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sidenav header background[11月29日]能源与气候变化经济学workshop
发布日期：2019-11-25 09:23 来源：
时间：2019年11月29日（周五）12:00-14:00地点：艺园会议室主讲人：宋洁（北京大学工学院）主持人：徐晋涛、王敏、黄卓、邢剑炜题目：Perishable Resource Allocation with Online Contextual Learning摘要：We formulate a novel class of online matching problems with learning. In these problems, randomly arriving customers must be matched to perishable resources so as to maximize a total expected reward. The matching accounts for variations in rewards among different customer-resource pairings. It also accounts for the perishability of the resources. For concreteness, we focus on healthcare platforms, but our work can be easily extended to other service applications. Our work belongs to the online resource allocation streams in service system. We propose the first online algorithm for contextual learning and resource allocation with perishable resources. Our algorithm explores and exploits in distinct interweaving phases. We propose a pioneer algorithm that helps service system to optimize resource allocation decisions while learns the uncertain reward of matching customer-resource pairings.
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sidenav header background[11月29日]能源与气候变化经济学workshop
发布日期：2019-11-25 09:23 来源：
时间：2019年11月29日（周五）12:00-14:00地点：艺园会议室主讲人：宋洁（北京大学工学院）主持人：徐晋涛、王敏、黄卓、邢剑炜题目：Perishable Resource Allocation with Online Contextual Learning摘要：We formulate a novel class of online matching problems with learning. In these problems, randomly arriving customers must be matched to perishable resources so as to maximize a total expected reward. The matching accounts for variations in rewards among different customer-resource pairings. It also accounts for the perishability of the resources. For concreteness, we focus on healthcare platforms, but our work can be easily extended to other service applications. Our work belongs to the online resource allocation streams in service system. We propose the first online algorithm for contextual learning and resource allocation with perishable resources. Our algorithm explores and exploits in distinct interweaving phases. We propose a pioneer algorithm that helps service system to optimize resource allocation decisions while learns the uncertain reward of matching customer-resource pairings.
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