Hybrid heuristic and mathematical programming in oil pipelines networks： Use of immigrants

We solve the problem of petroleum products distribution through oil pipelines networks. This problem is modelled and solved using two techniques: A heuristic method like a multiobjective evolutionary algorithm and Mathematical Programming. In the multiobjective evolutionary algorithm, several objective functions are defined to express the goals of the solutions as well as the preferences among them. Some constraints are included as hard objective functions and some are evaluated through a …     

基于遗传算法的自动化集装箱码头多载AGV调度

为提升自动化集装箱码头的作业效率,减轻码头吞吐量增大带来的交通问题,降低自动化导引小车（Automated Guided Vehicle, AGV)的空载率,在自动化集装箱码头应用可以同时搬运不止一个集装箱的多载AGV,建立多载AGV调度问题的混合整数线性规划（Mixed Integer Linear Programming, MILP)模型,应用遗传算法进行求解.借助算例,对比遗传算法与MILP算法的求解效果,分析交叉概率和变异概率对遗传算法的影响,比较多载AGV与单载AGV的作业时间,验证遗传算法的可靠性.该方法表明,遗传算法不仅求解效率高,而且对MILP算法不适用的大、中型多载AGV调度问题,也能给出值得信赖的近似最优解.     

基于混合整数线性规划的救护车（组）定点前置模型研究

在假定居民所在位置不变的前提下,增加救护车（组）前置点能减少救护车到急救需求点的运行时间,但是整个急救医疗机构的管理费用和成本也会相应增加。我国城市急救医疗体系虽已初步建成,然而在一定时期内急救医疗资源仍严重不足,故大幅度增加救护车（组）前置点,增加救护车数量的难度较大。因此合理选择救护车（组）前置定点位置,优化现有资源配置是短时期内提高急救服务水平的最有效途径。文章旨在通过数据汇集法提高居民急救需求预测的准确率,借助混合整数线性规划建立救护车（组）前置点多源选址模型,并根据急救资源数量在备选地点中确定最优前置点。     

Detection of gross errors using mixed integer optimization approach in process industry

A novel mixed integer linear programming （NMILP） model for detection of gross errors is presented in this paper. Yamamura et al.（1988） designed a model for detection of gross errors and data reconciliation based on Akaike information criterion （AIC）. But much computational cost is needed due to its combinational nature. A mixed integer linear programming （MILP） approach was performed to reduce the computational cost and enhance the robustness. But it loses the super performance of maximum likelihood estimation. To reduce the computational cost and have the merit of maximum likelihood estimation, the simultaneous data reconciliation method in an MILP framework is decomposed and replaced by an NMILP subproblem and a quadratic programming （QP） or a least squares estimation （LSE） subproblem. Simulation result of an industrial case shows the high efficiency of the method.