Dr. Daniel Aloise
Polytechnique Montréal
Dr Daniel Aloise is an assistant professor at the Computer Engineering Department at Polytechnique Montréeal. He obtained his PhD in Applied Maths from Polytechnique Montréeal in 2009 and he has been assistant professor at Universidade Federal do Rio Grande do Norte, Brazil, in 2009-2016. His research interests include data mining, optimization, mathematical programming and how these disciplines interact to tackle problems in the Big Data era. Daniel has published in leading operations research and data mining journals including Machine Learning, Pattern Recognition, European Journal of Operational Research, Mathematical Programming and Journal of Global Optimization.



Journal papers

T. G. da Silva, G. F. de Sousa Filho, I. A. M. Barbosa, N. Mladenovic, L. A.F Cabral, L. S. Ochi, D. Aloise, 2018. Efficient Heuristics for the Minimum Labeling Global Cut Problem. Electronic Notes in Discrete Mathematics. 66, 23-30.

D. Aloise, C. Contardo, 2018. A Sampling-Based Exact Algorithm for the Solution of the Minimax Diameter Clustering Problem. Journal of Global Optimization. 1-18

K. Gonçalves-e-Silva, D. Aloise, S. Xavier-de-Souza, 2018. Parallel Synchronous and Asynchronous Coupled Simulated Annealing. The Journal of Supercomputing. 1-29

B. Jefferson de S. Pessoa, D. Aloise, L. A.F. Cabral, 2018. The Weighted Fair Sequences Problem. Computers & Operations Research. 91, 121-13.

K. G. Silva, D. Aloise, S. Xavier-de-Souza, N. Mladenovic, 2018. Less is More: Simplified Nelder-Mead Method for Large Unconstrained Optimization. Yugoslav Journal of Operations Research. DOI:

L. R. Costa, D. Aloise, N. Mladenovic, 2017. Less is More Approach for Balanced Minimum Sum-Of-Squares Clustering. Information Sciences 415-416, 247-253.

F. B.A.R. Mariz, M. R. Almeida, D. Aloise, 2017. A Review of Dynamic Data Envelopment Analysis: State of the Art and Applications. International Transactions in Operational Research 25 (2) 469-505.

A. Pyatkin, D. Aloise, M. Mladenovic, 2016. NP-Hardness of Balanced Minimum Sum-Of-Squarres Clustering. Pattern Recognition Letters 97, 44-45.

I. F. Fernandes, D. Aloise, D. J. Aloise, T. P. Jeronimo, 2017. A Polynomial-Time Algorithm for the Discrete Facility Location Problem with Limited Distances and Capacity Constraints. Brazilian Journal of Operations & Production Management 14 (2), 136-1444. DOI:


D. Aloise, C. Nielsen Damasceno, N. Mladenovic, D. N. Pinheiro, 2017. On Strategies to Fix Degenerate k-Means Solutions. Journal of Classification. 34 (2) 165-190.

S. J. Blanchard, D. Aloise, W. S. DeSarbo, 2017. Extracting Summary Piles from Sorting Task Data. Journal of Marketing Research 54 (3) 398-414. doi:

D. Dzamic, D. Aloise, N. Mladenovic, 2017. Ascent – Descent Variable Neighborhood Decomposition Search for Community Detection by Modularity Maximization. Annals of Operations Research. 1-15.

E. Santi, D. Aloise, S, J Blanchard, 2016. A Model for Clustering Data from Heterogeneous Dissimilarities. European Journal of Operation Research, 253 (3), 659-672.

D. Aloise, A. Araújo, 2015. A Derivative-Free Algorithm for Refining Numerical Microaggregation Solutions. International Transactions in Operational Research, 22 (4) 693-712.

Working papers

D. Aloise, C. Contardo, 2016. An Iterative Algorithm for the Solution of very Large-Scale Diameter Clustering Problems. G-2015-140