A progressive sorting approach for multiple criteria decision aiding in the presence of non-monotonic preferences

A progressive sorting approach for multiple criteria decision aiding in the presence of non-monotonic preferences
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一种在存在非单调偏好的情况下辅助多标准决策的渐进排序方法

DOI:
10.1016/j.eswa.2019.01.033
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发表时间:
2019-06
影响因子:
8.5
通讯作者:
Jiapeng Liu
Jiapeng Liu
中科院分区:
计算机科学1区
文献类型:
--
作者:
Mengzhuo Guo;Xiuwu Liao;Jiapeng Liu

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提出了一种新的多准则排序问题的辅助决策方法,该方法考虑了特定准则下的偏好与备选方案评价之间的非单调关系。该方法采用价值函数作为偏好模型,并要求决策者提供参考方案子集的分配示例作为偏好信息。我们假设一个非单调准则的边际值函数在准则的最优水平前是非递减的,然后是非递增的。对于这些非单调准则,该方法从线性增加和减少边际值函数开始,但随后允许这些函数偏离线性并将其转换为更复杂的函数。我们开发了几种算法来帮助DM解决分配示例中的不一致性和分配非参考替代方案。该算法不仅考虑了决策人对偏好的不断进化的认知,而且考虑了满足增量偏好信息的能力和偏好模型的复杂性之间的权衡。DM被引导在每次迭代中评估结果,然后为随后的迭代提供反应,以便所建议的方法支持DM计算出令人满意的偏好模型。通过算例和数值实验验证了该方法的适用性和有效性。
A new decision-aiding approach for multiple criteria sorting problems is proposed for considering the non-monotonic relationship between the preference and evaluations of the alternatives on specific criteria. The approach employs a value function as the preference model and requires the decision maker (DM) to provide assignment examples of a subset of reference alternatives as preference information. We assume that the marginal value function of a non-monotonic criterion is non-decreasing up to the criterion’s most preferred level, and then it is non-increasing. For these non-monotonic criteria, the approach starts with linearly increasing and decreasing marginal value functions but then allows such functions to deviate from the linearity and switches them to more complex ones. We develop several algorithms to help the DM resolve the inconsistency in the assignment examples and assign non-reference alternatives. The algorithms not only incorporate the DM’s evolving cognition of the preference, but also take into account the trade-offs between the capacity for satisfying incremental preference information and the complexity of the preference model. The DM is guided to evaluate the results at each iteration and then provides reactions for the subsequent iterations so that the proposed approach supports the DM to work out a satisfactory preference model. We demonstrate the applicability and validity of the proposed approach with an illustrative example and a numerical experiment.
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