Reference number : GC-2017-Q1-03449, amendment number : , agreement type : G, recipient legal name : Jaumard, Brigitte (Concordia University)|Jaumard, Brigitte (Université Concordia), 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 : In today&rsquo;s digital world, with ever increasing amounts of data comes the need to solve optimization problems of unprecedented sizes. Machine learning, communication and social networks, logistics systems are some of the many prominent application domains where optimization problems arise with tens of thousands or millions of variables. Many optimization models and algorithms, while exhibiting great efficiency in modest dimensions, have great difficulties to scale for instances of this size and do not offer satisfactory solution. The primary and long-term objective of my research is to contribute to the design of novel optimization algorithms capable of working in very large-scale setting. I plan to investigate both exact and heuristic methods, and validate the findings on some particular applications in communication, logistics and social networks. 
 
For exact methods, the objective is to integrate knowledge based on both theoretical and empirical evidence from several disciplines, and explore the "what, why, how, and do" paradigm with an emphasis on (i) modelling aspects, (ii) combination of mathematical models, and (iii) parallelization techniques in order to take advantage of the heterogeneous environments combining multi-core processors, multi-threaded programming and GPU accelerators for very large scale optimization. While those environment were only available on mainframe computers, they are now available to computers that are easily accessible to the industry. 
 
For heuristic methods, focus will be on meta-heuristics, a wide class of solution methods that have been successfully applied to many optimization problems. However, they seem to have reached their limits to solve very large combinatorial problems such as those arising in cross-docking or network optimization. This is because meta-heuristics explore the solution space with ad-hoc methods, whose efficiency and computing time highly depend on the topology of the local optima which, except for some very particular problems, are very difficult to foresee. We plan to replace the ad-hoc exploration of the solution space with an informed exploration guided by machine learning. Comparison will be made with direct machine learning algorithms on practical problems arising in: (i) supply chain management and in particular with cross-docking, and (ii) network optimization and (iii) mechanism design in social networks. Data required by machine learning algorithms will be provided by ClearD and Ciena for the first two applications, and an organization/industrial partner needs to be identified for the third one. 
 
The results of my research will provide the industry (like ClearD and Ciena) information technology management tools for efficient and automated cross-docking/network management, not only to improve competitiveness but also to reduce energy consumption and therefore carbon footprint., program purpose french : In today&rsquo;s digital world, with ever increasing amounts of data comes the need to solve optimization problems of unprecedented sizes. Machine learning, communication and social networks, logistics systems are some of the many prominent application domains where optimization problems arise with tens of thousands or millions of variables. Many optimization models and algorithms, while exhibiting great efficiency in modest dimensions, have great difficulties to scale for instances of this size and do not offer satisfactory solution. The primary and long-term objective of my research is to contribute to the design of novel optimization algorithms capable of working in very large-scale setting. I plan to investigate both exact and heuristic methods, and validate the findings on some particular applications in communication, logistics and social networks. 
 
For exact methods, the objective is to integrate knowledge based on both theoretical and empirical evidence from several disciplines, and explore the "what, why, how, and do" paradigm with an emphasis on (i) modelling aspects, (ii) combination of mathematical models, and (iii) parallelization techniques in order to take advantage of the heterogeneous environments combining multi-core processors, multi-threaded programming and GPU accelerators for very large scale optimization. While those environment were only available on mainframe computers, they are now available to computers that are easily accessible to the industry. 
 
For heuristic methods, focus will be on meta-heuristics, a wide class of solution methods that have been successfully applied to many optimization problems. However, they seem to have reached their limits to solve very large combinatorial problems such as those arising in cross-docking or network optimization. This is because meta-heuristics explore the solution space with ad-hoc methods, whose efficiency and computing time highly depend on the topology of the local optima which, except for some very particular problems, are very difficult to foresee. We plan to replace the ad-hoc exploration of the solution space with an informed exploration guided by machine learning. Comparison will be made with direct machine learning algorithms on practical problems arising in: (i) supply chain management and in particular with cross-docking, and (ii) network optimization and (iii) mechanism design in social networks. Data required by machine learning algorithms will be provided by ClearD and Ciena for the first two applications, and an organization/industrial partner needs to be identified for the third one. 
 
The results of my research will provide the industry (like ClearD and Ciena) information technology management tools for efficient and automated cross-docking/network management, not only to improve competitiveness but also to reduce energy consumption and therefore carbon footprint., agreement title english : Large-Scale and Big Data Optimization, agreement title french : Large-Scale and Big Data Optimization, agreement number : RGPIN, agreement value : 205000.0, 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