A Cultural Algorithm for Solving the Job Shop Scheduling Problem

In this chapter, we propose an approach for solving the job shop scheduling problem using a cultural algorithm. Cultural algorithms are evolutionary computation methods that extract domain knowledge during the evolutionary process. Additional to this extracted knowledge, the proposed approach also uses domain knowledge given “a priori” (based on specific domain knowledge available for the job shop scheduling problem). The proposed approach is compared with respect to a Greedy Randomized Adaptive Search Procedure and to a Parallel Genetic Algorithm. The cultural algorithm proposed is able to produce competitive results with respect to the two approaches previously indicated at a significantly lower computational cost than at least one of them and without using any sort of parallel processing.

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