Course details
Application models in Business Analytics
MATH 60612A
Topics covered in this course include optimization problems, strategic decision making, and forecasting in the context of a variety of application areas (health care, sports, energy production, environmental management, politics, transportation, scheduling, etc.) In each application context, modeling and implementation issues are discussed, along with the relevant solution methodologies. Discussed applications are based on real cases published in scientific journals.
Themes covered
Health care (assignment scheduling localization collection and distribution diagnostics planning)
Sports (performance optimization performance analysis risk and reliability analysis organization of sport events scheduling routing)
Energy (transmission distribution and use global techno-economic models optimization of large problems non-convex optimization)
Environment (multi-criteria and multi-objective optimization data envelopment analysis stochastic optimization robust optimization)
Public affairs and national defense (localization and coverage prevention and detection assignment fairness)
Humanitarian aid and crisis management (logistics networks routing transportation distribution and storage simulation heuristics decision support systems)
Transportation and distribution (routes schedules on-demand transportation learning simulation-based optimization)
Human resource management (scheduling coverage modelling of collective agreements rotating schedules heuristics and metaheuristics)
Academic management (planning scheduling graph coloring heuristics integrated systems)
Customer relationship management (big data recommendation systems segmentation revenue management bi-level optimization)
Production and supply chains (diet issues supply networks competition hierarchy reverse logistics game theory).
Important notes
Course in French: MATH 60612
Course code
MATH 60612A
Subject
Mathématiques
Program
Maîtrise en gestion (M. Sc.)
Location
Côte-des-Neiges
Instruction mode
On-site learning
Credits
3