© Przemysław Szufel. Article available under the CC BY 4.0 licence
ARTICLE
ABSTRACT
This paper presents a model for short-term time-horizon production and distribution planning of a manufacturing company located in the middle of a supply chain. The model focuses on an unbalanced market with broken supply chains. This reflects the state of the current post-COVID-19 economy, which is additionally struggling with even more uncertainty and disruptions due to the Russian aggression against Ukraine. The manufacturer, operating on the post-pandemic and post-war market, on the one hand observes a soaring demand for its products, and on the other faces uncertainty regarding the availability of components (parts) used in the manufacturing process. The goal of the company is to maximise profits despite the uncertain availability of intermediate products. In the short term, the company cannot simply raise prices, as it is bound by long-term contracts with its business partners. The company also has to maintain a good relationship with its customers, i.e. businesses further in the supply chain, by proportionally dividing its insufficient production and trying to match production planning with the observed demand. The post-COVID-19 production-planning problem has been addressed with a robust mixed integer optimisation model along with a dedicated heuristic, which makes it possible to find approximate solutions in a large-scale real-world setting.
KEYWORDS
production, optimisation techniques, simulation modelling, programming models, transportation economics
JEL
C44, C61, L90
REFERENCES
Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust Optimization (vol. 28). Princeton University Press. https://doi.org/10.1515/9781400831050.
Bertsimas, D., Dunning, I., & Lubin, M. (2016). Reformulation versus cutting-planes for robust optimization. Computational Management Science, 13(2), 195–217. https://doi.org/10.1007/s10287-015-0236-z.
Beyer, H.-G., & Sendhoff, B. (2007). Robust optimization – A comprehensive survey. Computer methods in applied mechanics and engineering, 196(33–34), 3190–3218. https://doi.org/10.1016/j.cma.2007.03.003.
Boston, W. (2022. March 3). Ukraine War Plunges Auto Makers Into New Supply-Chain Crisis. Wall Street Journal, 3. https://www.wsj.com/articles/ukraine-war-plunges-auto-makers-into-new-supply-chain-crisis-11646309152.
Brakman, S., Garretsen, H., & van Witteloostuijn, A. (2020). The turn from just-in-time to just-in- -case globalization in and after times of COVID-19. An essay on the risk re-appraisal of borders and buffers. Social Sciences & Humanities Open, 2(1), 1–6. https://doi.org/10.1016/j.ssaho.2020.100034.
Cai, M., & Luo, J. (2020). Influence of COVID-19 on Manufacturing Industry and Corresponding Countermeasures from Supply Chain Perspective. Journal of Shanghai Jiaotong University (Science), 25(4), 409–416. https://doi.org/10.1007/s12204-020-2206-z.
Chen, J., Wang, H., & Zhong, R. Y. (2021). A supply chain disruption recovery strategy considering product change under COVID-19. Journal of Manufacturing Systems, 60, 920–927. https://doi.org/10.1016/j.jmsy.2021.04.004.
Dunning, I., Huchette, J., & Lubin, M. (2017). JuMP: A Modeling Language for Mathematical Optimization. SIAM review, 59(2), 295–320. https://doi.org/10.1137/15M1020575.
Legat, B., Dowson, O., Garcia, J. D., & Lubin, M. (2022). MathOptInterface: A Data Structure for Mathematical Optimization Problems. INFORMS Journal on Computing, 34(2), 672–689. https://doi.org/10.1287/ijoc.2021.1067.
Li, X., Wang, B., Liu, C., Freiheit, T., & Epureanu, B. I. (2020). Intelligent Manufacturing Systems in COVID-19 Pandemic and Beyond: Framework and Impact Assessment. Chinese Journal of Mechanical Engineering, 33(1), 1–5. https://doi.org/10.1186/s10033-020-00476-w.
Li, Z., Ding, R., & Floudas, C. A. (2011). A comparative theoretical and computational study on robust counterpart optimization: I. robust linear optimization and robust mixed integer linear optimization. Industrial & Engineering Chemistry Research, 50(18), 10567–10603. https://doi.org/10.1021/ie200150p.
Mbah, R. E., & Wasum, D. F. (2022). Russian-Ukraine 2022 war: A Review of the Economic Impact of Russian-Ukraine Crisis on the USA, UK, Canada, and Europe. Advances in Social Sciences Research Journal, 9(3), 144–153. https://doi.org/10.14738/assrj.93.12005.
Paul, S. K., & Chowdhury, P. (2021). A production recovery plan in manufacturing supply chains for a high-demand item during COVID-19. International Journal of Physical Distribution & Logistics Management, 51(2), 104–125. https://doi.org/10.1108/IJPDLM-04-2020-0127.
Queiroz, M. M., Ivanov, D., Dolgui, A., & Fosso Wamba, S. (2020). Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of Operations Research, 1–38. https://doi.org/10.1007/s10479-020-03685-7.
Shahed, K. S., Azeem, A., Ali, S. M., & Moktadir, M. A. (2021). A supply chain disruption risk mitigation model to manage COVID-19 pandemic risk. Environmental Science and Pollution Research, 1–16. https://doi.org/10.1007/s11356-020-12289-4.
Singh, S., Kumar, R., Panchal, R., & Tiwari, M. K. (2021). Impact of COVID-19 on logistics systems and disruptions in food supply chain. International Journal of Production Research, 59(7), 1993–2008. https://doi.org/10.1080/00207543.2020.1792000.
Tesfatsion, L. (2003). Agent-based computational economics: modeling economies as complex adaptive systems. Information Sciences, 149(4), 262–269. https://doi.org/10.1016/S0020-0255(02)00280-3.
Tsolas, S. D., & Hasan, M. M. F. (2021). Survivability-aware design and optimization of distributed supply chain networks in the post COVID-19 era. Journal of Advanced Manufacturing and Processing, 3(3), 1–14. https://doi.org/10.1002/amp2.10098.
