A Review of Task Multi-criteria Prioritization Methods

Abstract

Multi-criteria priority ranking involves determining priorities through calculations for a multitude of tasks as per potentially conflicting criteria, to aid decision-makers in efficient allocation of resources and prioritization of tasks based on their criticality. This process is essential for optimizing resource allocation and improving overall work efficiency. Over the past two decades, the field of priority ranking has seen the development of numerous mature classic methods through continuous modifications and expansions. Furthermore, new sorting methods rooted in advancing technologies in the era of big data have also emerged. To assist decision-makers in evaluating and selecting the most suitable multi-criteria prioritization methods for priority ranking needs in specific application scenarios, this paper first elucidates differences across various multi-criteria prioritization methods regarding criteria substitutability, advantages and disadvantages, data input/output requirements, and applicable scenes. It further categorizes existing multi-criteria task prioritization methods into four main types: full aggregation methods, inferiority ranking methods, reference level methods, and data processing-based prioritization methods. Additionally, it discusses in detail the basic principles, implementation steps, latest research findings, and pros and cons of representative methods within each category. Finally, this paper summarizes current research hotspots, including the analytic hierarchy process, fuzzy theory, and AI-assisted ranking technologies, while suggesting future research pathways covering AI-assisted big data ranking optimization, ranking stability enhancement, and intelligent preference identification.

Keywords

• priority ranking
• multi-criteria
• full aggregation methods
• outranking methods
• reference level method
• machine learning