Abstract
This paper is concerned with finding near optimal parameters for the inventory optimization model on large dataset. It is shown that our proposed method allows for very good model parameter estimation with great reduction in computation time. Model developed in cooperation with K2 atmitec s.r.o. company has four input parameters which must be set before the run. These parameters are estimated through computationally complex simulations by hyperparameter search. Since it is impossible to make grid search of the optimal parameters for all the input time series, it is necessary to approximate the parameters settings. This approximation is done through similarity search and computation of optimal parameters on the most central objects. Additionally, parameter estimation is improved by the clustering of time series and the results are upgraded by the new estimations.
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Acknowledgements
This work was supported by The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPS II) project “IT4Innovations excellence in science - LQ1602” and by the “IT4Innovations infrastructure which is supported from the Large Infrastructures for Research, Experimental Development and Innovations project IT4Innovations National Supercomputing Center - LM2015070” and partially supported by the SGC grant No. SP2018/173 “Dynamic systems problems and their implementation on HPC", VŠB - Technical University of Ostrava, Czech Republic.
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Martinovič, T., Janurová, K., Martinovič, J., Slaninová, K. (2021). Inventory Optimization Model Parameter Search Speed-Up Through Similarity Reduction. In: Balas, V., Jain, L., Balas, M., Shahbazova, S. (eds) Soft Computing Applications. SOFA 2018. Advances in Intelligent Systems and Computing, vol 1221. Springer, Cham. https://doi.org/10.1007/978-3-030-51992-6_9
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