Online stochastic optimization for dynamic operations management systems

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This research program aims at creating state-of-the-art knowledge on online stochastic optimization applied to dynamic operations management systems by developing new data-driven discrete optimization methods. Many of these problems appear for services such as ride sourcing, food delivery, clinic appointments, inventory management, and workforce management to name a few. More precisely, when a request (e.g., a ride) enters the system, the operator will allocate some resources (a driver) to fulfill the service. These operational problems have become critical at the era of cloud computing and mobile applications, as all the services must propose real-time applications that fit the needs of their clients in order to be competitive in multi-billion dollar expanding markets. My research focuses on online stochastic optimization to solve these dynamic problems with data-driven algorithms. Online stochastic optimization proposes a paradigm to efficiently solve these problems by regularly reoptimizing a decision plan to take into account the new requests and several scenarios on the arrival of future requests. The long-term objectives of my research program aim at developing discrete optimization algorithms tailored for dynamic operations management problems and at disseminating online stochastic optimization techniques in the industry. In particular, this five-year program plans 1) to develop an anytime data-driven algorithm based on the improved integral simplex and 2) to solve industrial applications in real-time with online stochastic optimization. This program contributes to the training of 6 graduate students who will get the opportunity to develop state-of-the-arts methods for real industrial problems. These methods have the potential to be game changing technologies as they will introduce a new generation of real-time optimized systems for ride-sourcing services or hospital inventory management for example.