Solving a new cost-oriented assembly line balancing problem by classical and hybrid meta-heuristic algorithms

In this study, a new cost-oriented assembly line balancing problem is proposed and formulated. A single objective function consisting of minimizing the cost associated with equipment, labor wage, and station establishment is considered for the problem. This problem is more complicated comparing to the literature as worker qualification is considered for determining his/her wage. As this problem is of NP-hard optimization problems, some meta-heuristic solution approaches, e.g., simulated annealing, variable neighborhood search, genetic algorithm, tabu search, population-based simulated annealing, and their hybrid versions are proposed. In the proposed algorithms, a novel encoding–decoding scheme is applied. This scheme uses the Hungarian method to assign the workers to the station to reduce the total wage of the workers. To study the performance of the proposed meta-heuristic algorithms, ten test problems are generated randomly, and using one of them the parameters of the algorithms are tuned by the Taguchi method. The final experiments on the proposed algorithms and the test problems show that in the most of the experiments, among the proposed algorithms, the single-solution-based algorithms, except TS, perform better than the population-based algorithms, especially for the case of large size test problems.