A number of intelligent scheduling models for product distribution have been introduced in this research. The databases, representing scheduling requirements and results, are described using object oriented modeling approach. The optimal schedule for product distribution considering relevant constraints is identified using two optimization approaches: state space search and genetic algorithm. State space search is employed when search space is not large. Genetic algorithm, on the other hand, provides a robust mechanism for identifying the global optimal schedule when the search space is large. Different heuristic functions, considering traveling time and distance, have been developed to evaluate the schedules generated in the optimization. In addition to single-vehicle based scheduling, scheduling considering a number of vehicles has also been studied.

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