A posterior preference articulation approach to dual-response-surface optimization

In dual-response-surface optimization, the mean and standard deviation responses are often in conflict. To obtain a satisfactory compromise, a Decision Maker (DM)'s preference information on the trade-offs between the responses should be incorporated into the problem. In most existing works, the DM expresses a subjective judgment on the responses through a preference parameter before the problem-solving process, after which a single solution is obtained. This study proposes a posterior preference articulation approach to dual-response-surface optimization. The posterior preference articulation approach initially finds a set of non-dominated solutions without the DM's preference information, and then allows the DM to select the best solution among the non-dominated solutions. The proposed method enables a satisfactory compromise solution to be achieved with minimum cognitive effort and gives the DM the opportunity to explore and better understand the trade-offs between the two responses.