Alessandro Arlotto

Associate Professor of Business Administration and Mathematics

Decision Sciences, The Fuqua School of Business

Department of Mathematics (secondary appointment)

Duke University

Email: alessandro.arlotto@duke.edu

Phone: +1 (919) 660-7780

My vita

 

Education:

Ph.D., University of Pennsylvania, 2012

A.M., University of Pennsylvania, 2009

M.S., Università degli Studi di Torino (Italy), 2007

B.S., Università degli Studi di Torino (Italy), 2004

 

Research Interests:

 

Grants Funded:

  1. National Science Foundation, CAREER Award, The effects of centralized and decentralized sequential decisions on system performance. (nsf)
  2. National Science Foundation, Conference on probability theory and combinatorial optimization. (nsf)

 

Submitted Papers:

  1. Arlotto, A., Frazelle, E.A., and Wei, Y. (2016) Strategic open routing in service networks, under review. (pdf) (ssrn)
  2. Arlotto, A., Wei, Y., and Xie, X. (2016) A O(log n)-optimal policy for the online selection of a monotone subsequence from a random sample, under review. (pdf) (arXiv)

 

Published Papers:

  1. Arlotto, A. and Steele, J.M. (2016) A central limit theorem for costs in Bulinskaya's inventory management problem when deliveries face delays, Methodology and Computing in Applied Probability, forthcoming. (pdf) (arXiv)
  2. Arlotto, A. and Steele, J.M. (2016) A central limit theorem for temporally non-homogenous Markov chains with applications to dynamic programming, Mathematics of Operations Research, 41, 1448-1468. (pdf) (arXiv)
  3. Arlotto, A. and Steele, J.M. (2016) Beardwood-Halton-Hammersley theorem for stationary ergodic sequences: a counterexample, The Annals of Applied Probability, 26, 2141-2168. (pdf) (arXiv)
  4. Arlotto, A., Mossel, E., and Steele, J.M. (2016) Quickest online selection of an increasing subsequence of specified size, Random Structures & Algorithms, 49, 235-252. (pdf) (arXiv)
  5. Arlotto, A., Nguyen, V. V. , and Steele, J.M. (2015) Optimal online selection of a monotone subsequence: a central limit theorem, Stochastic Processes and their Applications, 125, 3596-3622. (pdf) (arXiv)
  6. Arlotto, A., Gans, N., and Steele, J.M. (2014) Markov decision problems where means bound variances, Operations Research, 62, 864-875. (pdf)
  7. Arlotto, A. and Steele, J.M. (2014) Optimal online selection of an alternating subsequence: a central limit theorem, Advances in Applied Probability, 46, 536-559. (pdf) (arXiv)
  8. Arlotto, A., Chick, S.E., and Gans, N. (2014) Optimal hiring and retention policies for heterogeneous workers who learn, Management Science, 60, 110-129. (pdf) (online appendix)
  9. Arlotto, A., Chen, R.W., Shepp, L.A. and Steele, J.M. (2011) Online selection of alternating subsequences from a random sample, Journal of Applied Probability, 48, 1114-1132. (pdf) (arXiv)
  10. Arlotto, A. and Steele, J.M. (2011) Optimal sequential selection of a unimodal subsequence of a random sequence, Combinatorics, Probability and Computing, 20, 799-814. (pdf) (arXiv)
  11. Arlotto, A. and Scarsini, M. (2009) Hessian orders and multinormal distributions, Journal of Multivariate Analysis, 100, 2324-2330. (pdf)

 

Conference Proceedings:

  1. Arlotto, A., Chick, S.E., and Gans, N. (2010) Optimal employee retention when inferring unknown learning curves, Proceedings of the 2010 Winter Simulation Conference, 1178-1188. (pdf)

 

Events:

Conference on Probability Theory and Combinatorial Optimization
The Fuqua School of Business, Duke University
March 14-15, 2015