Affiliation:
1. Vladivostok State University of Economics and Service
Abstract
Optimising the supply of raw materials is closely related to the problems that arise at wood processing plants. Assessing the optimality of solutions often becomes a pressing issue.The article considers the scenario of a forestry plant without its own sources of raw materials, such as loggers, which seeks to find an optimal solution at the final stage of planning, based on data on previous transactions. A commodity exchange is considered as a source of raw materials, where lots appear daily in various regions of logging enterprises in a random order.The scientific sources offer multiple methods for calculating optimal profit throughout the entire planning period, but these approaches do not consider many important features of forest processing enterprises.This paper presents a mathematical model that allows estimating the optimal path for profit values throughout the entire planning period. A distinctive feature of the model is that it takes into account the share of the useful volume of raw materials that can be used in production of oriented strand boards after being delivered to the warehouse, as well as the delivery time of lots under conditions of uncertainty.After testing on data from the Russian Mercantile Exchange and one of Primorsky region enterprises, the model was applied to calculate the optimal profit trajectory for various data, including volumes of raw materials, time of lot delivery and other important production indicators, such as profit volume and volume of goods produced. Analysis of the results revealed difficulties in planning supply chains and production volumes. Regions as sources of raw materials were analysed, and it was determined from which regions and at what point it is worth purchasing raw materials. The article discusses in detail the disadvantages and advantages of the mathematical model.
Publisher
FSBEO HPE Moscow State University of Railway Engineering (MIIT)
Reference28 articles.
1. Ghasemy, Y. R. Enhancing supply chain productionmarketing planning with geometric multivariate demand function (a case study of textile industry). Computers & Industrial Engineering, 2020, Vol. 140 (19), 106220. DOI: 10.1016/j.cie.2019.106220.
2. Maina, J., Mwangangi, P. W. A Critical Review of Simulation Applications in Supply Chain Management. Journal of Logistics Management, 2020, Vol. 9. pp. 1–6. DOI: 10.5923/j.logistics.20200901.01.
3. Brintrup, A., Pak, J., Ratiney, D., Pearce, T., Wichmann, P., Woodall, P., McFarlane, D. Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing. International Journal of Production Research, 2020, Vol. 58, pp. 3330–3341. DOI: 1 0.1080/00207543.2019.1685705.
4. Dominguez, R., Cannella, S. Insights on Multi-Agent Systems Applications for Supply Chain Management. Sustainability, 2020, Vol. 12, Iss. 5, 1935. DOI: 10.3390/su12051935.
5. Luigi, R., Stamova, I. M., Tomasiello, S. Numerical schemes and genetic algorithms for the optimal control of a continuous model of supply chains. Applied Mathematics and Computation, 2021, Vol. 388, 125464. DOI: 10.1016/j. amc.2020.125464.