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Real-world optimization problems are difficult to model accurately because many objectives and constraints reside in domain experts'tacit knowledge, making them hard to formalize. As a result, optimization models often contain miscalibrated objectives, missing constraints, or omitted decision variables, leading to solu...
Recent advances in agentic heuristic design use AI agents and execution feedback to automate algorithm discovery for challenging optimization problems. In many practical settings, high-quality solutions must be obtained under strict runtime constraints, motivating hybrid approaches that combine problem-specific heurist...
A large-scale online discrete optimization model is formulates and a new online sampling optimization (OSO) algorithm is developed that anticipates future demand by repeatedly simulating future arrivals and reoptimizing decisions over time, demonstrating the real-world impact of optimization in cloud infrastructure man...
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