Minimizing total tardiness on parallel machines based on Genetic Algorithm

The problem of scheduling jobs on parallel machines to minimize total tardiness is NP problem. The properties of p // T macr problem were discussed. Base on the properties, the decoding function block decoding algorithm (BDA) of Genetic Algorithm (GA) was designed. Between the encoding space and the solution space, the element numbers of the encoding space are not necessary large than elements of the solution space, It only necessary that at least one element in encoding space corresponds an optimal solution by the decoding function. The encoding space of this paper is n-list space which element is a permutation of n elements. It is proved that at least one optimal solution is corresponded to a permutation of encoding space by the BDA. By the computational experiments, The performance of GA is best than four Heuristic Algorithms (WI,DS,TPI,KPM).