Skip to main content
Log in

Using Metric Spaces in Optimum Scheduling

  • Published:
Automation and Remote Control Aims and scope Submit manuscript

Abstract

The solution of an optimum problem of scheduling with n workpieces and m machine tools represents an optimum schedule of putting pieces on machines. In turn, the schedule is defined by an optimum collection of m permutations out of n objects, i.e., the vector permutation π = (π1, ..., π m ), where each permutation π i (1 ≤ im) points up the sequence of working of all pieces on the ith machine. In this case, to each admissible schedule there must correspond an integral point from the m-dimensional Euclidean space of permutations (or, which is practically the same, the permutation out of numbers {1, 2, ..., mn}. In an effort to seek an optimum schedule, use is made of the notion of a metric space in the set of admissible schedules and the justified methodology of the search for an optimum schedule. A few metric spaces are described and analyzed and their comparative effectiveness is investigated for the solution of a different-route problem of scheduling.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

REFERENCES

  1. Golenko, D., Statisticheskie modeli v upravlenii proizvodstrom (Statistical Models in Production Control), Moscow: Statistika, 1972.

    Google Scholar 

  2. Tanaev, V. and Shkurba, V., Vvedenie v teoriyu raspisanii (Introduction to the Theory of Schedules), Moscow: Nauka, 1975.

    Google Scholar 

  3. Mut, J. and Tompson, G., Kalendarnoe planirovanie (Scheduling), Moscow: Nauka, 1966.

    Google Scholar 

  4. Rinnooy Kan, A.H.G., Machine Scheduling Problems. Hague: Nijhoff, 1976.

    Google Scholar 

  5. Golenko, D., Using Metric Spaces in Optimal Calendar Planning, Ann.New York, Acad.Sci., 1985, vol. 452, pp. 11–22.

    Google Scholar 

  6. Golenko-Ginzburg, D. and Sims, J., Using Permutation Spaces in Job-Shop Scheduling, Oper.Res., 1992, vol. 9, pp. 183–193.

    Google Scholar 

  7. Page, E., On Monte Carlo Methods in Congestion Problems: Searching for Optimum Discrete Situations, Oper.Res., 1965, vol. 13, pp. 291–299.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Golenko-Ginzburg, D.I., Lyubkin, S.M., Rezer, V.S. et al. Using Metric Spaces in Optimum Scheduling. Automation and Remote Control 63, 1515–1523 (2002). https://doi.org/10.1023/A:1020098624562

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1020098624562

Keywords

Navigation