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    ItemOpen Access
    Supply Chain Management: Multi-Echelon Inventory Optimization
    (Suleyman Demirel University, 2007) Bahitcan Eshenkulov; Tolkynbek Nurgaliyev
    Effective management of inventory is essential for ensuring smooth operations in manufacturing, distribution, and retail enterprises. Multi-echelon supply chains, which include several interconnected storage and distribution stages such as regional distribution centers and local warehouses, require more complex coordination than single-echelon systems. Traditional approaches often treat each level independently, resulting in excess safety stocks, unreliable demand forecasting, and inconsistent service levels. These issues contribute to the bullwhip effect, where slight changes in customer demand lead to amplified variations across the supply chain. A true multi-echelon inventory optimization approach integrates the management of all echelons simultaneously. It focuses on minimizing total inventory while ensuring target service levels, improving visibility across the demand chain, accounting for lead time variability, and synchronizing replenishment decisions. By adopting such a coordinated strategy, enterprises can achieve reduced working capital, improved customer satisfaction, and better supplier performance across the entire network.
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    Calculus : brief summary and exercises. Part 1
    (Almaty, Suleyman demirel university - 2005, 2005) Nartova D.S.
    Abstract. The "Calculus: Theory and Practice" course is a comprehensive introduction to the fundamental branch of mathematics known as calculus. This abstract provides an overview of the course's key components, which include both theoretical concepts and practical exercises. The course begins by presenting the core principles of calculus, starting with the concept of limits, derivatives, and integrals. Students will delve into the mathematical foundations that underpin this discipline, understanding how calculus provides the tools to analyze and describe the behavior of functions, curves, and change itself. In the practical aspect of the course, students will engage in a wide range of exercises and problems that require them to apply calculus techniques to real-world scenarios. These exercises will cover differentiation, integration, optimization, and related rates, among other topics. Through these exercises, students will develop critical problem-solving skills and gain the ability to model and solve complex, dynamic situations.
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    Development and optimization of physics-informed neural networks for solving partial differential equations
    (SDU University, 2025) Sharimbayev B.
    This thesis talks about using physics-informed neural networks (PINNs) to solve Poisson equations in both one-dimensional and two-dimensional areas. These equations are common in many physical problems, like heat transfer and electrostatics. The results from PINNs are compared to the finite difference method (FDM), which is a classical numerical method often used to solve these kinds of equations. The study shows that PINNs can give results that are close to those from FDM, with the added benefit of being more flexible for different types of problems. Another part of this work focuses on using multi-task learning with PINNs. In this part, the neural network does more than one job. It not only finds the solution of the differential equation, but it also learns unknown values or parameters that are part of the equation. For example, in one test problem, the equation had a source term and a coefficient that changes depending on the position. The PINN was able to learn both of them correctly while still solving the equation with a low training error. The results show that PINNs can work well even when the equation is more complex or has unknown parts. The model showed good performance on new or unseen data and was able to find the correct hidden values in the system. Because of this, PINNs may be very useful in future applications for solving advanced problems in science and engineering, especially where traditional methods might be harder to use.