Short Graduate Program – Management Science (doctoral level)

Nos équipes de recherche disposent d’un leadership reconnu dans le domaine des sciences de la décision. Spécialisez-vous à leur contact, avec une formation de niveau doctoral marquée par des échanges d’expertises structurés.

Overview

Un pont entre la recherche et la pratique

Acquisition ou approfondissement de compétences méthodologiques ou de spécialisation avancées applicables en organisation ou en milieu de recherche.

Séminaires donnant lieu à des interactions de niveau expert entre chercheuses, chercheurs, et personnes disposant d’une expérience solide, acquise en organisation.

Large offre de cours, portant notamment sur : les méthodes d’apprentissage automatique et de recherche opérationnelle, les modèles de tarification, l’optimisation (à grande échelle, dynamique ou robuste) et la théorie des jeux.

Exploration de thèmes avancés de recherche en sciences de la décision présentés par plusieurs membres du corps professoral.

Program characteristics

Doctoral
9 credits

Part-time

On-site

Côte-des-Neiges

De la recherche de pointe dans ce domaine

Les membres du corps professoral associés à cette spécialisation doctorale cumulent des centaines d’articles publiés dans les meilleures revues. Plusieurs sont titulaires d’une chaire ou d’un professorship de recherche. Toutes et tous collaborent étroitement avec plusieurs groupes de recherche et pôles de transfert.

Chaires de recherche

Chaire de recherche du Canada sur la prise de décision en incertitude
Chaire de gestion du secteur de l’énergie
Chaire de recherche Scale AI en intelligence artificielle pour la mobilité et la logistique urbaines
Chaire de théorie des jeux et gestion
Deux chaires en intelligence artificielle Canada-CIFAR
Chaire en logistique et en transport
Chaire FRQ-IVADO en science des données

Groupes de recherche

Centre de recherches mathématiques (CRM)
Centre interuniversitaire de recherche sur les réseaux d’entreprise, la logistique et le transport (CIRRELT)
Groupe d’études et de recherche en analyse des décisions (GERAD)
IVADO – consortium de recherche en intelligence artificielle
MILA, centre de recherche en intelligence artificielle

Ce programme est fait pour vous

Vous êtes en emploi et vous envisagez une spécialisation avancée en sciences de la décision. Ce microprogramme est profitable à des personnes détenant déjà un MBA, une maîtrise en sciences de la décision ou dans un domaine connexe.
Vous venez de terminer une maîtrise et vous voulez poursuivre votre spécialisation sans intégrer un programme de doctorat complet.
Vous envisagez de poursuivre vos études avec un doctorat en sciences de la décision, mais vous voulez faire l’expérience des cours de 3^e cycle avant de prendre une décision.
Vous souhaitez élargir votre expertise après avoir obtenu un doctorat dans une discipline connexe.

D’autres programmes pour renforcer votre expertise et élargir votre réseau

Explorez les autres microprogrammes de 3e cycle

Découvrez nos autres programmes

Already started your program? Check your course list in HEC en ligne > Academic Progress

3 specialization seminars (9 credits)

You must choose 3 seminars from the following:

MATH 80627A

Complex Networks Analysis

Subject

Mathématiques

Program

PhD

Location

Côte-des-Neiges

Instruction mode

On-site learning

Credits

3

Would it be from social networks (Facebook, Twitter for instance), or from their own data (such as email exchanges), organizations have access to important amounts of data. This data may be of specific nature, namely, instead of a description of each observation by characteristics, relations between observations may be known. Studying these relations involves specific techniques. The nature of the data «relations between objects » makes sampling a difficult task. For that reason, it is usually better to work on the whole dataset, which is challenging from the algorithmic point of view.

Themes covered

Centrality analysis
Community detection
Network embeddings
Network descriptors
Distances in networks

See the course webpage
MATH 80680A

Dynamic Optimisation in Management

Subject

Mathématiques

Program

PhD

Location

Côte-des-Neiges

Instruction mode

On-site learning

Credits

3

At the end of the course, the students should be able to identify a business management situation presenting a dynamic optimization problem, represent this problem using a suitable mathematical model, and solve it using one of the available techniques of dynamic optimization.

See the course webpage
MATH 80685A

Game Theory

Subject

Mathématiques

Program

PhD

Instruction mode

On-site learning

Credits

3

The course covers static noncooperative and cooperative games, bargaining procedures, repeated games, differential games, and games played over event trees. The elements of each class of games are defined rigorously, the main issues are discussed, the solution concepts are introduced, and their computation challenges are highlighted. Examples from different areas of management science are provided to illustrate the concepts and see the limits of the models considered.

Themes covered

Noncooperative games
Cooperative games
Repeated games
Bargaining procedures
Optimal control and dynamic programming (a brief introduction)
Differential games
Dynamic games played over event trees.

See the course webpage
MATH 80655A

Large-Scale Optimization Models and Applications

Subject

Mathématiques

Program

PhD

Location

Côte-des-Neiges

Instruction mode

On-site learning

Credits

3

Advanced modeling approaches are studied, including non-compact models with exponentially many constraints or variables, two-stage models, and bilevel models. Various solution techniques are explored, such as branch-and-bound, cutting-plane methods, branch-and-cut, column generation, and Benders decomposition. Applications span fields such as distribution management, transportation, network design, routing, logistics, production planning, energy systems, telecommunications, disaster management, last-mile delivery, and healthcare logistics. The course incorporates contemporary topics from recent scientific publications. It also includes hands-on implementation of these models, and a course research project.

