Multi-Objective Robust Optimization for Medical Waste Recycling Location Problem with Operational Risks
In order to avoid the uncertain risk under public health emergencies,this paper studies the location of nodes such as recovery centers,treatment centers,and disposal centers in the medical waste recovery network.With the goal of minimizing total cost and total risk,a multi-objective robust optimization model for medical waste recycling location considering operational risk is constructed.A nondominated ranking genetic algorithm is designed,in which a p-robust iterative operator is proposed to solve the p-value lower bound,and the roulette selection,elite strategy,uniform crossing and reverse mutation is adopted.Based on the simulation case,the Pareto solution is obtained by solving the deterministic model and the robust optimization model.The model comparison results show that the robust optimization model is suitable for all scenarios,and the relative cost regret value is less than 2％,which can effectively cope with the change of facility site selection caused by parameter uncertainty.The sensitivity analysis of the p-value shows that when 0.004＜p＜0.08,the quality of the solution increases with the increase of the p-value.The closer the p-value is to the lower bound of 0.004,the faster the target value decreases,and the more suitable it is for emergency response.At the same time,the degree of risk appetite and total cost of decision-makers have an important impact on the layout of facilities,and the two need to be comprehensively weighed.
medical wastesmulti-object programminglocation modelNAGA-Ⅱrobust optimization
万方数据
维普
