A Genetic Algorithm for Single Machine Total Weighted Tardiness Scheduling Problem

This paper presents a new genetic algorithm for the single machine total weighted tardiness scheduling problem, which is a strong NP-hard problem. The developmental focus has been on techniques to speed up execution in order to solve large-size problems. This genetic algorithm uses the natural permutation representation of a chromosome for encoding simplicity. Heuristic dispatching rules combined with a random method are used to create the initial population for improving (decreasing) the search space, consequently improving searching simplicity. Position-based crossover and order-based mutation operators are used for operator simplicity. The best members of the population during generations are used for searching simplicity, too. Extensive computational results for randomly generated problems with up to 500 jobs show the good performance and the efficiency of the developed algorithm.

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