Hybrid Flow Shop Batch Scheduling Problem Solving Based on Improved Genetic Algorithm
To address the complex production planning and scheduling characteristics of the mixed flow production model with multiple variet-ies and small batches,this paper presents an equal batching strategy to solve the batching problem,so as to realize the simultaneous process-ing of workpieces in different processes and reduce the machine waiting time.With the maximum completion time as the optimization objec-tive,this paper establishes a mathematical model of the batch scheduling problem in the mixed flow shop;this paper designs an improved ge-netic algorithm to solve the model,using a combination of the NEH heuristic algorithm and random generation.The improved genetic algorithm is designed to generate high-quality initial solutions using a combination of the NEH heuristic algorithm and random generation,a binary tour-nament for selection operations,a binary crossover for crossover operations,insertion variation for generating new individuals,a greedy inser-tion of the domain search algorithm for local search,and a"sub-batch first+idle first"strategy for decoding.The application results of the en-gine connecting rod production case show that the hybrid flow shop batch scheduling problem model and the improved genetic algorithm are correct and effective.
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维普
