Multi‐period planning models result in solutions which are feasible at the boundaries of the periods but may be infeasible within the periods. The composite algorithm presented here (i) solves coarse multi‐period MILP model structure for production planning; (ii) sequences operations via a genetic algorithm to minimise switching; (iii) verifies schedule feasibility via agent‐based simulation and local logical decision making; and (iv) if infeasible, re‐partitions the time horizon into multi‐periods and resolves from (i) until feasible. Application of the algorithm to gasoline blending illustrates its effectiveness in computing feasible plans and schedules for such systems. © 2012 Canadian Society for Chemical Engineering

Exponential growth in number of possible strategies with the increase in number of relations in a query has been identified as a major problem in the field of query optimization of relational databases. Present database systems use exhaustive search to find the best possible strategy. But as the size of a query grows, exhaustive search method itself becomes quite expensive. Other algorithms like A* algorithm, Simulated Annealing etc. have been suggested as a solution. However, all these algorithms fail to produce the best results; necessarily required for query execution. We did some modifications to the A* algorithm to produce a randomized form of the algorithm and compared it with the original A* algorithm and exhaustive search. The comparison results have shown improved A* algorithm to be almost equivalent in output quality along with a colossal decrease in search space in comparison to exhaustive search method.