论文标题
公用事业驱动的工作选择问题
Utility Driven Job Selection Problem on Road Networks
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
In this paper, we study the problem of \textsc{Utility Driven Job Selection} on Road Networks for which the inputs are: a road network with the vertices as the set of Point-Of-Interests (Henceforth mentioned as POI) and the edges are road segments joining the POIs, a set of jobs with their originating POI, starting time, duration, and the utility. A worker can earn the utility associated with the job if (s)he performs this. As the jobs are originating at different POIs, the worker has to move from one POI to the other one to take up the job. Some budget is available for this purpose. Any two jobs can be taken up by the worker only if the finishing time of the first job plus traveling time from the POI of the first job to the second one should be less than or equal to the starting time of the second job. We call this constraint as the temporal constraint. The goal of this problem is to choose a subset of the jobs to maximize the earned utility such that the budget and temporal constraints should not be violated. We present two solution approaches with detailed analysis. First one of them works based on finding the locally optimal job at the end of every job and we call this approach as the \emph{Best First Search Approach}. The other approach is based on the Nearest Neighbor Search on road networks. We perform a set of experiments with real\mbox{-}world trajectory datasets to demonstrate the efficiency and effectiveness of the proposed solution approaches. We observe that the proposed approaches lead to more utility compared to baseline methods.
