(last update Sep 2020)
Current students/researchers/postdocs:
Professor (visiting): [10/2019 - Current] Lin Zhuco-advise with L-M. Rousseau
Postdoc: [03/2020 - Current] Mostafa Darwicheco-advise with L-M. Rousseau and J. Mendoza
Postdoc: [01/2020 - Current] Simon Belieresco-advise with J.-F. Cordeau
Postdoc: [06/2018 - Current] Karim Perezco-advise with R. Jans
PhD (visiting)[09/2020 - Current] Aura Jalal (Universidade Federal de São Carlos),  co-advise with R. Jans
PhD (visiting)[03/2020 - Current] Jitsama Tanlamai (Faculty of Medicine, Mahidol University & Intern at McGill), co-advise with W. Khern-am-nuai
PhD[09/2019 - CurrentPaula Metzker Soares (IMT-Atlantique), co-advise with S. Thevenin and A. Dolgui
PhD[09/2019 - Current] Qihua Zhong (HEC), co-advise with R. Jans and M. Cousineau
PhD[01/2019 - Current] Mehdi Ghaniabadi (HEC), co-advise with R. Jans
PhD[09/2018 - Current] Duy Tan Nguyen (HEC), co-advise with J.-F. Cordeau
PhD[09/2017 - Current] Mahdis Bayani (Polytechnique), co-advise with L-M. Rousseau
PhD[09/2017 - Current] Chun Cheng (Polytechnique), co-advise with L-M. Rousseau
PhD[09/2016 - Current] Narges Sereshti (HEC), co-advise with R. Jans
MSc: [09/2018 - Current] Feras Al-Basha (HEC, Thesis), co-advise with L. Charlin
MSc[01/2020 - Current] Daheng Liu (HEC, Project)
MSc[01/2020 - Current] Lili Ke (HEC, Project)

Former students/researchers/postdocs:
Postdoc[2017 - 2019] Amira Dems, co-advised with J.-F. Cordeau. Currently Researcher at Hydro-Québec's research institute (IREQ).
Postdoc[2017 - 2018] Simon Thevenin, co-advised with J.-F. Cordeau. Currently Assistant Professor at École des Mines Télécom Atlantique (IMT-Atlantique).
PhD (visiting): [09/2018 - 10/2019] Yu Qinxiao (Visiting PhD from Tianjin University), co-advised with L-M. Rousseau.
PhD (visiting)[02/2018 - 05/2018] Maaike Hoogeboom (Visiting PhD at MIT from VU University Amsterdam), co-advised with P. Jaillet and W.E.H. Dullaert. Now data scientist at Achmea
PhD (visiting)[08/2016 - 12/2016] Arthur Maheo (Visiting PhD from Australian National University), co-advised with J.-F. Cordeau. Now postdoc at Monash University.
PhD (visiting)[03/2016 - 04/2017] Karim Perez (Visiting PhD from Federal University of São Carlos, Brazil), co-advised with R. Jans. Now postdoc at HEC Montréal.
PhD (intern)[01/2015 - 05/2015] Thierry Moisan (PhD research intern @JDA Labs). Now data scientist at Element AI
MSc[04/2020] Lotfi Chikh (HEC, Thesis), co-advised with V. Bélanger
MSc[12/2017] Jiajun Shao (HEC, Thesis) 
MSc[12/2017] Shuanghao Shi (HEC, Thesis) 
MSc[09/2019 04/2020Jing Guo (HEC, Project), co-advised with R. Jans
MSc[09/2019 04/2020Xinyan Dong (HEC, Project)
MSc[09/2019 - 04/2020Zhaoyue Su (HEC, Project)
MSc[05/2019 12/2019] Ted Kong (HEC, Project), co-advised with J.-F. Cordeau
MSc[05/2019 - 12/2019] Xiaohui Tu (HEC, Project), co-advised with J.-F. Cordeau
MSc[05/2019 - 12/2019Jia-Hui Feng (HEC, Project)
MSc[01/2019 08/2019] Hai-Yang Bao (HEC, Project) 
MSc[01/2019 08/2019] Jieying Kong (HEC, Project) 
MSc[01/2019 - 08/2019] Niyati Deshmukh (HEC, Project) 
MSc: [01/2019 - 08/2019] Phan Thuy Dung Nguyen (IMT-Atlantique, Project), co-advised with S. Thevenin and A. Dolgui
MSc[09/2018 - 05/2019] Mayukh Bhat (HEC, Project) 
MSc[09/2018 - 05/2019] Hoa Nguyen (HEC, Project)
MSc[09/2018 05/2019Zibo Wang (HEC, Project) 
MSc[09/2018 05/2019Aaron-John-Teong-Yue Teo (HEC, Project) 
MSc[05/2018 - 12/2018Haomin Chen (HEC, Project) 
MSc[05/2018 - 12/2018] Gabriel Tremblay (HEC, Project) 
MSc[05/2018 - 08/2018Linghan Zhu (HEC, Project), co-advised with R. Jans
MSc[09/2017 - 05/2018] Asieh Haeri (HEC, Project), co-advised with J.-F. Cordeau
MSc[09/2017 - 05/2018] Juliana Bodo (HEC, Project), co-advised with V. Bélanger
MSc[09/2017 - 05/2018] Shilpa Rawat (HEC, Project), co-advised with R. Jans
MSc[05/2017 - 01/2018Chao Zhao  (HEC, Project), co-advised with M.-È. Rancourt
MSc[05/2017 - 12/2017] David Sanchez-Fernandez (HEC, Project), co-advised with R. Jans
MSc[05/2017 - 12/2017] Alex Delise (HEC, Project), co-advised with R. Jans
MSc[05/2017 - 11/2017] Duy Tan Nguyen (HEC, Project), co-advised with S. Landry
MSc[04/2018 - 08/2018Jiefeng Fang (MSc research intern from École Polytechnique, France), co-advised with L-M. Rousseau
MSc[09/2015 - 12/2015] Vincent Zou (Visiting MSc from Maastricht University)
MSc[09/2015 - 12/2015] Zoë Kik (Visiting MSc from Maastricht University)
MSc[08/2013 - 12/2013] Yee Sian Ng (Research intern @SMART MIT)co-advised with P. Jaillet and M. Sim. Now PhD student @MIT
MSc (intern)[04/2018 - 08/2018] Jiefeng Fang (MSc research intern from École Polytechnique, France), co-advised with L-M. Rousseau
MSc (intern)[04/2017 - 08/2017] Ta Duy Nguyen (MSc research intern from École Polytechnique - France & NUS - Singapore), co-advised with L-M. Rousseau
MSc (intern)[04/2017 - 08/2017Michel Deudon (MSc research intern from École Polytechnique, France), co-advised with L-M. Rousseau
MSc (intern)[04/2017 - 08/2017Pierre Cournut (MSc research intern from École Polytechnique, France), co-advised with L-M. Rousseau
MSc (intern)[04/2017 - 08/2017Yu Jia Cheong (MSc research intern from École Polytechnique - France & NUS - Singapore)co-advised with L-M. Rousseau

