A Hybrid Genetic-Iterative Neighborhood Search Optimization Algorithm for Solving IPPS Problems
In order to solve the integrated process planning and scheduling(IPPS)problem of optimizing the makespan,a hybrid genetic-iterative neighborhood search optimization algorithm is designed and studied.Firstly,considering the operation flexibility,sequencing flexibility and processing flexibility of the IPPS problem,a three-layer chromosome coding method is adopted.At the same time,considering the large feasible solution set,the population initialization method combined with a heuristic rule allocation method is used.Secondly,considering the focus on global optimization of genetic algorithm,iterative neighborhood search is introduced to search locally for the optimal solutions of the genetic algorithm.Besides,the strategy of introducing a new population to compete when the optimal solution remains unchanged after multiple iterations is used to avoid the local optimal trap.At last,by comparing and analyzing the solution results of known cases with existing algorithms,the optimal result obtained by this algorithm is better than most of the excellent algorithms.And using an actual case constructed in a certain station of an aircraft manufacturing company as the background,the effectiveness and the superiority of the proposed algorithm is verified.
integrated process planning and schedulinghybrid genetic-iterative neighborhood searchmakespan
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