任务多标准优先级排序方法综述

A Review of Task Multi-criteria Prioritization Methods

任务的多标准优先级排序(multi-criteria priority ranking)涉及在可能存在冲突的多个标准下为大量任务计算优先级,以便决策者能够高效地分配资源,优先处理更为关键的任务,进而提高整体工作效率。经过近二十年的持续修改与扩展,目前优先级排序领域已展现众多成熟的经典方法;且随着大数据时代的到来,采用新兴技术的排序方法也开始涌现。为了帮助决策者根据不同情境下的优先级排序需求来评估和选择最适合特定应用场景的多标准优先级排序方法,文章阐释了不同多标准优先级排序方法在标准间的可替代性、优缺点、数据输入/输出要求、适用场景等方面的差异,将当前多标准任务优先级排序方法分为:全聚合法、劣势排序法、参考水平法以及基于数据处理技术的优先级排序方法4大类,详细讨论了各类别下代表性方法的基本原理、最新研究结果和优缺点;并总结了当前优先级排序方法研究的热点,包括层次分析法、模糊理论及人工智能辅助排序技术,提出其未来研究方向,涵盖了AI辅助的大数据排序优化、增强排序稳定性以及智能偏好识别。

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.