Nowadays, operations research and decision support approaches are increasingly faced with the consideration of uncertainties and chronic disruptions, as well as occasional ones. These include data from information systems and sensors, along with uncertainties and variability in input data, and the dynamic properties of optimization problems, such as flexibility, resilience, and responsiveness. Furthermore, the data, constraints, and objective function may not remain stationary nor fully known; they may only be gradually revealed over time.
Dynamic optimization, robust optimization, and simulation-optimization approaches can be used to address these challenges. Another challenge is the real-time aspect, which often requires adapting to an uncertain environment by reacting quickly and adaptively.
Moreover, this special issue also explores how Artificial Intelligence (AI) tools and technologies can identify, analyze, and manage various uncertainties. AI techniques enable the analysis of large volumes of data in real-time, facilitating proactive decision-making and issue resolution. Additionally, machine learning algorithms, using both historical and real-time data, can detect patterns and forecast disruptions.
We invite submissions focusing on recent advancements in OR and decision support systems. This issue aims to explore innovative approaches for tackling complex challenges associated with uncertainty and disruption management.
The main topics of interest are (but not limited to):
Important Dates
Submission Deadline: January 30, 2025
Notification of Acceptance: March 30, 2025
Final Version Due: May 30, 2025
Guest Editors
Dr. Jaouad Boukachour
Le Havre Normandy University, Le Havre, France
Prof. Abdelhamid Benaini
Le Havre Normandy University, Le Havre, France
