IDEAS home Printed from https://ideas.repec.org/p/han/dpaper/dp-708.html
   My bibliography  Save this paper

A framework for multi-objective stochastic lot sizing with multiple decision stages

Author

Listed:
  • Friese, Fabian
  • Helber, Stefan

Abstract

In stochastic lot sizing subject to dynamic and random demand, the minimization of operational costs is not the only conceivable objective. Minimizing the tardiness in customer demand satisfaction is no less important. Furthermore, the decision maker is interested in production plan stability. Therefore, we consider those three objectives simultaneously and propose a multi-objective model formulation and decision-making framework of the stochastic capacitated lot sizing problem (MOSCLSP). Demand is modeled via the Martingale Model of Forecast Evolution to allow gradual adaptations of the demand forecasts due to sequential market observations. We propose an interactive multi-objective optimization algorithm for solving the MO-SCLSP, that systematically takes prior demand realization information into account. In multiple decision stages, periodic re-optimizations are carried out, allowing to adjust the production plan to the actual demand realizations. In each decision stage, methods from multi-objective optimization are applied to derive a set of Pareto-optimal solutions. These Pareto-optimal solutions outline the attainable objective space, thus supporting the decision maker in taking an informed and economically profound position between prioritizing low operational costs, high delivery reliability and low production plan nervousness.

Suggested Citation

  • Friese, Fabian & Helber, Stefan, 2023. "A framework for multi-objective stochastic lot sizing with multiple decision stages," Hannover Economic Papers (HEP) dp-708, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    • Handle: RePEc:han:dpaper:dp-708
    as

    Download full text from publisher

    File URL: https://diskussionspapiere.wiwi.uni-hannover.de/pdf_bib/dp-708.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. James H. Bookbinder & Jin-Yan Tan, 1988. "Strategies for the Probabilistic Lot-Sizing Problem with Service-Level Constraints," Management Science, INFORMS, vol. 34(9), pages 1096-1108, September.
    2. Amir Hossein Azadnia & Muhamad Zameri Mat Saman & Kuan Yew Wong, 2015. "Sustainable supplier selection and order lot-sizing: an integrated multi-objective decision-making process," International Journal of Production Research, Taylor & Francis Journals, vol. 53(2), pages 383-408, January.
    3. Karimi, B. & Fatemi Ghomi, S. M. T. & Wilson, J. M., 2003. "The capacitated lot sizing problem: a review of models and algorithms," Omega, Elsevier, vol. 31(5), pages 365-378, October.
    Full references (including those not matched with items on IDEAS)

