In the context of simulation-based optimisation, this paper reviews recent work related to the role of metaheuristics, matheuristics (combinations of exact optimisation methods with metaheuristics), simheuristics (hybridisation of simulation with metaheuristics), biased-randomised heuristics for ‘agile’ optimisation via parallel computing, and learnheuristics (combination of statistical/machine learning with metaheuristics) to deal with NP-hard and large-scale optimisation problems in areas such as transport and logistics, manufacturing and production, smart cities, telecommunication networks, finance and insurance, sustainable energy consumption, health care, military and defence, e-marketing, or bioinformatics. The manuscript provides the main related concepts and updated references that illustrate the applications of these hybrid optimisation–simulation–learning approaches in solving rich and real-life challenges under dynamic and uncertainty scenarios. A numerical analysis is also included to illustrate the benefits that these approaches can offer across different application fields. Finally, this work concludes by highlighting open research lines on the combination of these methodologies to extend the concept of simulation-based optimisation.

在基于模拟的优化的背景下，本文回顾了与元启发式、数学（精确优化方法与元启发式的组合）、模拟启发式（模拟与元启发式的混合）、“敏捷”优化的有偏随机启发式的作用相关的最新工作通过并行计算和学习启发式（统计/机器学习与元启发式的结合）来处理NP-hard运输和物流、制造和生产、智慧城市、电信网络、金融和保险、可持续能源消费、医疗保健、军事和国防、电子营销或生物信息学等领域的大规模优化问题。手稿提供了主要的相关概念和更新的参考资料，说明了这些混合优化-模拟-学习方法在解决动态和不确定场景下丰富的现实挑战中的应用。还包括数值分析，以说明这些方法可以在不同应用领域提供的好处。最后，这项工作最后强调了将这些方法结合起来以扩展基于模拟的优化概念的开放研究路线。