Call for Tracks

Abstract

This track focuses on applied operations research and optimization methods for complex prescriptive decision-making problems. It welcomes methodological and application-oriented contributions in mathematical programming, combinatorial optimization, exact and heuristic algorithms, metaheuristics, evolutionary and swarm optimization, multiobjective and multitask optimization, and robust, stochastic, and dynamic optimization. Particular interest is given to learning-augmented and data-driven optimization, where artificial intelligence or machine learning is used to improve modeling, search, parameter control, or solution efficiency rather than as a stand-alone predictive task. Application areas include vehicle routing and transportation, scheduling and timetabling, logistics and supply chain optimization, production and service operations, resource allocation, smart manufacturing, and disaster-response planning. The track aims to connect researchers and practitioners through new models, algorithms, computational studies, and real-world applications, with emphasis on solution quality, efficiency, scalability, and practical impact.