Amidst escalating threats from disasters compounded by the climate crisis, communities globally are intensifying efforts to bolster disaster resilience. Recognizing the pivotal role of Information and Communication Technology (ICT) in disaster risk management, there is a growing emphasis on addressing the intricacies involved, including diverse actors, infrastructures, and datasets with distinct functions, rules, and protocols. This complex landscape poses challenges for decision-making and event coordination, highlighting the crucial need for effective semantic interoperability among stakeholders. This paper introduces the RES-Q (RESCUE) approach, an innovative information technology solution prioritizing real-time recommendation and orchestration of post-disaster response plans, with a specific focus on enhancing the mobility of relevant actors. The RES-Q approach seamlessly integrates an expert system, a workflow execution engine, and a Reinforcement Learning (RL) agent. A multi-layered ontological model encapsulates the essential knowledge streams and a semantic rule repository for the modeling of response plans. During the design mode, the RL agent optimizes response plans for stakeholders, emphasizing their efficient mobility and actions within the ontological infrastructure. Throughout the execution of post-disaster plans, the system reasons over the rules and recommends the next steps of the orchestration process. The paper elaborates on the key modeling artifacts of the proposed approach and outlines the architecture of the system, providing a comprehensive overview of its components and functionalities.