Dynamic Configuration Method of Flexible Workshop Resources Based on IICA-NS Algorithm

Abstract

The optimal configuration of flexible workshop resources is critical to production efficiency, while disturbances pose significant challenges to the effectiveness of the configuration. Therefore, this paper proposes a hybrid-driven resource dynamic configuration model and an improved Imperialist Competitive Algorithm hybrid Neighborhood Search (IICA-NS) that incorporates domain knowledge to allocate resources in flexible workshops. First, a hybrid-driven configuration framework is proposed to optimize resource configuration strategies. Then, in the revolutionary step of the Imperialist Competitive Algorithm (ICA), the bottleneck heuristic neighborhood structure is adopted to retain the excellent genes in the imperial so that the updated imperial is closer to the optimal solution; And a population invasion strategy is proposed further to improve the searchability of the ICA algorithm. Finally, the simulation experiments are carried out through production examples on flexible workshop production cases, and the proposed algorithm is applied. Compared with traditional ICA, genetic algorithm (GA), particle swarm optimization algorithm (PSO), moth-flame optimization (MFO) and sparrow search algorithm (SSA), the proposed method and algorithm effectively solve flexible workshops’ resource dynamic configuration problems.