文章基本信息

标题：Adaptive State Space Partitioning for Dynamic Decision Processes
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作者：Soeffker, Ninja ; Soeffker, Ninja ; Ulmer, Marlin W. 等
期刊名称：Business & Information Systems Engineering
出版年度：2019
卷号：61
期号：3
页码：261-275
DOI：10.1007/s12599-019-00582-7
出版社：Association for Information Systems
摘要：With the rise of newbusiness 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.
关键词：Approximate dynamic programming; Dynamic service routing; State space partitioning; Datadriven modeling and simulation; Simulation-based optimization 1 Introduction