Themes covered

Introduction to modeling with integer variables; well-solved problems
Optimality relaxation bounds complexity and problem reductions
Branch-and-bound and branch-and-cut (with application to e.g. traveling salesman problems)
Column generation (with application to e.g. vehicle routing problems)
Branch-and-price (with application to e.g. location and routing problems)
Two-stage models and Benders decomposition (with application to e.g. facility location)
Linearization of bilevel models (with application to e.g. network design)

See the course webpage
MATH 80624A

Quantitative Risk Management Using Robust Optimization

Subject

Mathématiques

Program

PhD

Location

Côte-des-Neiges

Instruction mode

On-site learning

Credits

3

More specifically, students will become familiar with the main tools that are used in the application of the robust optimization paradigm: convex theory, data-driven uncertainty sets design, adjustable decision rules, tractable reformulation, and decomposition algorithms for problems of infinite size. The course will also cover a set of practical applications where the use of such tools is called-for. Applications will be inspired from a diversified range of fields of practice.

Themes covered

1) Why is there a recent surge of interest in robust optimization?

2) Robust counterpart of Linear Programs

3) Data-driven Uncertainty Set Design

4) Robust Nonlinear Programming

5) Adjustable Robust Linear Programming

6) Value of Flexibility Using Tractable Decision Rules

7) Globalized Robust Counterparts

8) Distributionally Robust Optimization

9) Robust Markov Decision Processes

10) Robust Preference Optimization

11) Pareto Robust Optimization

12) A Survey of Recent Applications

See the course webpage
MATH 80618A

Sample Efficient Optimization

Subject

Mathématiques

Program

PhD

Location

Côte-des-Neiges

Instruction mode

On-site learning

Credits

3

This course presents methods that combine domain knowledge from management, science or engineering within classical ML and OR techniques to yield practice-relevant optimization tools. Topics include Gaussian process modeling, Bayesian optimization, dimensionality reduction, variance reduction, continuous and/or discrete simulation-based optimization, physics-informed optimization, surrogate modeling. Concepts are illustrated using practical applications. The course presents established and emerging research through lectures and paper discussions. It includes guest research lecturers from major technology companies and / or top academic institutions worldwide.

Themes covered

Gaussian processes
Bayesian optimization
Dimensionality reduction
Variance reduction
Continuous simulation-based optimization
Discrete simulation-based optimization
Surrogate modeling

See the course webpage
MATH 80634A

Special Topics in Management Science

Subject

Mathématiques

Program

PhD

Instruction mode

On-site learning

Credits

3

This course covers advanced topics in Management Science. This course is typically taught by a team of professors.

See the course webpage
MATH 80617A

Topics in Pricing Models

Subject

Mathématiques

Program

PhD

Instruction mode

On-site learning

Credits

3

Themes covered

- The economics and marketing of pricing
- Pricing strategically in (imperfectly) competitve markets while anticipating other firms' responses
- Assessing the impact of price on demand (and other variables e.g. perception and buying intention) using easy-to-implement statistical models.
- Multi-attribute models and conjoint analysis and their use in measuring consumer's perception of price
- Adapting prices to diffusion effects (e.g. social imitation and word-of-mouth effects)
- Pricing perishable products and services

See the course webpage

Deadlines for submitting admission applications

Fall term: no later than March 1st
Winter term: no later than September 15th
Summer term: no admissions

International applicants

This program is offered part time. See our article (in French) to learn about the immigration rules.

Conditions and required documents

Admission requirements

Degree required

You must hold a master’s degree in a field related to the desired program or a degree deemed equivalent by the program administration.

Minimum GPA

You must have earned GPA of at least 3.0 out of 4.3.

If the university in which you completed the master's degree requires a higher GPA for admission to a doctoral program, this is the average to take into account.

Language requirements

Documents required for admission

You will need to provide documents as part of the admission process.

Check the list of required documents

Capacity is limited for certain programs. HEC Montréal does not guarantee that all eligible applicants will be accepted.

Estimated tuition fees

The amounts below are approximate. These amounts do not include the cost of health insurance, course materials, housing,

Each term, you will receive a bill with the exact amount you owe based on your credit load.

Residents of Quebec

You pay the Quebec rate if you are a resident of Quebec according to certain criteria, such as having a Quebec birth certificate or a Québec Selection Certificate.

Total cost of a 9-credit program: X XXX $

See if you meet one of the Quebec residence criteria.

Canadians who are not-residents of Quebec

You pay the Canadian rate if you are a citizen by birth or naturalization, an Indigenous person, or a permanent resident of Canada.

Total cost of a 9-credit program: X XXX $

See whether you can receive an exemption and pay the Quebec rate.

French and Belgian Francophone students

Through an agreement between governments, you are eligible for an exemption allowing you to pay the Quebec rate instead of the international rate.

Total cost of a 9-credit program: X XXX $

Check the conditions you must meet to receive this exemption.