Search, Space, and Time

  • George S. Fishman
Part of the Springer Series in Operations Research book series (ORFE)


Every execution of a computer program uses memory space and consumes computing time. In particular, a discrete-event simulation expends a considerable proportion of its running time executing searches for new space and creating and maintaining order among the myriad of entity records and event notices it generates as simulated time evolves. In spite of their relative importance, current PC workstation environments, with their substantial memories and reduced, if nonexistent, emphasis on execution within a specified computing time constraint, make these topics appear less important to the simulationist than they were in the past. Moreover, every simulation programming language implicitly provides a means for managing space and performing searches during execution of virtually any program written in the language, further removing these issues from a simulationist’s consciousness.


Computing Time Event Notice Memory Space Interarrival Time Average Selection Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Arguelles, M.C., and G.S. Fishman (1997). Reducing the frequency of future event set search in manufacturing simulation, Operations Research Department, University of North Carolina at Chapel Hill, in preparation.Google Scholar
  2. Chung, K., J. Sang, and V. Rego (1993). A performance comparison of event calendar algorithms: an empirical approach, Software-Practice and Experience, 23, 1107–1138.CrossRefGoogle Scholar
  3. Evans, J.B. (1988). Structure of Discrete Event Simulation, Ellis Horwood Limited, Chichester, England.Google Scholar
  4. Henriksen, J.O. (1977). An improved events list algorithm, Proceedings of the 1977 Winter Simulation Conference, H.J. Highland, R.G. Sargent, and J.W. Schmidt, editors, 547557.Google Scholar
  5. Henriksen, J.O. (1983). Event list management, a tutorial, Proc. Winter Simulation Conference, IEEE and SCS, Piscataway, N.J., 543–551.Google Scholar
  6. Henriksen, J.O. (1994). Personal communication.Google Scholar
  7. Jones, D.W. (1986). An empirical comparison of priority-queue and event-set implementations, Comm. ACM, 29, 300–310.CrossRefGoogle Scholar
  8. Kingston, J.H. (1986a). Analysis of Henriksen’s algorithm for the simulation event set, SIAM J. Comput., 15, 887–902.MathSciNetzbMATHCrossRefGoogle Scholar
  9. Kingston, J.H. (1986b). The amortized complexity of Henriksen’s algorithm, Bit, 26, 156163.Google Scholar
  10. Knuth, D. (1973). The Art of Computer Programming: Sorting and Searching, Addison-Wesley, Reading, MA.Google Scholar
  11. Köllerström, J. (1974). Heavy traffic theory for queues with several servers. I, J. Appl. Prob., 11, 544–552.zbMATHCrossRefGoogle Scholar
  12. McCormack, W.M. and R.G. Sargent (1981). Analysis of future event set algorithms for discrete event simulation, Comm. ACM, 24, 801–812.MathSciNetCrossRefGoogle Scholar
  13. Williams, J.W.J. (1964). “Algorithm 232,” Comm. ACM, 7, 347–348.Google Scholar

Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • George S. Fishman
    • 1
  1. 1.Department of Operations ResearchUniversity of North Carolina at Chapel HillChapel HillUSA

Personalised recommendations