Adaptive State Space Partitioning for Dynamic Decision Processes

Ninja Soeffker (), Marlin W. Ulmer and Dirk C. Mattfeld
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Ninja Soeffker: Technische Universität Braunschweig
Marlin W. Ulmer: Technische Universität Braunschweig
Dirk C. Mattfeld: Technische Universität Braunschweig

Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, 2019, vol. 61, issue 3, No 3, 275 pages

Abstract: Abstract 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.

Keywords: Approximate dynamic programming; Dynamic service routing; State space partitioning; Data-driven modeling and simulation; Simulation-based optimization (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s12599-019-00582-7

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