Distributed expert systems for queueing network capacity planning

Yuval Lirov, Benjamin Melamed

Research output: Contribution to journalArticlepeer-review

Abstract

Queueing network capacity planning can become algorithmically intractable for moderately large networks. It is, therefore, a promising application area for expert systems. However, a survey of the published literature reveals a paucity of integrated systems combining design and optimization of network-based problems. We present a distributed expert system for network capacity planning, which uses Monte Carlo simulation-based optimization methodology for queueing networks. Our architecture admits parallel simulation of multiple configurations. A knowledge-based search drives the performance optimization of the network. The search process is a randomized combination of steepest descent and branch and bound algorithms, where the generating function of new states uses qualitative reasoning, and the gradient of the objective function is estimated using a heuristic score function method. We found a random search based on the relative order of the performance gradient components to be a powerful qualitative reasoning technique. The system is implemented as a loosely coupled expert system with components written in Prolog, Simscript and C. We demonstrate the efficacy of our approach through an example from the domain of Jackson queueing networks.

Original languageEnglish (US)
Pages (from-to)137-155
Number of pages19
JournalAnnals of Operations Research
Volume39
Issue number1
DOIs
StatePublished - Dec 1992
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Management Science and Operations Research

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