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.
- X.-F. Xie. Round-table group optimization for sequencing problems. International Journal of Applied Metaheuristic Computing, 2012, 3(4): 1-24. [DOI]
- X.-F. Xie, S. Smith, G. Barlow. Schedule-driven coordination for real-time traffic network control. International Conference on Automated Planning and Scheduling (ICAPS), Sao Paulo, Brazil, 2012: 323-331.
- X.-F. Xie, S. Smith, G. Barlow. Coordinated look-ahead scheduling for real-time traffic signal control. International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), Valencia, Spain, 2012: 1271-1272. [DOI]
- X.-F. Xie, S. Smith, L. Lu, G. Barlow. Schedule-driven intersection control. Transportation Research Part C: Emerging Technologies, 2012, 24: 168-189. [DOI]
- X.-F. Xie, G. Barlow, S. Smith, Z. Rubinstein. Platoon-based self-scheduling for real-time traffic signal control. IEEE International Conference on Intelligent Transportation Systems (ITSC), Washington, DC, USA, 2011, 879-884. [DOI]
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).