<![CDATA[Existing research and practical applications of multi-objective optimization in this domain continue to rely mainly on manual Pareto analysis. Typically, decision makers analyze trade-off curves or a collection of candidate solutions before making subjective configuration choices. This method is time-consuming, difficult to replicate, and subject to bias or inconsistency among evaluators. Furthermore, many publications stop at creating the Pareto front without giving a systematic mechanism for automated selection or assessing the effectiveness of the produced front in comparison to alternative tactics. Data for fast-moving product categories with high profit margins can be processed in a computerized application. These two parameters will provide the best recommendations according to the Pareto principle, which states that 80% of the best income comes from 20% of sources. Pareto Method optimization has proven to narrow the focus of work on the parts that have a significant effect (benefit) for the pharmacy. The manual process used before the research was conducted resulted in one item recommendation in 6 minutes and 20 seconds, while the computerized DSS could process a large amount of item data in just 3 minutes and 15 seconds, with an average gross profit for the top 10 recommended items of 32.1%. This study presents an automated Pareto optimization and selection methodology, which eliminates the need for manual inspection. The system not only creates candidates for Pareto-optimal solutions, but also ranks and selects them based on quantitative criteria. In addition, the framework includes comparative benchmarking, which allows for performance evaluation against baseline methodologies, heuristics, or existing decision procedures. This results in an objective, repeatable, data-driven decision pipeline.]]>
