IDEAS home Printed from https://ideas.repec.org/a/spr/flsman/v31y2019i2d10.1007_s10696-018-9316-z.html
   My bibliography  Save this article

A branch-and-bound approach for the single machine maximum lateness stochastic scheduling problem to minimize the value-at-risk

Author

Listed:
  • M. Urgo

    (Politecnico di Milano)

  • J. Váncza

    (Hungarian Academy of Sciences
    Budapest University of Technology and Economics)

Abstract

The research in the field of robust scheduling aims at devising schedules which are not sensitive—to a certain extent—to the disruptive effects of unexpected events. Nevertheless, the protection of the schedule from rare events causing heavy losses is still a challenging aim. The paper presents a novel approach for protecting the quality of a schedule by assessing the risk associated to the different scheduling decisions. The approach is applied to a stochastic scheduling problem with a set of jobs to be sequenced on a single machine. The release dates and processing times of the jobs are generally distributed independent random variables, while the due dates are deterministic. A branch-and-bound approach is taken to minimise the value-at-risk of the distribution of the maximum lateness. The viability of the approach is demonstrated through a computational experiment and the application to an industrial problem in the tool making industry.

Suggested Citation

  • M. Urgo & J. Váncza, 2019. "A branch-and-bound approach for the single machine maximum lateness stochastic scheduling problem to minimize the value-at-risk," Flexible Services and Manufacturing Journal, Springer, vol. 31(2), pages 472-496, June.
    • Handle: RePEc:spr:flsman:v:31:y:2019:i:2:d:10.1007_s10696-018-9316-z
    DOI: 10.1007/s10696-018-9316-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10696-018-9316-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10696-018-9316-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Baker, Kenneth R. & Trietsch, Dan, 2009. "Safe scheduling: Setting due dates in single-machine problems," European Journal of Operational Research, Elsevier, vol. 196(1), pages 69-77, July.
    2. Carlier, Jacques, 1982. "The one-machine sequencing problem," European Journal of Operational Research, Elsevier, vol. 11(1), pages 42-47, September.
    3. Wu, Xianyi & Zhou, Xian, 2008. "Stochastic scheduling to minimize expected maximum lateness," European Journal of Operational Research, Elsevier, vol. 190(1), pages 103-115, October.
    4. Ma, Chenghu & Wong, Wing-Keung, 2010. "Stochastic dominance and risk measure: A decision-theoretic foundation for VaR and C-VaR," European Journal of Operational Research, Elsevier, vol. 207(2), pages 927-935, December.
    5. Kenneth R. Baker & Zaw‐Sing Su, 1974. "Sequencing with due‐dates and early start times to minimize maximum tardiness," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 21(1), pages 171-176, March.
    6. Subhash C. Sarin & Balaji Nagarajan & Sanjay Jain & Lingrui Liao, 2009. "Analytic evaluation of the expectation and variance of different performance measures of a schedule on a single machine under processing time variability," Journal of Combinatorial Optimization, Springer, vol. 17(4), pages 400-416, May.
    7. De, Prabuddha & Ghosh, Jay B. & Wells, Charles E., 1992. "Expectation-variance analyss of job sequences under processing time uncertainty," International Journal of Production Economics, Elsevier, vol. 28(3), pages 289-297, December.
    8. Cheng-Shang Chang & David D. Yao, 1993. "Rearrangement, Majorization and Stochastic Scheduling," Mathematics of Operations Research, INFORMS, vol. 18(3), pages 658-684, August.
    9. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    10. Grabowski, J. & Nowicki, E. & Zdrzalka, S., 1986. "A block approach for single-machine scheduling with release dates and due dates," European Journal of Operational Research, Elsevier, vol. 26(2), pages 278-285, August.
    11. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    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. Alfredo S. Ramos & Pablo A. Miranda-Gonzalez & Samuel Nucamendi-Guillén & Elias Olivares-Benitez, 2023. "A Formulation for the Stochastic Multi-Mode Resource-Constrained Project Scheduling Problem Solved with a Multi-Start Iterated Local Search Metaheuristic," Mathematics, MDPI, vol. 11(2), pages 1-25, January.
