Efficient job scheduling is important to the performance of many systems. These systems include multiprocessor platforms, employee scheduling tasks, flexible manufacturing systems, and autonomous navigation planning. We will explore applying a Genetic Algorithm (GA) strategy currently used in the Traveling Salesperson Problem (TSP) to solve the Job Scheduling problem. We examine the Matrix Crossover (MX) method used along with the 2-opt inversion operator which have given promising results in solving the TSP operator. This work advances the use of the MX operator and provides an analysis of how the operator effects schema. We present results that are within 13.5 of the lower-bound solution to a popular job scheduling problem (JSP). Results are also presented showing a reasonable improvement when combining the MX and an inversion operator.
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