A group decision making scheduling method for flexible job shop integrating blockchain and rule mining

The future workshop will be an intelligent one based on the Cyber-Physical System paradigm, where each device functions as an agent. Each agent will possess independent data perception and reasoning capabilities, enabling it to operate autonomously and freely join or leave the agent network. Production scheduling will be the result of multi-agent collective decision-making. Traditional centralized scheduling methods are no longer applicable to such workshops, and existing distributed scheduling approaches remain incomplete. Specifically, current distributed scheduling methods still exhibit traces of centralized scheduling and have not fully realized decentralized scheduling in a strict sense. To address this deficiency, we propose a multi-objective flexible job shop distributed scheduling method. In this method, we improve the neighborhood search algorithm by generating a high-quality initial solution, selecting effective critical operation blocks, and using rule extraction to search for the optimal solution. To achieve complete decentralization of scheduling, blockchain is introduced and redesigned for distributed processing of data. Some numerical experiments, based on well-known benchmark instances, are carried out. The results verify the feasibility and competitiveness of the scheduling method. The solution of this problem has important academic significance and engineering value for intelligent factory design.