Advances in Mathematical Programming for Optimization Algorithms

Dear Colleagues,

In recent years, advances in mathematical programming have substantially reshaped the landscape of optimization algorithms, driven notably by the escalating complexity of large-scale systems and the concomitant demand for computational efficiency. The convergence of exact methods, including integer linear programming and constraint programming with metaheuristic approaches, has given rise to hybrid frameworks capable of addressing problems previously considered intractable. Concurrently, the integration of machine learning into optimization paradigms represents a significant methodological shift, facilitating the dynamic calibration of parameters and the extraction of latent structures from recurring problem instances. These developments hold relevance across diverse application domains, such as logistics, energy system planning, educational resource scheduling, and telecommunication network optimization. Within this context, the present Special Issue seeks to assemble original contributions that explore the evolving frontiers of mathematical programming in optimization, encompassing theoretical advancements, innovative algorithmic designs, and empirical industrial case studies. The overarching aim is to offer a cross-disciplinary synthesis of recent progress and to delineate promising avenues for future inquiry.

Dr. Said Aqil
Dr. Mohamed Lahby
Guest Editors

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