Research And Application Of Distributed Hybrid Flow Shop Scheduling Problem Based On Biogeography Optimization Algorithm

Workshop scheduling problem plays an important role in the intelligent manufacturing system and is the core technology in the modern manufacturing production system.Reasonable and efficient scheduling strategy can significantly improves the manufacturing efficiency of enterprises.The distributed production mode can greatly reduce production costs,shorten the construction period,and improve the risk resistance of production accidents in practical engineering application.This research has important academic significance.Distributed hybrid flow shop scheduling problem(DHFSSP)combined hybrid flow shop scheduling problem with the concept of a distributed system.DHFSSP is an NP-hard problem,which scale is complex and the calculation process is difficult.Meta-heuristic algorithm is a main method for this problem at present.Biogeography-based optimization algorithm(BBO)is a group-based global optimization algorithm inspired by the biogeography theory.Because of its special group information sharing mechanism and strong group adaptability,it plays an important role in the field of optimization.This paper analyzed the core evolution mechanism of the BBO algorithm in detail,and discussed the feasibility of applying the algorithm to the DHFSSP problem.The research results are as follows:(1)Aiming at the DHFSSP problem with minimizing the maximum completion time as the optimization goal,this paper designed a hybrid biogeography-based optimization algorithm(HBBO).This paper used integer coding and NEH rules for coding,proposed a migration operator based on Path-Relinking,and designed a mutation operator based on self-switching neighborhood structure in the HBBO algorithm.The HBBO algorithm combined with the variable neighborhood gradient descent strategy to optimize the optimal solution of the current iteration,and then designed an orthogonal experiment method to determine the key parameters of the HBBO algorithm.Experiment on the data-set showed that the HBBO algorithm had a more stability and faster accuracy than the BBO algorithm and the GA algorithm.(2)Aiming at the distributed hybrid flow shop scheduling problem with the optimization goals of minimizing the maximum completion time and minimizing the waiting time,this paper proposed a multi-objective biogeography-based optimization algorithm(MBBO).In the MBBO algorithm,we used the concept of Pareto frontier solution as an evaluation index,proposed a fitness value model based on non-dominated solutions,and designed a migration operator based on the multi-target Path-Relinking strategy to provide a better migration individual.Secondly,we designed a mutation rate model which combined with the influence factor of the number of iterations to enhance the mining ability of the optimal solution in the later stage of the algorithm.This paper used three indicators to evaluate the effectiveness of the algorithm.The data-set test experiment showed that compared with the NSGA-Ⅱ algorithm,the MBBO algorithm had higher solution accuracy,wider solution distribution,more stability and higher effectiveness.