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Andrew L. Johnson
Researcher at University of Derby
Publications - 110
Citations - 3166
Andrew L. Johnson is an academic researcher from University of Derby. The author has contributed to research in topics: Data envelopment analysis & Estimator. The author has an hindex of 28, co-authored 107 publications receiving 2723 citations. Previous affiliations of Andrew L. Johnson include Texas A&M University & Aalto University.
Papers
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Journal ArticleDOI
Batch picking in narrow-aisle order picking systems with consideration for picker blocking
TL;DR: This paper develops strategies to control picker blocking that challenge the traditional assumptions regarding the tradeoffs between wide- and narrow-aisle order picking systems, and develops a mixed integer programming solution for exact control.
Journal ArticleDOI
One-stage estimation of the effects of operational conditions and practices on productive performance: asymptotically normal and efficient, root-n consistent StoNEZD method
Andrew L. Johnson,Timo Kuosmanen +1 more
TL;DR: In this article, a one-stage semi-nonparametric estimator for data envelopment of z variables data (StoNEZD) is proposed, which combines the nonparametric DEA-style frontier with a regression model of the contextual variables.
Journal ArticleDOI
The shell of the Queen Scallop Aequipecten opercularis (L.) as a promising tool for palaeoenvironmental reconstruction: evidence and reasons for equilibrium stable-isotope incorporation
TL;DR: The Queen Scallop Aequipecten opercularis has been an important part of the marine fauna of the northeast Atlantic region since the Miocene, and is a suitable candidate for palaeoenvironmental studies using stable isotopes as mentioned in this paper.
Journal ArticleDOI
Estimating the shadow prices of SO2 and NOx for U.S. coal power plants: A convex nonparametric least squares approach
TL;DR: In this article, a convex nonparametric least squares (CNLS) quadratic optimization problem is formulated to estimate a frontier production function assuming either a deterministic disturbance term consisting only of inefficiency, or a composite disturbance term composed of both inefficiency and noise.
Book ChapterDOI
Stochastic Nonparametric Approach to Efficiency Analysis: A Unified Framework
TL;DR: This chapter examines quantile estimation using StoN ED and an extension of the StoNED method to the general case of multiple inputs and multiple outputs and provides a detailed discussion of how to model heteroscedasticity in the inefficiency and noise terms.