Deepseek-V3 and Qwen2.5-Max in Solving Resource and Process Optimization Problems

Abstract

The digital world is invaded by a wide range of products that use Artificial Intelligence (AI) technology to provide services of various types. Developers come up with new products that always arouse both admiration and criticism. AI is gradually replacing repetitive and mechanical activities with automated and intelligent processes. This is achieved through sophisticated algorithms, machine learning, and other technologies that enable systems to make decisions, analyze data, and perform tasks that previously required human intervention. AI replaces repetitive and predictable activities, but cannot, at the current stage, completely replace creativity, empathy, or deep critical thinking. Collaboration between humans and machines in some areas is more effective than completely replacing human activity. A widely used AI product is chatbots, which are trained on a huge amount of data, which allows them to understand and generate natural language in a variety of contexts. They are useful for answering questions, performing translations, generating texts, recommendations, friendly conversations, and last but not least, they are learning assistants that can help users understand complex concepts or solve problems. The goal of the paper is to analyze the results provided by DeepSeek-V3 and Qwen2.5-Max models for complex academic problems in the field of Operational Research. The problems analyzed include activity planning, inventory management, and queuing systems analysis. CZU: 004.773:004.8; JEL: C02, C44, C50, C51, C65