A MapReduce-Based Decision-Making Approach for Multiple Criteria Sorting

In the context of big data and artificial intelligence, analyzing and extracting actionable insights from extensive datasets to enhance decision-making processes presents both intriguing opportunities and formidable challenges. Existing multiple criteria sorting (MCS) methodologies often struggle with the magnitude of these datasets, particularly in terms of time and memory requirements. Furthermore, traditional approaches typically rely on direct preference information, which can be cognitively demanding for decision-makers and may not scale effectively with increasing data complexity. This study introduces a scalable MCS approach grounded in the MapReduce framework, designed to handle extensive sets of alternatives and preference information in a parallel processing paradigm. The proposed approach utilizes an additive piecewise-linear value function as the underlying preference model, with model parameters inferred from assignment examples on a subset of reference alternatives through the application of preference disaggregation principles. To enable the parallel execution of the sorting procedure, a convex optimization model is formulated to estimate the parameters of the preference model. Subsequently, a parallel algorithm is devised to solve this optimization model, leveraging the MapReduce framework to process the set of reference alternatives and associated preference information concurrently, thereby accelerating computational efficiency. Additionally, the performance of the proposed approach is evaluated using a real-world dataset and a series of synthetic datasets comprising up to 400,000 alternatives. The findings demonstrate that this approach effectively addresses the MCS problem in the context of large sets of alternatives and extensive preference information.