Reinforcement learning versus heuristics for order acceptance on a single resource

M. Mainegra Hing, Aart van Harten, Peter Schuur

Research output: Contribution to journalArticleAcademicpeer-review

17 Citations (Scopus)
7 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)167-187
Number of pages21
JournalJournal of heuristics
Issue number2
Publication statusPublished - 2007


  • IR-95694
  • METIS-244116


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