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Effect of the Profile of the Decision Maker in the Search for Solutions in the Decision-Making Process

机译:决策者概况在决策过程中寻找解决方案中的影响

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Many real-world optimization problems involving several conflicting objective functions frequently appear in current scenarios and it is expected they will remain present in the future. However, approaches combining multi-objective optimization with the incorporation of the decision maker’s (DM’s) preferences through multi-criteria ordinal classification are still scarce. In addition, preferences are rarely associated with a DM’s characteristics; the preference selection is arbitrary. This paper proposes a new hybrid multi-objective optimization algorithm called P-HMCSGA (preference hybrid multi-criteria sorting genetic algorithm) that allows the DM’s preferences to be incorporated in the optimization process’ early phases and updated into the search process. P-HMCSGA incorporates preferences using a multi-criteria ordinal classification to distinguish solutions as good and bad; its parameters are determined with a preference disaggregation method. The main feature of P-HMCSGA is the new method proposed to associate preferences with the characterization profile of a DM and its integration with ordinal classification. This increases the selective pressure towards the desired region of interest more in agreement with the DM’s preferences specified in realistic profiles. The method is illustrated by solving real-size multi-objective PPPs (project portfolio problem). The experimentation aims to answer three questions: (i) To what extent does allowing the DM to express their preferences through a characterization profile impact the quality of the solution obtained in the optimization? (ii) How sensible is the proposal to different profiles? (iii) How much does the level of robustness of a profile impact the quality of final solutions (this question is related with the knowledge level that a DM has about his/her preferences)? Concluding, the proposal fulfills several desirable characteristics of a preferences incorporation method concerning these questions.
机译:许多涉及几个相互冲突的客观函数的真实优化问题经常出现在当前的情景中,预计将来将仍然存在。然而,通过多标准序数分类结合决策者(DM)偏好结合多目标优化的方法仍然是稀缺的。此外,偏好很少与DM特征相关联;偏好选择是任意的。本文提出了一种名为P-HMCSGA(偏好混合多标准分类遗传算法)的新的混合多目标优化算法,其允许DM的偏好结合在优化过程的早期阶段并更新到搜索过程中。 P-HMCSGA使用多标准序数分类来结合偏好,以将解决方案区分为好坏;其参数用偏好分类方法确定。 P-HMCSGA的主要特征是建议将偏好与DM的特征简档相关联的新方法及其与序数分类的集成。这使得与在现实简介中指定的DM的偏好方面更加增加了对所需感兴趣区域的选择性压力。通过解决实际大小的多目标PPP(项目组合问题)来说明该方法。实验旨在回答三个问题:(i)在多大程度上允许DM通过表征曲线表达他们的偏好,影响优化中获得的解决方案的质量? (ii)对不同型材的提议有多明智? (iii)轮廓的稳健性程度影响了最终解决方案的质量多少(这个问题与DM关于他/她的偏好有关)?结论,该提案履行了关于这些问题的偏好掺入方法的若干所需特征。

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