The recent advancements in optimization and scheduling problems across various domains, including urban management, warehousing, and healthcare, demonstrate a shift towards more adaptive and dynamic solutions. In urban management, the focus has been on integrating digital transformation with smart city concepts, particularly in location allocation problems that impact cost management and citizen satisfaction. In warehousing, innovative frameworks that predict task flow and pre-schedule tasks have shown significant improvements in efficiency, reducing empty running rates by over 50%. Surgical workflow anticipation has seen advancements with adaptive graph learning methods that leverage spatial information, enhancing surgical safety and operating room efficiency. Additionally, in home healthcare, decision support systems employing adaptive large neighborhood search algorithms have proven effective in managing scheduling and routing with lunch break considerations, improving service responsiveness. These developments highlight a trend towards more intelligent, adaptive, and integrated solutions that address complex real-world problems with greater efficiency and accuracy.