A Hybrid Particle Swarm optimization for Cost-oriented Robotic Assembly Line Balancing Problem

Abstract

Robots have been widely utilized in assembly lines to achieve higher production efficiency and better product quality. Due to relatively high purchasing costs of robots, the cost of robotic assembly lines is an important goal to be optimized. A mixed integer linear programming mathematical model is proposed to minimize the total cost including purchasing cost and operation cost. Based on the model, the optimal solutions of instances presented in this paper are obtained by LINGO in an acceptable time to verify the correctness of algorithms. To solve practical problems, a discrete particle swarm optimization (DPSO) for cost-oriented robotic assembly line balancing problem type-I is developed to find near-optimal solutions. Furthermore, a new algorithm combining dynamic programming and the DPSO (DPPSO) is proposed. Seventeen instances are generated to compare the performance of DPSO, DPPSO and the existing memetic algorithm. The computational results show that the DPPSO is superior to other two algorithms in finding high-quality solutions.