Optimization and Inventory Management

This book explores inventory models and optimal ordering policies through various optimization techniques, including genetic algorithms and data mining, while addressing real-world scenarios and offering insights for practitioners and researchers.

Optimization and Inventory Management explores various inventory models aimed at establishing optimal ordering policies through a range of optimization techniques, including genetic algorithms and data mining concepts. The book emphasizes the importance of sensitivity analyses to assess the robustness of these models, providing a collection of mathematical solutions tailored to real-world industry scenarios. Each solution is derived using diverse optimization methods to effectively determine the best ordering strategies.

The text delves into a variety of perspectives related to the application of optimization techniques. Topics covered include the effects of inflation, trade credit financing, fuzzy systems, human error, production learning, inspection processes, and the dynamics of green and closed supply chains. Additionally, it addresses advanced concepts such as reworks, game theory, and the implications of big data on inventory management and control. The progression from deterministic inventory models to more sophisticated approaches is a key feature of the book.

Divided into eight comprehensive sections, the book covers inventory control and management, trade credit financing for items of imperfect quality, the environmental impact on ordering policies, and the influence of learning on supply chain models. Other sections focus on EOQ models that consider warehousing, optimal ordering policies utilizing data mining and PSO techniques, fuzzy environment supply chain models, and marketing strategies to understand consumer behavior. This extensive scope makes the book a valuable resource for practitioners, educators, students, and researchers, offering insights that can enhance decision-making and improve business operations.