Chaotic Sparrow Search Algorithm Based Scheduling for Flexible Job Shop with Automatic Guided Vehicle

To solve the scheduling problem for a flexible job-shop with some automatic guided vehicles (AGVs), an improved chaotic sparrow algorithm is proposed to minimize the maximum completion time of jobs. Firstly, A mathematical model for the dual resource scheduling problem was established to optimize machines and AGVs simultaneously, and the two states of AGVs for both with and without loads were considered. Then, a chaotic sparrow search algorithm (CSSA) with feedback mechanism is proposed to balance search speed and convergence accuracy. A three-layer coding and greedy decoding of chaotic sparrow algorithm are designed to avoid it trapping in local optimal solution, and the Hénon chaotic mapping is used to improve the population diversity and convergence speed. Besides, an adaptive strategy improvement for safety values, discoverer as well as scout parameters, and multiple learning strategies are used. Finally, eight sets of standard arithmetic cases are selected for case study and simulation. The results show that the proposed algorithm CSSA has better optimization performance compared with the original sparrow search algorithm algorithm.