Research on Optimization of Dual-Resource Batch Scheduling in Flexible Job Shop

Abstract

With the rapid development of intelligent manufacturing and the changes in market demand, the current manufacturing industry presents the characteristics of multi-varieties, small batches, customization, and a short production cycle, with the whole production process having certain flexibility. In this paper, a mathematical model is established with the minimum production cycle as the optimization objective for the dual-resource batch scheduling of the flexible job shop, and an improved nested optimization algorithm is designed to solve the problem. The outer layer batch optimization problem is solved by the improved simulated annealing algorithm. The inner double resource scheduling problem is solved by the improved adaptive genetic algorithm, the double coding scheme, and the decoding scheme of Automated Guided Vehicle (AGV) scheduling based on the scheduling rules. The time consumption of collision-free paths is solved with the path planning algorithm which uses the Dijkstra algorithm based on a time window. Finally, the effectiveness of the algorithm is verified by actual cases, and the influence of AGV with different configurations on workshop production efficiency is analyzed.