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Abstract
Stochastic simulation is typically deployed for offline system design and control; however, the time delay in executing simulation hinders its application in making online decisions. With the rapid growth of computing power, simulation-based online optimization has emerged as an attractive research topic. We consider a problem of ranking and selection via simulation in the context of online decision-making, in which there exists a short time (referred to as online budget) after observing online scenarios. The goal is to select the best alternative conditional on each scenario. We propose a Unified Offline and Online Learning (UOOL) paradigm that exploits offline simulation, online scenarios, and online simulation budget simultaneously. Specifically, we model the mean performance of each alternative as a function of scenarios and learn a predictive model based on offline data. Then, we develop a sequential sampling procedure to generate online simulation data. The predictive model is updated based on offline and online data. Our theoretical result shows that online budget should be allocated to the revealed online scenario. Numerical experiments are conducted to demonstrate the superior performance of the UOOL paradigm and the benefits of offline and online simulation.
Acknowledgments
We thank the editors and anonymous reviewers for valuable comments.
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Notes on contributors
Haitao Liu
Haitao Liu received his BE and MS degrees in industrial engineering and management science from Sichuan University, China, in 2015 and 2018. Currently, he is a PhD candidate in the Department of Industrial Systems Engineering and Management (ISEM) at National University of Singapore (NUS). His research interests include simulation optimization and statistical learning.
Jinpeng Liang
Jinpeng Liang received his BS and MS degrees from Dalian Maritime University, Dalian, China, in 2013 and 2016, respectively. In 2020, he received his PhD degree in system science from Beijing Jiaotong University, Beijing, China. He is now an associate professor at the School of Transportation Engineering, Dalian Maritime University, Dalian, China. His research interests include stochastic system modeling and optimization.
Loo Hay Lee
Loo Hay Lee received his PhD degree in engineering science from Harvard University, USA. He currently is a professor in the Department of ISEM at NUS. His research interests include logistics, vehicle routing, supply chain modeling, and simulation-based optimization.
Ek Peng Chew
Ek Peng Chew received his PhD degree in industrial engineering from Georgia Institute of Technology, USA. He currently is a professor in the Department of ISEM at NUS. His research areas are in port logistics and maritime transportation, simulation optimization and inventory management.