Comparison of Two Genetic Algorithms in Solving Tough Job Shop Scheduling Problems

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In order to solve job shop scheduling problems (JSSPs) by a genetic algorithm (GA), one should first design an encoding scheme, on which a search space is constructed. This paper proposes two encoding formats; one is a string code format that leads to the redundancy of the code space, and the other is a matrix code format that overcomes the redundancy but only insures an approximate representation. Two corresponding genetic algorithms (GAs) are designed for investigating the encoding effectiveness. Complex problems like the JSSPs usually require complicated code structures, which in turn call for delicate design of genetic operations such as crossover. The code structures of the two encoding formats are analyzed and compared. Test-runs of the two GAs on several tough JSSP benchmarks are performed for a demonstration of the validation of the proposed methods.

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