Editorial: Learning-driven Optimization for Solving Scheduling and Logistics

Shi Cheng^1*Mitsuo Gen^2*Jie Gao³

• ¹Shaanxi Normal University, Xi'an, China
• ²Tokyo Rika Daigaku - Noda Campus, Noda, Japan
• ³Xi'an Jiaotong University, Xi'An, China

the problem landscape), and nonlinearity (relationships that do not follow a straight line). Prior knowledge 5 of the solved problem and the properties of algorithms are vital for solving complex problems. Therefore, 6 an algorithm should be able to learn from the solving process. Learning-driven methods help agents 7 learn the solution space by taking actions and interacting with the environment. In doing so, agents 8 continuously update their strategies. Reinforcement learning (RL), where an agent learns to make decisions 9 by receiving rewards or penalties based on its actions, and metaheuristic algorithms, which use problem-10 solving strategies that guide the search process to find near-optimal solutions, are emerging approaches 11 that use advanced computational power to address such challenges. These approaches have been actively 12 investigated and applied, especially in scheduling and logistics operations. New algorithm frameworks and theoretical analysis are new approaches to solving complex problems. Real-world applications are essential for validating algorithm performance. Learning-driven optimisation 20 algorithms now address more complex optimisation problems that challenge traditional approaches. In 21 scheduling, large data sets optimise routes, taxi dispatch, dynamic bus scheduling, and other mobility 22 services, boosting efficiency. In logistics, advanced data techniques optimise material movement within 23 and between supply chain entities, such as warehouses, factories, distribution centres, and retail shops. These applications remain complex, so no single solution works for all cases. Practitioners must adapt 25 learning-driven and metaheuristic methods to scheduling and logistics. This research topic aims to discuss new and existing issues in these areas, in particular, to explore We would like to thank the editorial staff and the Journal Manager, Hannah Lee, for their excellent 45 assistance with this research topic throughout the process. We are equally grateful to all the authors for 46 their valuable contributions and to the reviewers for their constructive suggestions, which have greatly 47 improved the quality of the papers presented here. We hope that the reader finds the papers collected in this 48 thematic issue interesting and helpful.