Computational Bounds for Elevator Control Policies by Large Scale Linear Programming

URN to cite this document: urn:nbn:de:bvb:703-opus4-13787

Title data

Heinz, Stefan ; Rambau, Jörg ; Tuchscherer, Andreas:
Computational Bounds for Elevator Control Policies by Large Scale Linear Programming.
Bayreuth , 2013 . - 29 S.

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Abstract

We computationally assess policies for the elevator control problem by a new column-generation approach for the linear programming method for discounted infinite-horizon Markov decision problems. By analyzing the optimality of given actions in given states, we were able to provably improve the well-known nearest-neighbor policy. Moreover, with the method we could identify an optimal parking policy. This approach can be used to detect and resolve weaknesses in particular policies for Markov decision problems.

Further data

Item Type:	Preprint, postprint
Additional notes (visible to public):	msc: 90-XX Dies ist eine revidierte Fassung von urn:nbn:de:bvb:703-opus-8615, die in Mathematical Methods of Operations Research erscheint und online bereits unter doi:10.1007/s00186-013-0454-5 verfügbar ist.
Keywords:	Operations Research; column generation; performance guarantee; Markov decision problem; bounds; large scale
DDC Subjects:	500 Science
Institutions of the University:	Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics Faculties Faculties > Faculty of Mathematics, Physics und Computer Science
Language:	English
Originates at UBT:	Yes
URN:	urn:nbn:de:bvb:703-opus4-13787
Date Deposited:	24 Apr 2014 14:37
Last Modified:	28 Mar 2019 10:22
URI:	https://epub.uni-bayreuth.de/id/eprint/109

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