A genetic algorithm for the single machine total weighted tardiness problem

Scheduling problems are important NP-hard problems. Genetic algorithms can provide good solutions for such optimization problems. In this paper, we present a genetic algorithm to solve the single machine total weighted tardiness scheduling problem, which is a strong NP-hard problem. The algorithm uses the natural permutation representation of a chromosome, heuristic dispatching rules combined with random method to create the initial population, position-based crossover and order-based mutation operators, and the best members of the population during generations. The computational results of problem examples with 10 and 25 jobs and general problems with 50, 100, 200 and 500 jobs show the good performance and the efficiency of the developed algorithm.