An ant colony hybrid simulated annealing algorithm for collaborative optimization of robotic mixed-model parallel two-sided assembly lines balancing

Abstract

Aiming at the flexible production of small-lot and multi-variety assembly lines in intelligent manufacturing systems, a robot-operated mixed-model sequencing and parallel two-sided assembly line assembly system is proposed. Systematic co-optimization in three dimensions of task assignment at mated-stations and multi-line stations, multi-product mixed-model sequencing and task line balancing, as well as robot types and task time at workstations are solved. Mixed-model parallel two-sided assembly line system and its key terms are defined. A mathematical model of the mixed-model robotic parallel two-sided assemblyGreen line type-II balancing problem (MRPTALBP-II) considering energy consumption is developed. Based on the ant colony optimization algorithm (ACO) and simulated annealing algorithm (SA) for solving the model, the ant colony hybrid simulated annealing algorithm (ACHSA) is proposed to solve the model. A new initial solution encoding, multiple neighborhood structures and dual pheromone matrix updating method are designed,in order to avoid the algorithm from falling into local optimum and to expand the search range. Three sets of calculations are obtained for comparative analysis in conjunction with classical arithmetic cases. The results show that the ACHSA outperforms the SA and the ACO, with an excellence rate of 100% for large-scale arithmetic cases and 61% for small-scale arithmetic cases, which verifies the validity of the model and the algorithm. Firstly, the MRPTALBP-II is successfully solved, which provides a useful reference for the solution of multi-factor collaborative problems of complex systems.