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(last update Sep 2019)
Current students/researchers/postdocs: Professor (Visiting): [10/2019 - Current] Lin Zhu, co-advise with L-M. Rousseau Postdoc: [01/2020 - Current] Simon Belieres, co-advised with J.-F. Cordeau Postdoc: [06/2018 - Current] Karim Perez, co-advised with R. Jans PhD: [09/2019 - Current] Paula Metzker Soares (IMT-Atlantique), co-advised with S. Thevenin and A. Dolgui 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: [09/2017 - Current] Lotfi Chikh (HEC, Thesis), co-advise with V. Bélanger MSc: [01/2020 - Current] Daheng Liu (HEC, Project) MSc: [01/2020 - Current] Lili Ke (HEC, Project) MSc: [09/2019 - Current] Jing Guo (HEC, Project), co-advised with R. Jans MSc: [09/2019 - Current] Xinyan Dong (HEC, Project) MSc: [09/2019 - Current] Zhaoyue Su (HEC, Project) MSc: [05/2019 - Current] Ted Kong (HEC, Project), co-advised with J.-F. Cordeau
Former students/researchers/postdocs: PhD (visiting): [09/2018 - 10/2019] Yu Qinxiao (Visiting PhD from Tianjin University), co-advise 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 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 PhD (intern): [01/2015 - 05/2015] Thierry Moisan (PhD research intern @JDA Labs). Now data scientist at Element AI MSc: [12/2017] Jiajun Shao (HEC, Thesis) MSc: [12/2017] Shuanghao Shi (HEC, Thesis) MSc: [05/2019 - 12/2019] Xiaohui Tu (HEC, Project), co-advised with J.-F. Cordeau MSc: [05/2019 - 12/2019] Jia-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 - 05/2019] Zibo Wang (HEC, Project) MSc: [09/2018 - 05/2019] Aaron-John-Teong-Yue Teo (HEC, Project) MSc: [05/2018 - 12/2018] Haomin Chen (HEC, Project) MSc: [05/2018 - 12/2018] Gabriel Tremblay (HEC, Project) MSc: [05/2018 - 08/2018] Linghan 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 - 01/2018] Chao 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 - 08/2018] Jiefeng 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 - 08/2017] Michel Deudon (MSc research intern from École Polytechnique, France), co-advised with L-M. Rousseau MSc (intern): [04/2017 - 08/2017] Pierre Cournut (MSc research intern from École Polytechnique, France), co-advised with L-M. Rousseau MSc (intern): [04/2017 - 08/2017] Yu 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.
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.
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%
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.
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