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Simulation and optimization of supply chains: alternative or complementary approaches?

In: Supply Chain Planning

Author

Listed:
  • Christian Almeder

    (University of Vienna)

  • Margaretha Preusser

    (University of Vienna)

  • Richard F. Hartl

    (University of Vienna)

Abstract

Discrete-event simulation and (mixed-integer) linear programming are widely used for supply chain planning. We present a general framework to support the operational decisions for supply chain networks using a combination of an optimization model and discrete-event simulation. The simulation model includes nonlinear and stochastic elements, whereas the optimization model represents a simplified version. Based on initial simulation runs cost parameters, production, and transportation times are estimated for the optimization model. The solution of the optimization model is translated into decision rules for the discrete-event simulation. This procedure is applied iteratively until the difference between subsequent solutions is small enough. This method is applied successfully to several test examples and is shown to deliver competitive results much faster compared to conventional mixed-integer models in a stochastic environment. It provides the possibility to model and solve more realistic problems (incorporating dynamism and uncertainty) in an acceptable way. The limitations of this approach are given as well.

Suggested Citation

  • Christian Almeder & Margaretha Preusser & Richard F. Hartl, 2009. "Simulation and optimization of supply chains: alternative or complementary approaches?," Springer Books, in: Herbert Meyr & Hans-Otto Günther (ed.), Supply Chain Planning, pages 29-53, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-93775-3_2
    DOI: 10.1007/978-3-540-93775-3_2
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    Citations

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    Cited by:

    1. Giscard Valonne Mouafo Nebot & Haiyan Wang, 2022. "RETRACTED: Port Terminal Performance Evaluation and Modeling," Logistics, MDPI, vol. 6(1), pages 1-22, January.
    2. MacCarthy, Bart L. & Ovutmen, Tamer, 2015. "Using a central Vehicle Holding Compound (VHC) in an open pipeline automotive order fulfilment system: A simulation study," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 590-601.
    3. Huang, Shiyang & Hu, Guiping & Chennault, Carrie & Su, Liu & Brandes, Elke & Heaton, Emily & Schulte, Lisa & Wang, Lizhi & Tyndall, John, 2016. "Agent-based modeling of bioenergy crop adoption and farmer decision-making," Energy, Elsevier, vol. 115(P1), pages 1188-1201.
    4. Romauch, Martin & Hartl, Richard F., 2017. "Capacity planning for cluster tools in the semiconductor industry," International Journal of Production Economics, Elsevier, vol. 194(C), pages 167-180.
    5. Matt Bassett & Leslie Gardner, 2013. "Designing optimal global supply chains at Dow AgroSciences," Annals of Operations Research, Springer, vol. 203(1), pages 187-216, March.
    6. Eren Özceylan & Cihan Çetinkaya & Neslihan Demirel & Ozan Sabırlıoğlu, 2017. "Impacts of Additive Manufacturing on Supply Chain Flow: A Simulation Approach in Healthcare Industry," Logistics, MDPI, vol. 2(1), pages 1-20, December.
    7. Shishvan, Masoud Soleymani & Benndorf, Jörg, 2019. "Simulation-based optimization approach for material dispatching in continuous mining systems," European Journal of Operational Research, Elsevier, vol. 275(3), pages 1108-1125.
    8. Manuel Schlenkrich & Wolfgang Seiringer & Klaus Altendorfer & Sophie N. Parragh, 2024. "Enhancing Rolling Horizon Production Planning Through Stochastic Optimization Evaluated by Means of Simulation," Papers 2402.14506, arXiv.org, revised Sep 2024.
    9. Andres F. Osorio & Sally C. Brailsford & Honora K. Smith & Sonia P. Forero-Matiz & Bernardo A. Camacho-Rodríguez, 2017. "Simulation-optimization model for production planning in the blood supply chain," Health Care Management Science, Springer, vol. 20(4), pages 548-564, December.
    10. Juan, Angel A. & Faulin, Javier & Grasman, Scott E. & Rabe, Markus & Figueira, Gonçalo, 2015. "A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems," Operations Research Perspectives, Elsevier, vol. 2(C), pages 62-72.
    11. Huang, Shiyang & Hu, Guiping, 2018. "Biomass supply contract pricing and environmental policy analysis: A simulation approach," Energy, Elsevier, vol. 145(C), pages 557-566.
    12. Bilge Bilgen & Yelda Çelebi, 2013. "Integrated production scheduling and distribution planning in dairy supply chain by hybrid modelling," Annals of Operations Research, Springer, vol. 211(1), pages 55-82, December.
    13. Mizgier, Kamil J. & Wagner, Stephan M. & Holyst, Janusz A., 2012. "Modeling defaults of companies in multi-stage supply chain networks," International Journal of Production Economics, Elsevier, vol. 135(1), pages 14-23.
    14. Bertha Maya Sopha & Sekar Sakti & Ari Carisza Graha Prasetia & Marselina Winda Dwiansarinopa & Kevin Cullinane, 2021. "Simulating long-term performance of regional distribution centers in archipelagic logistics systems," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(4), pages 697-725, December.
    15. Linsen Chong & Carolina Osorio, 2018. "A Simulation-Based Optimization Algorithm for Dynamic Large-Scale Urban Transportation Problems," Transportation Science, INFORMS, vol. 52(3), pages 637-656, June.
    16. Martin Hrušovský & Emrah Demir & Werner Jammernegg & Tom Woensel, 2018. "Hybrid simulation and optimization approach for green intermodal transportation problem with travel time uncertainty," Flexible Services and Manufacturing Journal, Springer, vol. 30(3), pages 486-516, September.

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