MemeticAlgorithmand Genetic Algorithm for the Single Machine Scheduling Problem with Linear Earliness and Quadratic Tardiness Costs


The Single Machine Scheduling (SMS) problem with Multiple Objective Function (MOF) is one of the most representative problems in the scheduling area. In this paper, we consider the SMS problem with linear earliness and quadratic tardiness costs, and no machine idle time. The chosen method is based on memetic algorithm and genetic algorithm.For this purpose, Genetic Algorithms (GA) are a population-based Meta heuristics. They have been successfully applied to many optimization problems. A Memetic Algorithm (MA) is an extension of the traditional genetic algorithm. And we introduce two types of crossover. The methods were tested and various experimental results show that MA performs better than the GA for big jobs but GA was better with small jobs.