A hybrid genetic algorithm for job shop scheduling problem to minimize makespan

A hybrid genetic algorithm is presented for the job shop scheduling problem. The chromosome representation is based on random keys. A chromosome includes genes representing the relative priorities of all operations and genes determining the idle time permitted on a machine before processing an operation. The SPV (smallest position value) rule is used to convert a real number vector to a job repetition representation. Then the schedule is constructed using the hybrid scheduler that introduces parameters to control the scope of search space. Finally, a neighborhood-based local search is used to improve the solution quality. The experimental results on the well-known benchmark instances show the proposed algorithm is very effective and competitive with other methods in literatures.