Reconfigurable assembly line balancing for cloud manufacturing

Abstract

In an attempt to react to the increasing imbalance of assembly line due to the high uncertainty of assembly resources in the cloud manufacturing environment, this study investigates the reconfigurable assembly line balancing problem (ALBP) in a cloud manufacturing environment based on the actual production process. We designed the assembly precedence relation model on the basis of analyzing the characteristics and categories of the reconfigurable ALBP. Thereafter, an optimization model of ALBP under traditional mode is established. Combined with the dynamic and collaborative operation of cloud manufacturing, a workstation information sharing framework for cloud manufacturing is designed, and an equilibrium optimization model of ALBP in cloud manufacturing environment is developed to obtain the maximum productivity and the minimum the load smoothness. Moreover, an improved memetic algorithm is proposed to solve the optimization model, which has strong global and local search capabilities compared with the general algorithm. Finally, performance of the proposed approach is tested on a set of examples, and distinguished results can be acquired by comparing with particle swarm optimization algorithm, simulated annealing and genetic algorithm.