.

[20]  Vasant Dhar,et al.  Integer programming vs. expert systems: an experimental comparison , 1990, CACM.

[21]  Ehud Gudes,et al.  On resource allocation by an expert system , 1990 .

[22]  Murray Hill,et al.  Yacc: Yet Another Compiler-Compiler , 1978 .