Case Study: An Intelligent Decision-Support System

Abstract

Information technology applications that support decision-making processes and problem-solving activities have proliferated and evolved. Distributing used cars to various automobile auctions is a complicated problem with multiple variables. We developed a software system to address these complexities and implemented it on a real distribution problem for a large car manufacturer. The system detects data trends in a dynamic environment, incorporates optimization modules to recommend a near-optimum decision, and includes self-learning modules to improve future recommendations. A software system that combines prediction, optimization, and adaptation techniques has generated impressive profits for a large auto manufacturer.

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