A Survey on Hybrid Modelling using Data Science Techniques and Computer Simulation
通过调查117位研究者与实践者，综述了将监督学习、强化学习等数据科学技术与基于智能体的建模、离散事件仿真等仿真方法结合的混合模型，指出其能提升精度、降低成本并改进决策。
Using mixed methods approaches for problem-solving has a long history in Operations Research (OR) and Modelling and Simulation (M&S). Data Science (DS), with its strong alignment with the disciplines of Mathematics, Statistics and Computer Science, has experienced a surge in interest in recent decades and is now increasingly applied in business and management. Similarly, M&S, a sub-field of OR, has a long history of theoretical and practical work in the dynamic modelling of operational systems. Thus, hybrid models employing DS techniques such as supervised machine learning and reinforcement learning with M&S approaches like agent-based modelling and discrete-event simulation enable us to realise synergies associated with multiple methods; their combined application potentially goes beyond what could be possible by employing single techniques. Through a comprehensive survey of 117 researchers and practitioners, our work aims to identify the key challenges and opportunities in developing hybrid models. Our findings suggest that hybrid M&S-DS models can improve model accuracy, reduce computational costs, improve efficiency and potentially lead to improved decision-making. Our study advances the M&S knowledge base by broadening the methodological foundations that current and future researchers engage with, showcasing how combining traditional M&S approaches with emerging DS techniques deepens critical understanding of mixed methods approaches. By capturing both theoretical frameworks and practitioner insights, it supports the development of a more contemporary and comprehensive view of M&S practice.