caa a22 4500 463202864 CHVBK 20180405153117.0 cr unu---uuuuu 170326e20070401xx s 000 0 eng 10.1007/s10732-006-9006-y doi (NATIONALLICENCE)springer-10.1007/s10732-006-9006-y Reinforcement learning versus heuristics for order acceptance on a single resource [Elektronische Daten] [M. Hing, A. Harten, P. Schuur] Order Acceptance (OA) is one of the main functions in business control. Accepting an order when capacity is available could disable the system to accept more profitable orders in the future with opportunity losses as a consequence. Uncertain information is also an important issue here. We use Markov decision models and learning methods from Artificial Intelligence to find decision policies under uncertainty. Reinforcement Learning (RL) is quite a new approach in OA. It is shown here that RL works well compared with heuristics. It is demonstrated that employing an RL trained agent is a robust, flexible approach that in addition can be used to support the detection of good heuristics. Springer Science+Business Media, LLC, 2006 Order acceptance nationallicence Opportunity costs nationallicence Decisions under uncertainty nationallicence Markov decision process nationallicence Reinforcement learning nationallicence Artificial neural networks nationallicence Hing M. School of Business, Public Administration and Technology, University of Twente, Enschede, The Netherlands aut Harten A. School of Business, Public Administration and Technology, University of Twente, Enschede, The Netherlands aut Schuur P. School of Business, Public Administration and Technology, University of Twente, Enschede, The Netherlands aut Journal of Heuristics Kluwer Academic Publishers-Plenum Publishers; http://www.springer-ny.com 13/2(2007-04-01), 167-187 1381-1231 13:2<167 2007 13 10732 https://doi.org/10.1007/s10732-006-9006-y text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10732-006-9006-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Hing M. School of Business, Public Administration and Technology, University of Twente, Enschede, The Netherlands aut NATIONALLICENCE 700 1- Harten A. School of Business, Public Administration and Technology, University of Twente, Enschede, The Netherlands aut NATIONALLICENCE 700 1- Schuur P. School of Business, Public Administration and Technology, University of Twente, Enschede, The Netherlands aut NATIONALLICENCE 773 0- Journal of Heuristics Kluwer Academic Publishers-Plenum Publishers; http://www.springer-ny.com 13/2(2007-04-01), 167-187 1381-1231 13:2<167 2007 13 10732 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer