A Novel Approach to Optimizing Assignment Problems in Business Economics Strategies

Abstract

The assignment problem is a fundamental optimization challenge in business economics, where resources or tasks must be allocated efficiently to minimize costs or maximize productivity. Traditional methods, such as the Hungarian algorithm, provide optimal solutions but fail to incorporate dynamic business constraints such as fluctuating economic conditions, priority-based assignments, and fairness considerations. This paper proposes a novel approach that integrates priority-based assignment modelling, a dynamic cost adjustment mechanism, and an iterative refinement process to enhance flexibility and efficiency. By incorporating these elements, the proposed method allows for real-time adaptability and strategic decision-making, addressing limitations in conventional optimization techniques. To validate the effectiveness of the novel approach, a numerical example involving product distribution in a retail business is analysed. The results demonstrate that the proposed method reduces total costs compared to the Hungarian algorithm while improving adaptability to economic fluctuations. The comparative analysis highlights its superiority in terms of cost reduction, flexibility, and business-specific constraints. The findings indicate that this approach can be applied to various business economics strategies requiring optimal resource allocation. Future research can explore further refinements, including machine learning-based optimization techniques, to enhance decision-making processes in assignment problems. By improving efficiency and adaptability, this novel approach contributes to the advancement of assignment problem-solving methods in business economics