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In this work we study an over-constrained scheduling problem where constraints cannot be relaxed. This problem originates from a local defense agency where activities to be scheduled are strongly ranked in a priority scheme determined by planners ahead of time and operational real-time demands require solutions to be available almost immediately. A hybrid framework is used which is composed of two levels. A high-level component explores different orderings of activities by priorities using Tabu Search or Genetic Algorithm heuristics, while in a low-level component, constraint programming and minimal critical sets are used to resolve conflicts. Realdata used to test the algorithm show that a larger number of high priority activities are scheduled when compared to a CP-based system used currently. Further tests were performed using randomly generated data and results compared with CPLEX. The approach provided in this paper offers a framework for problems where all constraints are treated as hard constraints and where conflict resolution is achieved only through the removal of variables rather than constraints.

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In a rapidly changing environment, the behavior and decision-making power of agents may have to be adaptive with respect to a fluctuating autonomy. In this paper, a centralized fuzzy approach is proposed to sense changes in environmental conditions and translate them to changes in agent autonomy. A distributed coalition formation scheme is then applied to allow agents in the new autonomy to renegotiate to establish schedule consistency. The proposed framework is applied to a real-time logistics control of a military hazardous material storage facility under peaceto-war transition.

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