    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. Simon Thevenin & Yossiri Adulyasak & Jean-François Cordeau, 2022. "Stochastic Dual Dynamic Programming for Multiechelon Lot Sizing with Component Substitution," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3151-3169, November.
    2. Tempelmeier, Horst, 2011. "A column generation heuristic for dynamic capacitated lot sizing with random demand under a fill rate constraint," Omega, Elsevier, vol. 39(6), pages 627-633, December.
    3. Hartmut Stadtler & Nikolai Heinrichs, 2024. "Multi-period descriptive sampling for scenario generation applied to the stochastic capacitated lot-sizing problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(3), pages 639-668, September.
    4. Timo Hilger & Florian Sahling & Horst Tempelmeier, 2016. "Capacitated dynamic production and remanufacturing planning under demand and return uncertainty," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(4), pages 849-876, October.
    5. Azaron, Amir & Tang, Ou & Tavakkoli-Moghaddam, Reza, 2009. "Dynamic lot sizing problem with continuous-time Markovian production cost," International Journal of Production Economics, Elsevier, vol. 120(2), pages 607-612, August.
    6. Taş, Duygu & Gendreau, Michel & Jabali, Ola & Jans, Raf, 2019. "A capacitated lot sizing problem with stochastic setup times and overtime," European Journal of Operational Research, Elsevier, vol. 273(1), pages 146-159.
    7. Brahimi, Nadjib & Absi, Nabil & Dauzère-Pérès, Stéphane & Nordli, Atle, 2017. "Single-item dynamic lot-sizing problems: An updated survey," European Journal of Operational Research, Elsevier, vol. 263(3), pages 838-863.
    8. Gruson, Matthieu & Cordeau, Jean-François & Jans, Raf, 2018. "The impact of service level constraints in deterministic lot sizing with backlogging," Omega, Elsevier, vol. 79(C), pages 91-103.
    9. Tiacci, Lorenzo & Saetta, Stefano, 2009. "An approach to evaluate the impact of interaction between demand forecasting method and stock control policy on the inventory system performances," International Journal of Production Economics, Elsevier, vol. 118(1), pages 63-71, March.
    10. Koca, Esra & Yaman, Hande & Selim Aktürk, M., 2015. "Stochastic lot sizing problem with controllable processing times," Omega, Elsevier, vol. 53(C), pages 1-10.
    11. Sereshti, Narges & Adulyasak, Yossiri & Jans, Raf, 2024. "Managing flexibility in stochastic multi-level lot sizing problem with service level constraints," Omega, Elsevier, vol. 122(C).
    12. Özen, Ulaş & Doğru, Mustafa K. & Armagan Tarim, S., 2012. "Static-dynamic uncertainty strategy for a single-item stochastic inventory control problem," Omega, Elsevier, vol. 40(3), pages 348-357.
    13. Kilic, Onur A. & Tunc, Huseyin & Tarim, S. Armagan, 2018. "Heuristic policies for the stochastic economic lot sizing problem with remanufacturing under service level constraints," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1102-1109.
    14. Melega, Gislaine Mara & de Araujo, Silvio Alexandre & Jans, Raf, 2018. "Classification and literature review of integrated lot-sizing and cutting stock problems," European Journal of Operational Research, Elsevier, vol. 271(1), pages 1-19.
    15. Andreas Schiessl & Richard Müller & Rebekka Volk & Konrad Zimmer & Patrick Breun & Frank Schultmann, 2020. "Integrating site-specific environmental impact assessment in supplier selection: exemplary application to steel procurement," Journal of Business Economics, Springer, vol. 90(9), pages 1409-1457, November.
    16. Metzker Soares, Paula & Thevenin, Simon & Adulyasak, Yossiri & Dolgui, Alexandre, 2024. "Adaptive robust optimization for lot-sizing under yield uncertainty," European Journal of Operational Research, Elsevier, vol. 313(2), pages 513-526.
    17. Abderahman Rejeb & Karim Rejeb & Yasanur Kayikci & Andrea Appolloni & Horst Treiblmaier, 2024. "Mapping the knowledge domain of green procurement: a review and bibliometric analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(12), pages 30027-30061, December.
    18. Abdullah Yıldızbaşı & Cihat Öztürk & Deniz Efendioğlu & Serol Bulkan, 2021. "Assessing the social sustainable supply chain indicators using an integrated fuzzy multi-criteria decision-making methods: a case study of Turkey," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(3), pages 4285-4320, March.
    19. Ali Saeed Almuflih & Janpriy Sharma & Mohit Tyagi & Arvind Bhardwaj & Mohamed Rafik Noor Mohamed Qureshi & Nawaf Khan, 2022. "Leveraging the Dynamics of Food Supply Chains towards Avenues of Sustainability," Sustainability, MDPI, vol. 14(12), pages 1-15, June.
    20. Céline Gicquel & Jianqiang Cheng, 2018. "A joint chance-constrained programming approach for the single-item capacitated lot-sizing problem with stochastic demand," Annals of Operations Research, Springer, vol. 264(1), pages 123-155, May.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

    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:han:dpaper:dp-708. 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: Heidrich, Christian (email available below). General contact details of provider: https://edirc.repec.org/data/fwhande.html .

    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.