Research On Hybrid Flow Shop Scheduling Problem Based On Migrating Birds Optimization Algorithm

With the development of workshop automation technology and the improvement of social energy awareness,how to improve production efficiency and reduce production energy consumption becomes a difficult problem for enterprises.Shop scheduling is the key technology to solve this problem.Hybrid Flow Shop(HFS)scheduling problem is a classic scheduling problem,which exists widely in actual manufacturing.In this paper,the Migrating Birds Optimization(MBO)algorithm is used to study the single-objective and multi-objective HFS problems.The main contents include:Firstly,an improved migrating birds optimization(IMBO)algorithm is proposed to solve the HFS problem with the makespan criteria.In the IMBO algorithm,a random iteration list decoding method is proposed,combining it with the original list decoding method,a two-stage decoding method is proposed.The leader and the followers evolve through the optimal insertion operation and the optimal exchange operation.Four neighborhood structures are designed to perform local search for followers.Using IMBO to solve 24 difficult benchmark instances and 10 large-scale benchmark instances,a new better solution of a large-scale benchmark instance is obtained,therefore,the effectiveness of the proposed algorithm is verified.Secondly,the HFS problem with the total energy consumption(TEC)criteria is studied and the MBO algorithm is used to solve it.Firstly,the composition of workshop's TEC is analyzed,and the mixed integer linear programming model of HFS with TEC criteria is given.Then the MBO algorithm is dicussed in detail.In MBO,four decoding methods are proposed.In order to reduce the standby energy consumption of machine tools,a turning On and Off strategy and a move strategy based on key process are proposed.The leader and the followers evolve through the optimal insertion operation and the optimal exchange operation.Finally,the MBO algorithm is used to solve the instances to verify the effectiveness of the proposed energy-saving strategies and the proposed algorithm.Finally,a multi-objective migrating birds optimization(MOMBO)algorithm is proposed to study the multi-objective HFS problem considering the makespan criteria and the TEC criteria simultaneously.In MOMBO,the leader and the followers evolve through four neighborhood structures.A re-group strategy is used to increase the communication among birds.In order to mine better solutions,a variable neighborhood search algorithm is performed on individuals in the first non-dominated solution set.Finally,the instances are solved by MOMBO and compared with NSGA-II to verify the effectiveness of the proposed algorithm,and the gray correlation analysis method is used to make decisions on the Pareto set.