Multiple Criteria Decision Analysis (MCDA) is a subfield of Operational Research that aims to support Decision-Makers (DMs) in the decision-making process through mathematical models and computational procedures. In this perspective, MCDA employs structured and traceable protocols to identify potential actions and the criteria for evaluating them. MCDA procedures aim to define recommendations consistent with the preferences of DMs for the specific decision problem at hand. These problems are generally formulated in terms of either choosing the best action, classifying actions into pre-defined and ordered decision classes, or ranking actions from best to worst. As the evaluation criteria are generally conflicting, the main challenge is to aggregate them into a mathematical preference model representing the DM value system. We review the development of MCDA over the past fifty years and describe its evolution with examples of distinctive methods. They are distinguished by the type of preference information elicited by DMs, the type of the preference model (criteria aggregation), and the way of converting the preference relation induced by the preference model in the set of potential actions into a decision recommendation. We focus on MCDA methods with a finite set of actions. References to specific application areas will be given. In the conclusion section, some prospective avenues of research will be outlined.

中文翻译：

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五十年多标准决策分析：从经典方法到稳健序数回归


多标准决策分析 (MCDA) 是运筹学的一个子领域，旨在通过数学模型和计算程序在决策过程中支持决策者 (DM)。从这个角度来看，MCDA 采用结构化且可追踪的协议来识别潜在的行动以及评估这些行动的标准。 MCDA 程序旨在针对当前的具体决策问题定义与 DM 偏好一致的建议。这些问题通常通过选择最佳操作、将操作分类为预定义和有序的决策类别或从最佳到最差对操作进行排序来表述。由于评估标准通常是相互冲突的，因此主要的挑战是将它们聚合成代表DM价值体系的数学偏好模型。我们回顾了 MCDA 在过去五十年的发展，并通过独特方法的例子描述了它的演变。它们的区别在于DM引发的偏好信息的类型、偏好模型的类型（标准聚合）以及将潜在动作集中的偏好模型引发的偏好关系转换为决策推荐的方式。我们专注于具有有限动作集的 MCDA 方法。将给出具体应用领域的参考。在结论部分，将概述一些前瞻性的研究途径。