Extensions of the sequential stochastic assignment problem

Arash Khatibi (), Golshid Baharian, Banafsheh Behzad and Sheldon Jacobson

Mathematical Methods of Operations Research, 2015, vol. 82, issue 3, 317-340

Abstract: The sequential stochastic assignment problem (SSAP) allocates N workers to N IID sequentially arriving tasks so as to maximize the expected total reward. This paper studies two extensions of the SSAP. The first one assumes that the values of any two consecutive tasks are dependent on each other while the exact number of tasks to arrive is unknown until after the final arrival. The second extension generalizes the first one by assuming that the number of workers is also random. Optimal assignment policies for both problems are derived and proven to have the same threshold structure as the optimal policy of the SSAP. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Dynamic programming; Sequential assignment; Stochastic processes; Markov processes (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1007/s00186-015-0516-y (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:mathme:v:82:y:2015:i:3:p:317-340

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/00186

DOI: 10.1007/s00186-015-0516-y

Access Statistics for this article

Mathematical Methods of Operations Research is currently edited by Oliver Stein

More articles in Mathematical Methods of Operations Research from Springer, Gesellschaft für Operations Research (GOR), Nederlands Genootschap voor Besliskunde (NGB)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().