Abstract: Based on the characteristics of distributed flexible job-shop problems,a mathematical model was constructed with the objective functions of minimizing completion time,factory energy consumption,and equipment load,and the three objectives were normalized using linear weighted sum method.On the basis of the traditional Seagull Optimization Algorithm (SOA),an adaptive additional variable update strategy has been improved to improve the local optimization ability and convergence accuracy of the algorithm in the later stage. The flight mechanism in the sparrow algorithm was integrated to expand the local optimization range of individuals,and further improve the optimization accuracy.Introducing a variable neighborhood search algorithm for key factories has expanded the neighborhood search range and enhanced the local search ability of the Seagull algorithm. The effectiveness and feasibility of ISOA in solving multi-objective distributed and flexible job-shop problems were verified through standard and actual factory examples.

Key words: distributed and flexible job-shop, multi-objective optimization, variable neighborhood search, adaptive additional variable