We have worked on advanced scheduling strategies for hard computational problems (e.g., Flowshop Scheduling) and real-world applications (e.g., Smart and Scalable Urban Traffic Control). Some of them could be solved using cooperative group optimization with metaheuristic search strategies, where some of them need be handled using decentralized multiagent optimization.

Planning is a notoriously hard combinatorial problem. A plan is a sequence of actions that  can transform an initial state of the world to a goal state. In general, it is PSPACE-complete to determine if a planning instance has any solutions. We have rich experience on creating own algorithms and applying various existing planning algorithms, e.g., heuristic-search planning (HSP) and fast-forward (FF) planning, to solve problems defined in the Planning Domain Definition Language (PDDL).

One problem domain is game planning, e.g., the Lunar Lockout Game, with the goal to move the red spacecraft to the center square:

lunalockout9  lunalockout18 lunalockout27  lunalockout36

Another problem domain is a personal planning problem for improving personal production and operating efficiency of personal time and resource.