基于二阶段随机规划的城市医疗废弃物回收网络设计

城市医疗废弃物日益增加，且回收需求量受诸多因素的影响，难以准确预测，假定回收需求为确定值的医疗废弃物网络优化设计不能与实际需求相匹配。本文考虑了离散随机参数环境下，医疗回收网络设计中选址规划、分配计划及运输规划的协同优化问题，建立了以选址成本、运输成本最小为目标，设施与车辆能力限制为约束的二阶段随机规划模型。根据模型特点，设计了基于Benders decomposition的求解算法，同时，设计了一系列加速技术用于提高算法的求解效率。最后，以国内某城市医疗回收网络为背景设计算例，检验本文模型和求解策略的可行性和有效性。结果表明：相比确定性规划，随机规划的解能够节约总成本，结合一系列加速技术的Benders decomposition方法比CPLEX与纯的Benders decomposition更有优势。

A Two-stage Stochastic Programming Approach for Urban Medical Waste Recycling Network Design

The urban medical waste demand increases greatly, which is also difficult to be determined accurately with the influence of many factors. Therefore, the model for urban medical waste recycling network design model with deterministic recycling demand might not match the actual demand. The problem that the location,assignment as well as transportation are optimized collaboratively is considered, and a two-stage stochastic programming model is built with minimizing the location cost and transportation cost as well as considering the facility and vehicle capacity constraints. And a benders decomposition algorithm is developed according to the model structure. In addition, a series of acceleration techniques are designed to improve the efficiency of this algorithm. Finally, the feasibility and effectiveness of the proposed model and solution strategy are verified through case studies which based on the certain city in China. The results show that the solution of stochastic programming can save more cost than the deterministic programming, and the benders decomposition method combined with a series of accelerating techniques has more advantages than the CPLEX and pure benders decomposition without any accelerating technique.