New parallel genetic algorithms for the single-machine scheduling problems in agro-food industry

this paper investigates the multi-objective single-machine scheduling problems in agro-food industry. These problems are strongly NP-hard and metaheuristics are known for theirs adaptability to this kind of problems. In this paper, is developed a novel parallel genetic algorithm to solve the single-machine scheduling problems. A comparison between the conventional and parallel versions of genetic algorithm is provided. It relates to the quality of the solution and the execution time of the two approaches. Computational experiments on benchmark data sets show that the proposed approach reach better solutions in short computational times.