Using Excel Solver for Optimization in Operation Research Problems

This research paper explores the effectiveness of Microsoft Excel Solver as a practical tool for solving key Operations Research (OR) problems, specifically the Linear Programming Problem (LPP), Assignment Problem, and Transportation Problem. Through three carefully selected caselets, the study compares results obtained from traditional manual methods such as the Graphical Method, Vogel’s Approximation Method, and Hungarian Method with those generated using Excel Solver. In each case, Solver successfully produced the same optimal solutions as the manual results, demonstrating its computational accuracy and consistency. Beyond accuracy, Excel Solver’s usability, speed, and adaptability in dynamic decision-making environments are commendable. Manual techniques are often time-intensive and mathematically complex, but Excel Solver allows users to model problems with greater efficiency and effectiveness. This makes it particularly useful for business professionals who may not have deep mathematical backgrounds but require practical optimization tools. The research concludes that while traditional methods remain valuable for conceptual understanding, Excel Solver provides a more accessible and scalable solution for real-world applications in logistics, resource allocation, and operational planning. The findings support the integration of Solver into both academic and professional settings as a reliable and user-friendly optimization tool in the field of Operations Research.