The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …

Recently there has been a surge of interest in operations research (OR) and the machine
learning (ML) community in combining prediction algorithms and optimization techniques to …

The integration of automation and process optimization within the context of AI-powered
digital transformation has emerged as a pivotal strategy for organizations aiming to enhance …

Astoundingly, recent technological advancements have enabled robots to display emotions.
Yet, while emotional expression is valued in the field of service, understanding emotions in …

The rapid growth of urban populations worldwide imposes new challenges on citizens' daily
lives, including environmental pollution, public security, road congestion, etc. New …

Business analytics aims to enable organizations to make quicker, better, and more
intelligent decisions with the aim to create business value. To date, the major focus in the …

Although scholars in management recognize the value of harnessing big data to
understand, predict and respond to future events, there remains little or very limited overview …

Forecasting demand and determining safety stocks are key aspects of supply chain
planning. Demand forecasting involves predicting future demand for a product or service …

Ports are pivotal nodes in supply chain and transportation networks, in which most of the
existing data remain underutilized. Machine learning methods are versatile tools to utilize …

Decision-focused learning (DFL) is an emerging paradigm that integrates machine learning
(ML) and constrained optimization to enhance decision quality by training ML models in an …