Modeling Supervisory Control in the Air Defense Warfare Domain with Queueing Theory

Abstract

In this paper, we hypothesize that the performance of a supervisory control operator that must process tasks recommended by a system task manager is analogous to the performance of a vacationing server, M/Er/1 queue. Thus, we assume that the input process is Markovian and that service consists of r- stages of processing, each of which is exponentially distributed. In addition, we assume that when there are no tasks in the queue to process, the operator takes a vacation, i.e., goes off and performs other duties. The model assumed vacation time was exponentially distributed. We derive the queueing statistics for this system. These statistics include (1) the average number of customers, tasks, in the queue, (2) the average time a task spends in the queue, and (3) the average waiting time in the queue. We extend this model to a two-class priority M/Er/1 vacationing server system. The results of these predictions were compared to actual operator performance. This operator was also modeled using GOMSL. Both the GOMSL and queueing models provided effective prediction of actual operator performance.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2005
Accession Number
ADA463905

Entities

People

  • Glenn Osga
  • Joseph Divita
  • Robert Morris

Organizations

  • Naval Information Warfare Systems Command

Tags

Communities of Interest

  • Autonomy
  • Human Systems
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Defense
  • Cognition
  • Cognitive Systems Engineering
  • Command And Control
  • Human Factors Engineering
  • Human Systems Integration
  • Human-Computer Interaction
  • Language
  • Naval Warfare
  • Probability
  • Psychology
  • Queueing Theory
  • Random Variables
  • Reaction Time
  • Social Sciences
  • Supervisory Control
  • Task Performance And Analysis

Readers

  • Computational Modeling and Simulation
  • Instructional Design and Training Evaluation.
  • Mathematical Modeling and Probability Theory.