The rapid development of AI offers new perspectives for decision-making under uncertainty. AI can contribute to uncertainty representation, improve solution performance, and enhance real-world applications in sectors such as energy and transportation. In this context, it is important to investigate the integration of AI with classical decision-making under uncertainty methods, such as stochastic optimisation.
Being able to capture the value of flexibility is a key advantage of stochastic optimisation over deterministic counterparts. However, modelling flexibility sufficiently has been a key challenge since stochastic optimisation was introduced. New tools like machine learning and AI can have a significant impact on how uncertainty is modelled and how stochastic optimisation models are solved. These developments ultimately allow decision makers to better exploit the value of flexibility and make more informed decisions in a continuously changing world.
Despite the promising future of AI and stochastic optimisation, future research is needed to translate emerging ideas into new methods, models, and tools.
Therefore, in this collection, we aim to attract original and innovative papers from all domains of computational management science that focus on the theoretical and empirical integration of AI with stochastic optimisation. We welcome submissions focusing on, but not limited to, the following topics: (1) AI for uncertainty representation, (2) AI for stochastic and robust optimisation, (3) AI driven metrics for solution performance evaluation, (4) Applications of AI and stochastic optimisation, (5) AI in computational methods for stochastic optimisation.
Papers should be submitted electronically using the Computational Management Science submission system and following the instructions for authors. When submitting, authors are requested to choose the collection “Interfaces between AI and decision-making under uncertainty” to indicate the paper is intended for this collection.
Please find more details about this special issue and submit your paper here: https://link.springer.com/collections/ehgfcjfbhe.
Please direct questions about the collection to the Guest Editors:
Dr Alan King
Professor Enza Messina
Dr Hongyu Zhang
Dr Vincent Leclère