A genetic algorithm-based model for solving multi-period supplier selection problem with assembly sequence

Under fierce market competition, only products that can meet market demands timely and are competitive can enjoy advantages in the market. Production planning is important in enhancing product competitiveness by effectively reducing both production cost and time. To complete the planning task, a better assembly sequence that includes selecting suitable part suppliers and satisfying the multi-period demands should be designed. In this paper, a mathematical model is presented for dealing with this planning problem, and its objective is to minimise the value of integrated criteria. A hybrid heuristic algorithm, which involves guided genetic algorithm combined with Pareto genetic algorithm, known as Guided-Pareto genetic algorithm (Gu-PGA), is developed for solving the addressed problem. Finally, experiments are conducted to validate the proposed algorithm. The results demonstrate that the Gu-PGA is more effective in solving the multi-period supplier selection problem.