摘要
由于城市医疗废弃物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回收路径优化模型不能与实际需求相匹配。在两种不确定性集合的基础上,建立了以总成本最小为目标、带时间窗的、变化单位运营成本的医疗废弃物回收路径鲁棒优化模型。运用"时空+容量"的三维网络图重建模型,设计了基于拉格朗日启发式的求解算法。最后通过实例分析检验本文模型和求解策略的可行性和有效性。结果表明:第I类不确定性集合下的最优解极易趋于最保守解。
Due to the influence of many factors,it is difficult to determine accurate medical waste demand for every medical unit. The model for urban medical waste recycling routing problem with deterministic recycling demand might not match the actual demand well.Therefore,according to two broad classes of uncertainties,a robust optimization model is built with time-windows and varied unit operating cost to minimize the total cost.A reformulation is got through a space-time-capacity network,and a Lagrangian relaxation algorithm is developed based on the model structure.Finally,the feasibility and effectiveness of the proposed model as well as solution strategy are verified through case studies.The results show that the optimal solution of the first uncertainty set tends to be the most conservative solution.
作者
蒲松
夏嫦
PU Song;XIA Chang(School of Economics and Management,Chengdu Technological University,Chengdu 610031,China;School of Economics and Management,University of Electronic Science and Technology of China,Chengdu 611731,China;School of Mathematics and Statistics,Chengdu College of Arts and Sciences,Chengdu 610401,China)
出处
《系统工程》
CSSCI
北大核心
2018年第6期117-123,共7页
Systems Engineering
基金
国家自然科学基金重点项目(71432003)
四川循环经济中心项目(XHJJ-1708)
成都工业学院人才引进科研项目(2017RC021)
关键词
医疗废弃物
回收路径优化
鲁棒优化
拉格朗日松弛
Medical Waste
Recycling Route Optimization
Robust Optimization
Lagrangian Relaxation
