Particle swarm optimization for minimizing total earliness/tardiness costs of two-stage assembly flowshop scheduling problem in a batched delivery system

This paper considers a two-stage assembly flow shop scheduling problem. When all parts of each product are completed in the first stage, they are assembled into a final product on an assembly machine in the second stage. In order to reduce the delivery cost, completed products can be held until completion of some other products to be delivered in a same batch. The proposed problem addresses scheduling a set of operation with specific due date in a batch delivery system. The aim is to minimize total weighted earliness/tardiness and delivery costs. As the problem is demonstrated to be NP-hard, a genetic algorithm (GA) and a particle swarm optimization (PSO) are presented to solve the problem in real scales. Number of problems are solved by them and the results are compared. The computational results illustrate that the proposed PSO has a qualifier performance than the GA.