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Journal of Intelligent Manufacturing

, Volume 19, Issue 5, pp 611–622 | Cite as

A scalable data structure for real-time estimation of resource availability in build-to-order environments

  • Scott A. Moses
  • Le Gruenwald
  • Khushru Dadachanji
Article

Abstract

This paper defines a highly scalable interval index structure called the Temporal Bin tree (TB-tree) that can be embedded in any resource planning application whose algorithms require efficiently estimating either the time that a resource will be available to process a specific task of known length or the net availability of a resource during a specified period of time. It is specifically engineered to meet the real-time response and space efficiency requirements of large-scale resource planning applications that are required for mass customization. Basically, the TB-tree is a binary tree structure that represents availability of a resource across a planning horizon. Representing intervals of availability hierarchically using a tree structure increases the efficiency of search for resource availability when the discretization of time is fine-grained or the planning horizon is long. The tree forms a backbone structure that does not require disruptive rebalancing during update operations, which would mitigate the ability of the tree to respond to queries in real time. Its specific implementation allows for random access at any level of the tree to further improve scalability. An application of planning to real-time promising of order due dates for custom built products provides the context for empirical evaluation. Results of analytical evaluations and simulation experiments clearly demonstrate the scalability of the TB-tree relative to existing index structures in terms of both time and space.

Keywords

Mass customization Build-to-order Resource planning Resource availability Due date assignment Index data structures 

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References

  1. Aho A.V., Hopcroft J.E., Ullman J.D. (1974) The design and analysis of computer algorithms. Reading, MA, Addison-WesleyGoogle Scholar
  2. Ammann, A. C., Hanrahan, M. B., & Krishnamurthy, R. (1985). Design of a memory resident DBMS. In Proceedings of the IEEE Spring Computer Conference, San Francisco, CA (pp. 54–57). New York: IEEE Press.Google Scholar
  3. Ang C.H., Tan K.P. (1995) The interval B-tree. Information Processing Letters 53: 85–89CrossRefGoogle Scholar
  4. Chaabouni, M., & Chung, S. M. (1993). The point-range tree: A data structure for indexing intervals. In Proceedings of the 1993 ACM Conference on Computer Science, Indianapolis, IN (pp. 453–460). New York: ACM Press.Google Scholar
  5. Comer D. (1979) Ubiquitous B-tree. ACM Computing Surveys 11: 121–137CrossRefGoogle Scholar
  6. Edelsbrunner H. (1983a) A new approach to rectangle intersections, part I. International Journal of Computer Mathematics 13: 209–219CrossRefGoogle Scholar
  7. Edelsbrunner H. (1983b) A new approach to rectangle intersections, part II. International Journal of Computer Mathematics 13: 221–229CrossRefGoogle Scholar
  8. Elmasri R., Wuu G.T., Kouramajian V. (1993) The time index and the monotonic B+ -tree. In: Tansel A.U., Clifford J., Gadia S.K., Jajodia S., Segev A., Snodgrass R.(eds) Temporal databases: Theory, design and implementation. Benjamin/Cummings, Redwood City, CA, pp 433–456Google Scholar
  9. Guttman, A. (1984). R-trees: A dynamic index structure for spatial searching. In Proceedings of the 1984 ACM SIGMOD International Conference on Management of Data, Boston, MA (pp. 47–57). New York: ACM Press.Google Scholar
  10. Kouramajian, V., Kamel, I., Elmasri, R., & Waheed, S. (1994). The Time Index+: An incremental access structure for temporal databases. In Proceedings of the Third International Conference on Information and Knowledge Management, Gaithersburg, Maryland (pp. 296–303). New York: ACM Press.Google Scholar
  11. Lehman, T. J., & Carey, M. J. (1986). A study of index structures for main memory database management systems. In Proceedings of the Twelfth International Conference on Very Large Data Bases, Kyoto, Japan (pp. 294–303). San Francisco, CA: Morgan Kaufmann.Google Scholar
  12. Moses S.A., Grant F.H., Gruenwald G.L., Pulat P.S. (2004) Real-time due-date promising by build-to-order environments. International Journal of Production Research 42: 4353–4375CrossRefGoogle Scholar
  13. Nascimento M.A., Dunham M.H. (1999) Indexing valid time databases via B+ -trees. IEEE Transactions on Knowledge and Data Engineering 11: 929–947CrossRefGoogle Scholar
  14. Rao, J., & Ross, K. A. (1999). Cache conscious indexing for decision-support in main memory. In Proceedings of the 25th International Conference on Very Large Data Bases, Seattle, WA (pp. 78–89). San Francisco, CA: Morgan Kaufmann.Google Scholar
  15. Rao, J., & Ross, K. A. (2000). Making B+ -trees cache conscious in main memory. In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, TX (pp. 475–486). New York: ACM Press.Google Scholar
  16. Salzberg B., Tsotras V.J. (1999) Comparison for access methods for time-evolving data. ACM Computing Surveys 31: 158–221CrossRefGoogle Scholar
  17. Samet H. (1990) The design and analysis of spatial data structures. Addison-Wesley, Reading, MAGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Scott A. Moses
    • 1
  • Le Gruenwald
    • 2
  • Khushru Dadachanji
    • 2
  1. 1.School of Industrial EngineeringThe University of OklahomaNormanUSA
  2. 2.School of Computer ScienceThe University of OklahomaNormanUSA

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