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.
Soeffker, Ninja; Ulmer, Marlin W.; and Mattfeld, Dirk C.
"Adaptive State Space Partitioning for Dynamic Decision Processes,"
Business & Information Systems Engineering:
Vol. 61: Iss. 3, 261-275.
Available at: https://aisel.aisnet.org/bise/vol61/iss3/3