A multi‐objective hybrid evolutionary search algorithm for parallel production line balancing problem including disassembly and assembly tasks

Abstract

AbstractThis paper proposes a multi‐objective hybrid evolutionary search algorithm to simultaneously optimize the number of workstations, the idle index and the quantity of the production equipment required for the parallel production line balancing problem including disassembly and assembly tasks. The proposed algorithm uses the crossover operation and the mutation operation to generate neighborhood individuals of the current evolutionary population. Based on the mixed population composed of the current evolutionary population and the newly generated neighborhood population, a novel population update method is developed. The individuals of the new generation population are composed of Pareto dominant individuals filtered from the mixed population and several individuals randomly selected from the dominated individuals. Then the local search strategy based on simulated annealing operation is applied to the new generation of population individuals to overcome the defect of premature convergence of the proposed algorithm. By solving test examples of different scales and comparing them with a variety of classical algorithms, the advantages in the aspects of the diversity, the convergence, and the distribution of the solution results of the proposed algorithm are proved. Finally, the established mathematical model and the developed algorithm are applied to the parallel production line designed for the bogie remanufacturing production line of a railway freight car maintenance company. The advantages of the parallel production line in improving the production efficiency and reducing the production cost are identified.