基于Mo-RVIKOR的混合多屬性決策方法

摘要： 針對(duì)復(fù)雜性和不確定性多屬性決策問題,考慮定量和定性融合的屬性形式,提出了模塊化隨機(jī)多準(zhǔn)則妥協(xié)解排序法(Modular Random VlseKriterijumska Opti-mizacija I Kompromisno Resenje,Mo-RVIKOR),該方法無需將信息統(tǒng)一,就能處理多種信息形式存在的多屬性決策問題。采用精確數(shù)、隨機(jī)變量處理定量評(píng)價(jià)信息,用概率語義術(shù)語集處理定性評(píng)價(jià)信息;通過改進(jìn)離差最大化法確定屬性權(quán)重;根據(jù)Mo-RVIKOR對(duì)決策對(duì)象進(jìn)行排序;最后以某公司C2B定制化服務(wù)質(zhì)量評(píng)測項(xiàng)目為例,驗(yàn)證了所提方法的有效性。
Hybrid multiple attribute decision making(MADM) problems have broad applications in the fields of economy, management and social science, etc. The existing methods to support hybrid MADM are more and more common which can process many different types of information such as crisp, interval, fuzzy, hesitant fuzzy. However, hybrid information is often converted into the same form which leads to the loss of information in most of these methods. In addition, only few research concerned the uncertainty caused by random variable. Aiming to avoid any transformation step and take random variable into account, Modular Random VlseKriterijumska Opti-mizacija I Kompromisno Resenje(Mo-RVIKOR) is proposed which can break heterogeneous information into modules and process information in a straightforward way without unifying. Firstly, real numbers and random variable are used by experts to process quantitative evaluation information, probabilistic linguistic term set to process qualitative evaluation information. Secondly, the weights of attribute are determined by the improved deviation maximization method. Finally, Mo-RVIKOR is adopted to rank the alternatives. This method can effectively handle certain or uncertain mixed evaluation information, and a case study of C2 B customized service quality assessment of one company is presented to illustrate the effectiveness of the proposed approach.