An interactive optimisation framework for incorporating a broader range of human feedback into stochastic multi-objective mixed integer linear programs
提出一种交互式优化框架，允许用户回应多种查询，通过蒙特卡洛方法将人类反馈转化为随机多目标混合整数线性规划模型的输入，并用条件风险价值平衡期望表现与风险容忍度，在供应商选择问题上缩小现实差距并收敛到真实解。
Interactive optimisation (IO) combines the analytical power of optimisation frameworks with human’s contextual expertise. However, prior IO approaches require human users to repeatedly provide the same type of input or directly modify the model to incorporate different information. As a result, IO frameworks elicit a narrow range of human knowledge or require substantial optimisation expertise from users. To address these limitations, an IO framework is proposed that allows human users to respond to multiple types of queries. The framework aims to produce higher-fidelity stochastic multi-objective mixed-integer linear programming models. It employs targeted questions to elicit specific information from users, a Monte Carlo-based framework to transform human responses into input data for a scenario-based optimisation model formulation, and uses a Conditional Value at Risk (CVaR) formulation to balance expected performance with risk tolerances. Computational experiments on a supplier selection problem demonstrate that this framework can narrow the reality gap and converge towards the ground-truth solution. Moreover, it dynamically adapts to user feedback, and when the human expresses insufficient confidence in the solution’s performance, it can recommend solutions with narrower performance confidence intervals.