Adaptive State Space Partitioning for Dynamic Decision Processes

Ninja Söffker, Marlin W. Ulmer, Dirk C. Mattfeld

Veröffentlichungen: Beitrag in FachzeitschriftArtikelPeer Reviewed


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.
Seiten (von - bis)261-275
FachzeitschriftBusiness & Information Systems Engineering
PublikationsstatusVeröffentlicht - 28 Jan. 2019
Extern publiziertJa

ÖFOS 2012

  • 102015 Informationssysteme