IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v142y2002i1p128-137.html

An optimization approach to plan for reusable software components

Author

Listed:
  • Sundarraj, R. P.

Abstract

No abstract is available for this item.

Suggested Citation

  • Sundarraj, R. P., 2002. "An optimization approach to plan for reusable software components," European Journal of Operational Research, Elsevier, vol. 142(1), pages 128-137, October.
    • Handle: RePEc:eee:ejores:v:142:y:2002:i:1:p:128-137
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(01)00285-5
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. G. L. Nemhauser & L. A. Wolsey, 1978. "Best Algorithms for Approximating the Maximum of a Submodular Set Function," Mathematics of Operations Research, INFORMS, vol. 3(3), pages 177-188, August.
    2. Donald Erlenkotter, 1978. "A Dual-Based Procedure for Uncapacitated Facility Location," Operations Research, INFORMS, vol. 26(6), pages 992-1009, December.
    3. Rajiv D. Banker & Gordon B. Davis & Sandra A. Slaughter, 1998. "Software Development Practices, Software Complexity, and Software Maintenance Performance: A Field Study," Management Science, INFORMS, vol. 44(4), pages 433-450, April.
    4. Nemhauser, G.L. & Wolsey, L.A., 1978. "Best algorithms for approximating the maximum of a submodular set function," LIDAM Reprints CORE 343, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tang, J.F. & Mu, L.F. & Kwong, C.K. & Luo, X.G., 2011. "An optimization model for software component selection under multiple applications development," European Journal of Operational Research, Elsevier, vol. 212(2), pages 301-311, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chuangen Gao & Shuyang Gu & Jiguo Yu & Hai Du & Weili Wu, 2022. "Adaptive seeding for profit maximization in social networks," Journal of Global Optimization, Springer, vol. 82(2), pages 413-432, February.
    2. Yuval Filmus & Yasushi Kawase & Yusuke Kobayashi & Yutaro Yamaguchi, 2021. "Tight Approximation for Unconstrained XOS Maximization," Mathematics of Operations Research, INFORMS, vol. 46(4), pages 1599-1610, November.
    3. Suning Gong & Qingqin Nong & Jiazhu Fang & Ding-Zhu Du, 2024. "Algorithms for Cardinality-Constrained Monotone DR-Submodular Maximization with Low Adaptivity and Query Complexity," Journal of Optimization Theory and Applications, Springer, vol. 200(1), pages 194-214, January.
    4. Bin Liu & Miaomiao Hu, 2022. "Fast algorithms for maximizing monotone nonsubmodular functions," Journal of Combinatorial Optimization, Springer, vol. 43(5), pages 1655-1670, July.
    5. R. Garbe & K. D. Glazebrook, 1998. "Submodular Returns and Greedy Heuristics for Queueing Scheduling Problems," Operations Research, INFORMS, vol. 46(3), pages 336-346, June.
    6. Wenzhe Zhang & Shufang Gong & Bin Liu & Qian Liu & Priyanshi Garg, 2026. "On maximizing k-submodular functions under p-system and d-knapsack constraints," Journal of Combinatorial Optimization, Springer, vol. 51(2), pages 1-23, March.
    7. Qimeng Yu & Simge Küçükyavuz, 2025. "On Constrained Mixed-Integer DR-Submodular Minimization," Mathematics of Operations Research, INFORMS, vol. 50(2), pages 871-909, May.
    8. Zhenning Zhang & Donglei Du & Yanjun Jiang & Chenchen Wu, 2021. "Maximizing DR-submodular+supermodular functions on the integer lattice subject to a cardinality constraint," Journal of Global Optimization, Springer, vol. 80(3), pages 595-616, July.
    9. Awi Federgruen & Nan Yang, 2008. "Selecting a Portfolio of Suppliers Under Demand and Supply Risks," Operations Research, INFORMS, vol. 56(4), pages 916-936, August.
    10. Eric Balkanski & Aviad Rubinstein & Yaron Singer, 2022. "An Optimal Approximation for Submodular Maximization Under a Matroid Constraint in the Adaptive Complexity Model," Operations Research, INFORMS, vol. 70(5), pages 2967-2981, September.
    11. Simon Bruggmann & Rico Zenklusen, 2019. "Submodular Maximization Through the Lens of Linear Programming," Management Science, INFORMS, vol. 44(4), pages 1221-1244, November.
    12. Ivan Contreras & Elena Fernández, 2014. "Hub Location as the Minimization of a Supermodular Set Function," Operations Research, INFORMS, vol. 62(3), pages 557-570, June.
    13. Xin Sun & Tiande Guo & Congying Han & Hongyang Zhang, 2025. "Greedy algorithms for stochastic monotone k-submodular maximization under full-bandit feedback," Journal of Combinatorial Optimization, Springer, vol. 49(1), pages 1-25, January.
    14. Moran Feldman & Paul Liu & Ashkan Norouzi-Fard & Ola Svensson & Rico Zenklusen, 2026. "Streaming Submodular Maximization Under Matroid Constraints," Mathematics of Operations Research, INFORMS, vol. 51(1), pages 299-332, January.
    15. Jon Lee & Maxim Sviridenko & Jan Vondrák, 2010. "Submodular Maximization over Multiple Matroids via Generalized Exchange Properties," Mathematics of Operations Research, INFORMS, vol. 35(4), pages 795-806, November.
    16. Cheng Lu & Wenguo Yang & Ruiqi Yang & Suixiang Gao, 2022. "Maximizing a non-decreasing non-submodular function subject to various types of constraints," Journal of Global Optimization, Springer, vol. 83(4), pages 727-751, August.
    17. Maxim Sviridenko & Jan Vondrák & Justin Ward, 2017. "Optimal Approximation for Submodular and Supermodular Optimization with Bounded Curvature," Mathematics of Operations Research, INFORMS, vol. 42(4), pages 1197-1218, November.
    18. Christopher Harshaw & Ehsan Kazemi & Moran Feldman & Amin Karbasi, 2022. "The Power of Subsampling in Submodular Maximization," Mathematics of Operations Research, INFORMS, vol. 47(2), pages 1365-1393, May.
    19. Niv Buchbinder & Moran Feldman & Roy Schwartz, 2017. "Comparing Apples and Oranges: Query Trade-off in Submodular Maximization," Mathematics of Operations Research, INFORMS, vol. 42(2), pages 308-329, May.
    20. Naor Alaluf & Alina Ene & Moran Feldman & Huy L. Nguyen & Andrew Suh, 2022. "An Optimal Streaming Algorithm for Submodular Maximization with a Cardinality Constraint," Mathematics of Operations Research, INFORMS, vol. 47(4), pages 2667-2690, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:142:y:2002:i:1:p:128-137. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.