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Production scheduling is characterised by closely related problems which have different manufacturing capabilities, many competing constraints, and performance requirements. A sparse matrix representation for such problems is presented and two new genetic operators are described, which ensure that job process sequence is maintained and the generated schedules are valid. Results are presented which show that the representation is effective at providing relevant information for interpreting schedules easily. We show that the cost of computing solutions with the sparse matrix representation is linear with the total number of processes. An extension to the genetic operators is discussed where the number of processes for different jobs is unequal. We show the performance of our representation through empirical results for the Job Shop Scheduling Problem (JSSP), although the representation is not restricted to the JSSP.

A sparse matrix representation for production scheduling using genetic algorithms