Order acceptance under uncertainty: a reinforcement learning approach

Marisela Mainegra Hing

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

17 Downloads (Pure)

Abstract

In this thesis, we study Order Acceptance (OA) problems under uncertainty and their solutions using Reinforcement Learning (RL). OA is an essential business problem that has not been extensively studied in this respect. Orders with different characteristics arrive stochastically at a processing facility; based on expected total profit a decision has to be made whether or not to accept an incoming order. Unaccepted orders are lost forever. RL is a promising approach that combines the ideas of modeling uncertainty and solving the problem of incomplete information (using learning methods). The idea of modeling uncertainty is based on modeling semi-Markov decision problems (SMDP).
Original languageUndefined
Awarding Institution
  • University of Twente
Supervisors/Advisors
  • van Harten, Aart, Supervisor
  • Schuur, Peter Cornelis, Supervisor
Award date7 Sep 2006
Place of PublicationEnschede
Publisher
Print ISBNs9789036524056
Publication statusPublished - 2006

Keywords

  • IR-57122

Cite this

Hing, M. M. (2006). Order acceptance under uncertainty: a reinforcement learning approach. Enschede: University of Twente.
Hing, Marisela Mainegra. / Order acceptance under uncertainty: a reinforcement learning approach. Enschede : University of Twente, 2006. 180 p.
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Hing, MM 2006, 'Order acceptance under uncertainty: a reinforcement learning approach', University of Twente, Enschede.

Order acceptance under uncertainty: a reinforcement learning approach. / Hing, Marisela Mainegra.

Enschede : University of Twente, 2006. 180 p.

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

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T1 - Order acceptance under uncertainty: a reinforcement learning approach

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AB - In this thesis, we study Order Acceptance (OA) problems under uncertainty and their solutions using Reinforcement Learning (RL). OA is an essential business problem that has not been extensively studied in this respect. Orders with different characteristics arrive stochastically at a processing facility; based on expected total profit a decision has to be made whether or not to accept an incoming order. Unaccepted orders are lost forever. RL is a promising approach that combines the ideas of modeling uncertainty and solving the problem of incomplete information (using learning methods). The idea of modeling uncertainty is based on modeling semi-Markov decision problems (SMDP).

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M3 - PhD Thesis - Research UT, graduation UT

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Hing MM. Order acceptance under uncertainty: a reinforcement learning approach. Enschede: University of Twente, 2006. 180 p.