Genetic algorithms for batch sizing and production scheduling

This paper proposes three genetic algorithms with different types of crossovers dedicated to solve the problem of short-term scheduling of batch processes. The genetic algorithms suggested are able to determine the amount of batches produced, their production sequence, machinery assignments, and a different batch size for each batch. This is a more complex variation of the batch scheduling problem where genetic algorithms are usually limited to decide the batch production sequence. A manufacturing case study is used to test the performance when minimizing the makespan, in order to compare the proposed genetic algorithms against a simulated annealing implementation, a totally random search, and a simplified genetic algorithm with no crossover (to test the crossovers' efficiency). The results show that one of the crossovers proposed has the fastest convergence in all scenarios, finding the best solutions in short searches. This crossover is slightly outperformed by the simulated annealing only in extensive searches with more iterations. The other two crossovers suggested have a good performance in certain scenarios, but show poor results in others.