Friday, 13 December 2013


Rumana Anjum
Lecturer in CS dept
Brindavan PU and Degree College
Boopsandra, Bangalore

Scheduling problems most often use heuristic algorithms to search for the optimal solution. Heuristic search methods suffer as the inputs become more complex and varied. This type of problem is known in computer science as an NP-Hard problem. This means that there are no known algorithms for finding an optimal solution in polynomial time.
Genetic algorithms are well suited to solving production scheduling problems, because unlike heuristic methods genetic algorithms operate on a population of solutions rather than a single solution. To apply a genetic algorithm to a scheduling problem we must first represent it as a genome. One way to represent a scheduling genome is to define a sequence of tasks and the start times of those tasks relative to one another. Each task and its corresponding start time represent a gene.
With genetic algorithms we then take this initial population and cross it, combining genomes along with a small amount of randomness (mutation). The offspring of this combination is selected based on a fitness function that includes one or many of our constraints, such as minimizing time and minimizing defects. We let this process continue either for a pre-allotted time or until we find a solution that fits our minimum criteria. Overall each successive generation will have a greater average fitness i.e. taking less time with higher quality than the proceeding generations. In scheduling problems, as with other genetic algorithm solutions, we must make sure that we do not select offspring that are infeasible, such as offspring that violate our precedence constraint.

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