A model for optimizing plans for procurement of raw materials from regions of Russia in a timber-processing enterprise

Abstract

In this paper a model for the formation of sustainable supply chains of raw materials for a timber processing complex is proposed. The model allows one to optimize the plan of purchases from the Russian Commodity Exchange, as well as the plan of manufacturing finished products. The model presents the task of mathematical programming, whereby the company’s profit is used as the objective function, and the input data include the forecasted values of structure and volumes of offers available on the Russian Commodity Exchange, as well as demand for finished products. The recurrence dependencies of the model describe the flow of raw materials at the enterprise’s warehouse, taking into account revenues from purchased lots, transportation time and consumption of resources that are required for production of simulated volumes of products. Constraints of the model represent formalization of the limited flow of financial resources, taking into account sales and warehouse characteristics. The optimization task deals with variables including volumes of daily output of finished products according to a given nomenclature, as well as variables that specify the inclusion of lots into the portfolio of applications purchased on the exchange. The model solution is found using the branch and bound method with preliminary clipping based on the modified Chvatal–Gomory method. One example considers formation of optimal plans for the purchase and sales in a timber processing complex located in the Primorsky Territory (Russia), which does not have its own forest plots providing production with raw materials. The usefulness of the interaction of the enterprise with the timber department of the commodity and raw materials exchange is assessed.

A mathematical model for the formation of the pricing policy and the plan of the production and transport system in a timber-processing enterprise

“…As you can see from the figure, the profit value changed nonmonotonously. This is because at each step random variables were played (20,21,(24)(25)(26) and -products demand noise component. The last element especially strongly influenced the final value of the volume of demand for the products, which, in turn, was reflected in the profit indicator.…”

“…Formula (19) shows that the price can change by no more than . Formulas (20)(21)(22)(23)(24)(25)(26) reflect the stochastic nature of the changes, in other words, for each realization of such random variables, the optimal or suboptimal solution of the system will be determined.…”

“…The demand value is calculated using (18,19,23) (if you reapply stage 1, the next iteration will use the price values obtained in the previous iteration of stage 2). The values from expressions (20)(21)(22)(23)(24)(25)(26) are played every iteration. The search for a solution to the problem is carried out using the Gomori algorithm included in the system MathWorks 5 .…”

“…In order to speed up the process of finding solutions, we deliberately did not impose an integer constraint on the z variables. This will not affect the correctness of the solution of the problem, because in the theory of the transport problem, it is proved that the solution of such a subproblem is always integer [21].…”

Research on Supply Chain Efficiency in COVID-19: The Role of Information and Communications

“…Индустрия 4.0 является стимулом для автоматизации отраслей, внедряя электронный обмен данными в управление цепочкой поставок [12][13][14], тем самым способствуя улучшению управления глобальной SC, особенно в условиях сбоев [12,15]. Подход Lean Six Sigma в глобальной транспортной системе с использованием Индустрии 4.0 и интернета вещей создает идеальный технологический процесс, который является высокооптимизированным, а также совершенным и свободным от дефектов и потерь.…”