(last update Jan 2016)
This is a list of the projects/applications that I have developed or get involved during the past few years. NOTE: this is not very up to date.

Network Inventory Optimization
Note: Involved as a scientific consultant. See [InvOpt] for more details.

Retail Analytics
Note: Involved as a scientific consultant. See [Retail.Me] for more details.

Vehicle Routing under Uncertainty
Overview: In distribution management, travel time uncertainty is a common issue in practice. This study addresses this issue in the vehicle routing problem (VRP) with deadlines by applying robust optimization and stochastic programming techniques. 
Approach(es): Decomposition approach for convex optimization, branch-and-cut algorithm, Benders decomposition.
Resource(s): [VRP-D]

Production and Inventory Routing Optimization
Overview: This project concerns the development of optimization applications of integrated supply chain operational planning systems. Our main focus is on the production routing problem (PRP), a combined version of the well-known lot-sizing problem (LSP) and the vehicle routing problem (VRP), which jointly optimizes production, inventory, distribution and routing decisions. As a generalization of the PRP, we also extended our approaches to the inventory routing problem (IRP). Efficient exact and heuristic algorithms were developed to tackle the problems in deterministic and stochastic environments in a two-stage and multi-stage fashion.
Approach(es): branch-and-cut algorithm, Benders decomposition based branch-and-cut algorithm, large-scale neighborhood search heuristic.

Truck and Container Loading Optimization
Overview: A real world application concerning a development of an efficient tool to optimize truck and container load utilization in order to minimize the number of required units. The application was developed to handle two types of orders, i.e., splitable (for international shipments) and unsplitable (for domestic shipments), while taking into account several restrictions, e.g., fleet capacity and compatibility, customer specific requirements for fleet composition, tariff benefits, customs compliance, etc.
Approach(es): density-index based greedy heuristic.
Results: cost savings US$1- 2MM per year, and man-hr reduction 10-20 hrs per week

Continuous Move Optimization
Overview: An application from the real world problem in a large cement company. This tool was developed to optimize routing decisions for a fleet of ready mixed concrete vehicles dispatched from multiple pickup locations to a set of customers. The shipment is full truck load but one can attempt to build a set of continuous moves among the pickup and delivery points in order to reduce total empty haul distance, while taking into consideration truck ban restrictions and pickup and delivery time windows.
Approach(es): column generation and two heuristics.
Results: potential operating cost savings between 10% and 37%
Resource(s): [pdf1] [pdf2]

Optimization tool for pavement maintenance planning
Overview: This application was part of a government project. It involved a development of an efficient tool to determine optimal pavement maintenance and safety improvement plans for the countrywide network by taking into account an available budget.
Approach(es): MIP based heuristic and a greedy heuristic.