    2. Lei Liu & Marcello Urgo, 2024. "Robust scheduling in a two-machine re-entrant flow shop to minimise the value-at-risk of the makespan: branch-and-bound and heuristic algorithms based on Markovian activity networks and phase-type dis," Annals of Operations Research, Springer, vol. 338(1), pages 741-764, 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. Eeckhoudt, Louis & Fiori, Anna Maria & Rosazza Gianin, Emanuela, 2016. "Loss-averse preferences and portfolio choices: An extension," European Journal of Operational Research, Elsevier, vol. 249(1), pages 224-230.
    2. Chang, Zhiqi & Song, Shiji & Zhang, Yuli & Ding, Jian-Ya & Zhang, Rui & Chiong, Raymond, 2017. "Distributionally robust single machine scheduling with risk aversion," European Journal of Operational Research, Elsevier, vol. 256(1), pages 261-274.
    3. Chai, Shanglei & Zhou, P., 2018. "The Minimum-CVaR strategy with semi-parametric estimation in carbon market hedging problems," Energy Economics, Elsevier, vol. 76(C), pages 64-75.
    4. Zongxin Li & Xinge Li & Yongchang Hui & Wing-Keung Wong, 2018. "Maslow Portfolio Selection for Individuals with Low Financial Sustainability," Sustainability, MDPI, vol. 10(4), pages 1-11, April.
    5. Cui, Xueting & Zhu, Shushang & Sun, Xiaoling & Li, Duan, 2013. "Nonlinear portfolio selection using approximate parametric Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 2124-2139.
    6. Kull, Andreas, 2009. "Sharing Risk – An Economic Perspective," ASTIN Bulletin, Cambridge University Press, vol. 39(2), pages 591-613, November.
    7. Curtis, John & Lynch, Muireann Á. & Zubiate, Laura, 2016. "The impact of the North Atlantic Oscillation on electricity markets: A case study on Ireland," Energy Economics, Elsevier, vol. 58(C), pages 186-198.
    8. Brian Tomlin & Yimin Wang, 2005. "On the Value of Mix Flexibility and Dual Sourcing in Unreliable Newsvendor Networks," Manufacturing & Service Operations Management, INFORMS, vol. 7(1), pages 37-57, June.
    9. Alexander, Gordon J. & Baptista, Alexandre M. & Yan, Shu, 2014. "Bank regulation and international financial stability: A case against the 2006 Basel framework for controlling tail risk in trading books," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 107-130.
    10. Kolos Ágoston, 2012. "CVaR minimization by the SRA algorithm," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(4), pages 623-632, December.
    11. Vladimir Rankovic & Mikica Drenovak & Branko Uroševic & Ranko Jelic, 2016. "Mean Univariate-GARCH VaR Portfolio Optimization: Actual Portfolio Approach," CESifo Working Paper Series 5731, CESifo.
    12. Harris, Richard D.F. & Mazibas, Murat, 2013. "Dynamic hedge fund portfolio construction: A semi-parametric approach," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 139-149.
    13. Maziar Sahamkhadam, 2021. "Dynamic copula-based expectile portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 22(3), pages 209-223, May.
    14. Alexandre Carbonneau & Fr'ed'eric Godin, 2021. "Deep equal risk pricing of financial derivatives with non-translation invariant risk measures," Papers 2107.11340, arXiv.org.
    15. Ben Ameur, Hachmi & Ftiti, Zied & Louhichi, Waël & Yousfi, Mohamed, 2024. "Do green investments improve portfolio diversification? Evidence from mean conditional value-at-risk optimization," International Review of Financial Analysis, Elsevier, vol. 94(C).
    16. Martin Herdegen & Cosimo Munari, 2023. "An elementary proof of the dual representation of Expected Shortfall," Papers 2306.14506, arXiv.org.
    17. Matthew Norton & Valentyn Khokhlov & Stan Uryasev, 2021. "Calculating CVaR and bPOE for common probability distributions with application to portfolio optimization and density estimation," Annals of Operations Research, Springer, vol. 299(1), pages 1281-1315, April.
    18. Juan Ma & Foad Mahdavi Pajouh & Balabhaskar Balasundaram & Vladimir Boginski, 2016. "The Minimum Spanning k -Core Problem with Bounded CVaR Under Probabilistic Edge Failures," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 295-307, May.
    19. Ken Kobayashi & Yuichi Takano & Kazuhide Nakata, 2021. "Bilevel cutting-plane algorithm for cardinality-constrained mean-CVaR portfolio optimization," Journal of Global Optimization, Springer, vol. 81(2), pages 493-528, October.
    20. Yuanying Guan & Zhanyi Jiao & Ruodu Wang, 2022. "A reverse ES (CVaR) optimization formula," Papers 2203.02599, arXiv.org, revised May 2023.

    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:spr:flsman:v:31:y:2019:i:2:d:10.1007_s10696-018-9316-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.