Online stochastic optimization for dynamic operations management systems

动态运营管理系统的在线随机优化

• 批准号: RGPIN-2021-03796
• 负责人: Legrain, Antoine
• 金额: $ 1.89万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759421
• 关键词: Online; stochastic; optimization; dynamic; operations

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.

该研究计划旨在通过开发新的数据驱动的离散优化方法来创建针对动态操作管理系统的在线随机优化的最新知识。这些问题中有许多出现在乘车采购，食品交付，诊所约会，库存管理和劳动力管理等服务中。更确切地说，当请求（例如，乘车）进入系统时，操作员将分配一些资源（驱动程序）以实现服务。在云计算和移动应用程序的时代，这些操作问题已经变得至关重要，因为所有服务都必须提出适合其客户需求的实时应用程序，以便在数十亿美元的扩大市场中具有竞争力。我的研究重点是在线随机优化，以通过数据驱动算法解决这些动态问题。在线随机优化提出了一个范式，以定期重新调整决策计划，以考虑新请求以及有关未来请求到达的几种情况，以有效地解决这些问题。我的研究计划的长期目标旨在开发针对动态运营管理问题的离散优化算法，并传播行业中的在线随机优化技术。特别是，这项为期五年计划的计划1）基于改进的整体单纯胶和2）以在线随机优化实时求解工业应用程序，开发以数据驱动的算法开发。该计划有助于对6名研究生进行培训，他们将有机会开发针对实际工业问题的最先进方法。这些方法有可能改变游戏的技术，因为它们将引入新一代的实时优化系统，以用于乘车服务或医院库存管理。