Abstract: Optimization, an integral aspect of engineering, presents formidable challenges in problem-solving. Many of these engineering dilemmas encountered in daily life confront the NP-Hard complexity class, posing substantial hurdles to attaining optimal solutions. Instead, engineers often resort to heuristic methods to procure feasible solutions within reasonable timeframes. These challenges manifest in various realms of operations research, where combinatorial optimization problems emerge as daunting obstacles. Tasks ranging from task assignment to route optimization, crew scheduling, and factory planning encapsulate the breadth of these issues.
Taking a closer look, specific scenarios further accentuate the breadth of combinatorial optimization's domain. Consider, for instance, the intricacies of optimizing robot trajectory planning or the meticulous analysis required for determining the optimal configuration of graphene structures. Moreover, the exploration of quantum optimization opens new avenues for discovering novel chemical materials, presenting promising yet intricate challenges in optimization. Additionally, the pursuit of optimal structural design to mitigate resonance in production processes underscores the diverse applications of optimization across industries.
Despite advancements, the efficacy of quantum optimization algorithms designed for these challenges faces constraints due to prevalent noise levels in contemporary quantum computing. Consequently, there's a growing interest in developing optimization algorithms with shallower depth, aimed at circumventing these limitations. The strategic implementation of such algorithms holds the potential to yield cost-effective solutions, thus reshaping industrial landscapes.
Researchers to Contribute to the Working Group:
Berk Kalelioğlu (METU)
Osman Barış Malcıoğlu (METU)
Mehmet Cengiz Onbaşlı (KÜ)
Deniz Türkpençe (İTÜ)
