Reference number : GC-2017-Q1-03433, amendment number : , agreement type : G, recipient legal name : Terekhov, Daria (Concordia University)|Terekhov, Daria (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 dynamic scheduling problems, the aim is to optimally assign arriving jobs (e.g., product orders in manufacturing; patients in healthcare) to resources (e.g., machines in manufacturing; nurses in healthcare) over time in a continuously-changing environment (e.g., machines break down; new more urgent patients arrive). 
 
While many optimization methods for scheduling have been proposed and implemented in scheduling software, in practice the resulting &lsquo;optimal&rsquo; schedules are not necessarily acceptable to the end users. Frequently, the schedules are manually modified before implementation, and, if the schedules repeatedly do not meet user expectations, the software itself might be discarded. The main premise of this proposal is that the true characteristics of the system and user preferences are captured by the data collected about the problem and its past (implemented) solutions, and that as a result the mismatch between the schedules created by software and those desired by the user can be reduced or eliminated through data-driven modelling. 
 
Modern information systems are capable of collecting data on past job and resource characteristics (e.g., arrival times, processing times), real-time system status updates (e.g., number of jobs currently in the system), and past scheduling decisions (e.g., decisions made by managers in the past). Traditionally, each of these data sources has been considered in isolation by distinct research areas, namely queueing theory, classical scheduling, and inverse scheduling, respectively. 
 
My research program will leverage the availability of both historical and real-time system and preference data through the integration of combinatorial scheduling, queueing theory and inverse optimization techniques. The resulting hybrid scheduling models will capture system characteristics and user preferences better than traditional approaches, producing schedules that would be more readily accepted by the users. Thus, this research has the potential to increase the adoption of scheduling software in practice, which will in turn lead to greater productivity and efficiency of those systems., program purpose french : In dynamic scheduling problems, the aim is to optimally assign arriving jobs (e.g., product orders in manufacturing; patients in healthcare) to resources (e.g., machines in manufacturing; nurses in healthcare) over time in a continuously-changing environment (e.g., machines break down; new more urgent patients arrive). 
 
While many optimization methods for scheduling have been proposed and implemented in scheduling software, in practice the resulting &lsquo;optimal&rsquo; schedules are not necessarily acceptable to the end users. Frequently, the schedules are manually modified before implementation, and, if the schedules repeatedly do not meet user expectations, the software itself might be discarded. The main premise of this proposal is that the true characteristics of the system and user preferences are captured by the data collected about the problem and its past (implemented) solutions, and that as a result the mismatch between the schedules created by software and those desired by the user can be reduced or eliminated through data-driven modelling. 
 
Modern information systems are capable of collecting data on past job and resource characteristics (e.g., arrival times, processing times), real-time system status updates (e.g., number of jobs currently in the system), and past scheduling decisions (e.g., decisions made by managers in the past). Traditionally, each of these data sources has been considered in isolation by distinct research areas, namely queueing theory, classical scheduling, and inverse scheduling, respectively. 
 
My research program will leverage the availability of both historical and real-time system and preference data through the integration of combinatorial scheduling, queueing theory and inverse optimization techniques. The resulting hybrid scheduling models will capture system characteristics and user preferences better than traditional approaches, producing schedules that would be more readily accepted by the users. Thus, this research has the potential to increase the adoption of scheduling software in practice, which will in turn lead to greater productivity and efficiency of those systems., agreement title english : Data-driven Dynamic Scheduling, agreement title french : Data-driven Dynamic Scheduling, agreement number : RGPIN, agreement value : 100000.0, 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