Genetic Algorithm's meta heuristics and inherent parallel approach for exploration makes it a prominent candidate solution scheme for stochastic Scheduling. Their appropriateness to combat uncertainty in stochastic attributes of the problem domain and their robustness towards combinatorial optimization, especially for the problems with conflicting goals encourages researchers to apply these methods to NP hard class of resource constrained stochastic scheduling. Motivated by resource constrained stochastic scheduling problem we have considered an extended version of this research problem with unknown precedence among the entities to be scheduled and perishable resources. In this work, we have proposed a modified pittsburg approach of genetic algorithm for optimization and to learn precedence order among the entities involved .Efficiency and merits of the proposed scheme are evident from the results.

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Pittsburg Approach of Genetic Algorithm Greedy Scheduling Resource Constraint Scheduling