论文标题
贝叶斯的偏好引起多目标组合优化
Bayesian preference elicitation for multiobjective combinatorial optimization
论文作者
论文摘要
我们引入了一个新的增量偏好启发程序，能够处理决策者（DM）的嘈杂响应。该贡献的原始性是提出一种贝叶斯方法，用于在涉及一组替代方案组合的多目标决策问题中确定首选解决方案。我们假设DM的偏好由参数未知的聚合函数表示，并且对它们的不确定性在参数空间上以密度函数表示。成对比较查询用于减少这种不确定性（贝叶斯修订版）。查询选择策略基于混合整数线性程序的解决方案，该程序与组合的变量和约束组合，该程序需要使用列和约束生成方法。提供数值测试以显示方法的实用性。
We introduce a new incremental preference elicitation procedure able to deal with noisy responses of a Decision Maker (DM). The originality of the contribution is to propose a Bayesian approach for determining a preferred solution in a multiobjective decision problem involving a combinatorial set of alternatives. We assume that the preferences of the DM are represented by an aggregation function whose parameters are unknown and that the uncertainty about them is represented by a density function on the parameter space. Pairwise comparison queries are used to reduce this uncertainty (by Bayesian revision). The query selection strategy is based on the solution of a mixed integer linear program with a combinatorial set of variables and constraints, which requires to use columns and constraints generation methods. Numerical tests are provided to show the practicability of the approach.
