His scientific interests lie mostly in Mathematical optimization, Simulated annealing, Heuristics, Heuristic and Operations research. His Mathematical optimization research is multidisciplinary, incorporating elements of Customer lifetime value, Algorithm, Homogeneity and Market segmentation. The study incorporates disciplines such as Complete-linkage clustering, Data mining and Feature selection in addition to Algorithm.
His biological study spans a wide range of topics, including Uniform distribution, Combinatorial optimization and Permutation. His studies in Heuristics integrate themes in fields like Linear programming and Scheduling. His Operations research research focuses on Operations management and how it connects with Time horizon and Branch and bound.
Michael J. Brusco spends much of his time researching Mathematical optimization, Cluster analysis, Algorithm, Heuristics and Heuristic. Mathematical optimization is frequently linked to Combinatorial data analysis in his study. His Cluster analysis research includes elements of Data mining and Partition.
His Algorithm research is multidisciplinary, incorporating perspectives in Feature selection, Row and column spaces, k-means clustering and Symmetric matrix. His study explores the link between Heuristics and topics such as Operations research that cross with problems in Operations management, Time horizon and Service system. His Integer programming course of study focuses on Scheduling and Linear programming.
The scientist’s investigation covers issues in Statistics, Cluster analysis, Algorithm, Data mining and Heuristics. His Statistics study incorporates themes from Customer satisfaction and Residual. His Algorithm research includes themes of Test, Test theory, Feature and Heuristic.
His work deals with themes such as Cohen's kappa, Data point and Categorical variable, which intersect with Data mining. Mathematical optimization covers he research in Heuristics. When carried out as part of a general Mathematical optimization research project, his work on Scheduling and Integer programming is frequently linked to work in Augmentation procedure, Cyclic shift and Shift length, therefore connecting diverse disciplines of study.
His main research concerns Algorithm, Statistics, Pairwise comparison, Heuristic and Mode. He carries out multidisciplinary research, doing studies in Algorithm and Ising model. His study brings together the fields of Logical matrix and Statistics.
His work carried out in the field of Pairwise comparison brings together such families of science as Similarity and Sample.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques
Douglas Steinley;Michael J. Brusco.
Journal of Classification (2007)
Branch-and-Bound Applications in Combinatorial Data Analysis
Michael J Brusco;Stephanie Stahl.
Service Sweethearting: Its Antecedents and Customer Consequences
Michael K. Brady;Clay M. Voorhees;Michael J. Brusco.
Staffing a Multiskilled Workforce with Varying Levels of Productivity: An Analysis of Cross‐training Policies*
Michael J. Brusco;Tony R. Johns.
Decision Sciences (1998)
Note: A local-search heuristic for large set-covering problems
Larry W. Jacobs;Michael J. Brusco.
Naval Research Logistics (1995)
A Comparative Evaluation of Labor Tour Scheduling Methods
Stephen E. Bechtold;Michael J. Brusco;Michael J. Showalter.
Decision Sciences (1991)
A variable-selection heuristic for K-means clustering
Michael J. Brusco;J. Dennis Cradit.
Improving Personnel Scheduling at Airline Stations
Michael J. Brusco;Larry W. Jacobs;Robert J. Bongiorno;Duane V. Lyons.
Operations Research (1995)
Evaluating mixture modeling for clustering: recommendations and cautions.
Douglas Steinley;Michael J. Brusco.
Psychological Methods (2011)
A simulated annealing approach to the cyclic staff-scheduling problem
Michael J. Brusco;Larry W. Jacobs.
Naval Research Logistics (1993)
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