Synthesis of individual best local priority vectors in AHP-group decision making
Synthesis of individual best local priority vectors in AHP-group decision making
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DOI：
10.1016/j.asoc.2012.11.010
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发表时间：
2013-04
期刊：
影响因子：
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通讯作者：
B. Srdjevic;Z. Srdjevic
中科院分区：
文献类型：
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作者：
B. Srdjevic;Z. Srdjevic
对相关决策者的个人判断和层次分析法生成的优先级向量的评估表明，决策者的个人一致性可能会有显着差异，从而使最终的群体决策不太可靠。本文提出了一种在AHP综合中如何合并联合收割机决策者的局部优先向量，减少群体不一致的方法。而不是聚合个人的判断（AIJ），或聚合个别派生的最终优先级（AIP），我们建议执行一个AHP合成的最佳本地优先向量，从最一致的决策者。在关键词“多准则组优先级综合”之后，我们将该方法和相关算法标记为MGPS。该概念类似于Srdjevic [1]针对单个AHP应用提出的概念，其中基于几种最流行的优先级排序方法的一致性性能来选择最佳局部优先级向量。在这里，决策者被结合起来，而不是优先级的方法，并充分实施组上下文。在完成评估的决策者不一致的层次结构中的每个节点，所选择的最佳的本地优先级向量合成在一个标准的方式，并宣布最终的解决方案是一个AHP组的决定。两个数值例子表明，开发的方法和算法产生的替代品的最终优先级与最低的整体不一致性（在多准则意义上）。
An assessment of the individual judgments and AHP-produced priority vectors for involved decision-makers indicates that the individual consistencies of decision makers may vary significantly, thus making the final group decision less reliable. In this paper, an approach is proposed as to how to combine decision makers’ local priority vectors in AHP synthesis and reduce so-called group inconsistency. Instead of aggregating individual judgments (AIJ), or aggregating individually derived final priorities (AIP), we propose to perform an AHP synthesis of the best local priority vectors taken from the most consistent decision makers. The approach and related algorithm we label as MGPS after the key terms ‘multicriteria group prioritization synthesis.’ The concept is analogous to the one proposed by Srdjevic [1] for individual AHP applications where the best local priority vectors are selected based on the consistency performance of several of the most popular prioritization methods. Here, decision makers are combined instead of prioritization methods, and group context is fully implemented. After completing an evaluation of the decision makers inconsistencies in each node of the hierarchy, the selected best local priority vectors are synthesized in a standard manner, and the final solution is declared to be an AHP-group decision. Two numerical examples indicate that the developed approach and algorithm generate the final priorities of alternatives with the lowest overall inconsistency (in the multicriteria sense).