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Distributionally robust multi-item newsvendor problems with multimodal demand distributions

Overview of attention for article published in Mathematical Programming, April 2014
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Mentioned by

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1 tweeter

Citations

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23 Dimensions

Readers on

mendeley
42 Mendeley
Title
Distributionally robust multi-item newsvendor problems with multimodal demand distributions
Published in
Mathematical Programming, April 2014
DOI 10.1007/s10107-014-0776-y
Authors

Grani A. Hanasusanto, Daniel Kuhn, Stein W. Wallace, Steve Zymler

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 33%
Student > Master 8 19%
Student > Doctoral Student 5 12%
Professor 4 10%
Researcher 4 10%
Other 7 17%
Readers by discipline Count As %
Engineering 12 29%
Mathematics 7 17%
Unspecified 6 14%
Economics, Econometrics and Finance 5 12%
Business, Management and Accounting 4 10%
Other 8 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 July 2015.
All research outputs
#10,840,032
of 12,228,633 outputs
Outputs from Mathematical Programming
#308
of 397 outputs
Outputs of similar age
#196,390
of 238,164 outputs
Outputs of similar age from Mathematical Programming
#1
of 4 outputs
Altmetric has tracked 12,228,633 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 397 research outputs from this source. They receive a mean Attention Score of 2.5. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 238,164 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them