A Genetic Algorithm with an incomplete representation for the Job Shop Scheduling Problems

Most of the GA approaches for job shop scheduling problem (JSSP) represent a solution by a chromosome containing the sequence of all the operations and decode the chromosome to a real schedule from the first gene to the last gene. There are two common problems for this kind of GAs, namely, high redundancy at the tail of the chromosome and little significance of rear genes on the overall schedule quality. GAoperators (e.g. the 1-point, 2-point crossover, and some mutation operators, etc.) applied on the real part of the chromosome (only involving the change of the real part of a chromosome) are less likely to create good offsprings, i.e., most likely a waste of evolution (time). In this paper, we propose a genetic algorithm with an incomplete representation (the number of genes is less than the number of operations) and apply it to the JSSPs. In our approach, the most important and the largest part of a schedule is decoded from a chromosome and the rest of the schedule is completed by a simple heuristic rule.