Reference number : GC-2017-Q1-03027, amendment number : , agreement type : G, recipient legal name : Gendron, Bernard (Université de Montréal), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : This research program is dedicated to the development of novel methods for solving large-scale combinatorial optimization problems, in particular those formulated in terms of networks. Such problems give rise to models that are difficult to solve, even using state-of-the-art optimization tools. Hence, we will develop specialized methods based on the latest advances in operations research. This research program aims to pursue and intensify the continued efforts of the applicant in developing methods for solving large-scale, difficult, combinatorial optimization problems. The long-term vision is driven by the objective of bridging the gaps between theory, methodology and algorithm implementation, thus leading to successful transfer and use of the proposed methods in real applications. In particular, the long-term goals of this research program are: 1) increase our capacity to solve large-scale combinatorial optimization problems; 2) expand the applicability of large-scale combinatorial optimization methods; 3) develop a unifying generic methodology to integrate supply optimization and demand analysis; 4) exploit knowledge to solve real applications. Our research on combinatorial optimization is driven by real applications, mostly in the area of logistics and transportation. These include applications of network design and facility location models in freight distribution and safety/security in transportation, as well as applications of staff scheduling in retail services, all of prime importance for the Canadian economy. Our ultimate goal is to provide practical solutions to end-users of these applications by developing advanced models and methods., program purpose french : This research program is dedicated to the development of novel methods for solving large-scale combinatorial optimization problems, in particular those formulated in terms of networks. Such problems give rise to models that are difficult to solve, even using state-of-the-art optimization tools. Hence, we will develop specialized methods based on the latest advances in operations research. This research program aims to pursue and intensify the continued efforts of the applicant in developing methods for solving large-scale, difficult, combinatorial optimization problems. The long-term vision is driven by the objective of bridging the gaps between theory, methodology and algorithm implementation, thus leading to successful transfer and use of the proposed methods in real applications. In particular, the long-term goals of this research program are: 1) increase our capacity to solve large-scale combinatorial optimization problems; 2) expand the applicability of large-scale combinatorial optimization methods; 3) develop a unifying generic methodology to integrate supply optimization and demand analysis; 4) exploit knowledge to solve real applications. Our research on combinatorial optimization is driven by real applications, mostly in the area of logistics and transportation. These include applications of network design and facility location models in freight distribution and safety/security in transportation, as well as applications of staff scheduling in retail services, all of prime importance for the Canadian economy. Our ultimate goal is to provide practical solutions to end-users of these applications by developing advanced models and methods., agreement title english : Large-Scale Combinatorial and Network Optimization, agreement title french : Large-Scale Combinatorial and Network Optimization, agreement number : RGPIN, agreement value : 355000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada