pMachine-DM: A Stochastic Machine Learning-Based Decision-Maker for Interactive Multi-Criterion Decision-Making

Abstract

Interactive multi-criterion decision-making (iMCDM) procedures allow a humam decision-maker (DM) to iteratively adjusts preferences—via objective classification, reference points/directions, or weights—and solves a scalarized problem until a satisfactory Paretooptimal solution is obtained. However, benchmarking a iMCDM procedure remains challenging due to involvement of human DM in making decisions resulting in different solutions. To address this challenge, this paper proposes a stochastic machine learning-based decision-maker: pMachine-DM, which emulates human preference articulation using two stochastic ANNs. The first ANN classifies all objectives according to their desired improvement, relaxation, or satisfaction, while the second ANN predicts the associated bounding parameters. The proposed pMachine-DM is implemented with a specific iMCDM procedure - STEP method. The effectiveness of the approach is demonstrated on four benchmark and four engineering problems. The proposed pMachine-DM is generic and can be integrated with other iMCDM procedures, enabling systematic and reproducible benchmarking without involving human DMs.