Mapping Flows onto Networks to Optimize Organizational Processes

Abstract

Interdependence of tasks in a mission necessitates information flow among the organizational elements (agents) assigned to these tasks. This information flow introduces communication delays. An effective task schedule that minimizes the total execution time, including task processing and coordination delays, is an important issue in designing an organization and its task processing strategy. This paper defines the structure of information-dependent tasks, and describes an approach to map this structure to a network of organizational elements (agents). Since the general problem of scheduling tasks with communication is NP-hard, only fast heuristic (e.g., list scheduling and linear clustering) algorithms are discussed. The authors modify the priority calculation for list scheduling methods, matching the critical path with a network of heterogeneous agents. They then present their algorithm, termed Heterogeneous Dynamic Bottom Level (HDBL), and compare it with various list-scheduling heuristics. The results show that HDBL exhibits superior performance to all list scheduling algorithms, providing an improvement of over 25% in schedule length for communication-intensive task graphs.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA440387

Entities

People

  • David Lee Kleinman
  • Georgiy M. Levchuk
  • Krishna R. Pattipati
  • Yuri N. Levchuk

Organizations

  • University of Connecticut

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Command And Control
  • Computations
  • Computing System Architectures
  • Distributed Computing
  • Electronic Mail
  • Gantt Charts
  • Heterogeneous Networks
  • Information Transfer
  • Military Research
  • Network Topology
  • Networks
  • Organizational Structure
  • Parallel Computing
  • Parallel Processing
  • Scheduling (Production)
  • Time Intervals

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Parallel and Distributed Computing.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.