不确定性条件下的设备替换优化的 随机动态规划方法

本文提出了一种解决设备更新换代优化（ERO）问题的随机动态规划（SDP）模 型，用以明确地解释在车辆利用中的不确定性，并采用 Bellman 算法解决 ERO SDP 问题. 针对 SDP 状态空间的增长，提出了特殊简化算法，以解决动态规划方法中固有的“维数 灾”问题，确保所需的内存和计算时间不会随着时间范围的增加而成倍增长.并对 SDP 软 件的实现技术、功能和图形用户界面（GUI）进行了讨论，开发了基于 SDP 的 ERO 软件， 并使用美国得克萨斯交通局（TxDOT）现有车辆数据进行验证.对统计结果、软件计算时 间和求解效果进行综合分析，结果显示，使用该 ERO 软件，估计大量成本可以节省.

A Stochastic Dynamic Programming Approach for the Equipment Replacement Optimization under Uncertainty

In this paper, a stochastic dynamic programming (SDP) based optimization model is formulated for the equipment replacement optimization (ERO) problem that can explicitly account for the uncertainty in vehicle utilization. The Bellman approach is developed and implemented to solving the ERO SDP problem. Particular attention is paid to the SDP state-space growth and special scenario reduction techniques are devel- oped to resolve the“curse of dimensionality”issue that is inherent to the dynamic programming method to ensure that the computer memory and solution computational time required will not increase exponentially with the increase in time horizon. SDP software computer implementation techniques, functionalities and the Graphical User Interfaces (GUI) are discussed. The developed SDP-based ERO software is tested and validat- ed using the current Texas Department of Transportation (TxDOT) vehicle fleet data. Comprehensive numeri- cal results, such as statistical analyses, the software computational time and solution quality, are described and substantial cost-savings have been estimated by using this ERO software.