With the rise of new business processes that require real-time decision making, anticipatory decision making becomes necessary to use the available resources wisely. Dynamic real-time problems occur in many business fields, for example in vehicle routing applications with stochastic customer service requests expecting a fast response. For anticipatory decision making, offline simulation-based optimization methods like value function approximation are promising solution approaches. However, these methods require a suitable approximation architecture to store the value information for the problem states. In this paper, an approach is proposed that finds and adapts this architecture iteratively during the approximation process. A computational proof of concept is presented for a dynamic vehicle routing problem. In comparison to conventional architectures, the proposed method is able to improve the solution quality and reduces the required architecture size significantly.

中文翻译：

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动态决策过程的自适应状态空间分区

随着需要实时决策的新业务流程的兴起，为了明智地使用可用资源，必须做出预期决策。动态实时问题出现在许多业务领域，例如在具有期望快速响应的随机客户服务请求的车辆路由应用中。对于预期决策，价值函数逼近等基于离线模拟的优化方法是很有前途的解决方法。然而，这些方法需要一个合适的近似架构来存储问题状态的值信息。在本文中，提出了一种在逼近过程中迭代地发现和适应这种架构的方法。为动态车辆路线选择问题提供了概念的计算证明。