A computational efficient optimization of flow shop scheduling problems

Zhongyuan Liang,Peisi Zhong,Chao Zhang,Zhenyu Zhang,Mei Liu

doi:10.1038/s41598-022-04887-8

Zhongyuan Liang, Peisi Zhong + Show 3 more

Open Access

https://doi.org/10.1038/s41598-022-04887-8

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Abstract

Flow shop scheduling problems are NP-hard problems. Heuristic algorithms and evolutionary metaheuristic algorithms are commonly used to solve this kind of problem. Although heuristic algorithms have high solving speed, the solution quality is not good. Evolutionary algorithms make up for this defect in small-scale problems, but the solution performance will deteriorate with the expansion of the problem scale and there will be premature problems. In order to improve the solving accuracy of flow shop scheduling problems, a computational efficient optimization approach combining NEH and niche genetic algorithm (NEH-NGA) is developed. It is strengthened in the following three aspects: NEH algorithm is used to optimize the initial population, three crossover operators are used to enhance the genetic efficiency, and the niche mechanism is used to control the population distribution. A concrete application scheme of the proposed method is introduced. The results of compared with NEH heuristic algorithm and standard genetic algorithm (SGA) evolutionary metaheuristic algorithm after testing on 101 FSP benchmark instances show that the solution accuracy has been significantly improved.

Highlights

Flow shop scheduling problems are NP-hard problems
In order to improve the solving accuracy of Flow shop scheduling problem (FSP) problems, a computational efficient optimization approach combining NEH and niche genetic algorithm (NEH-NGA) is developed after we studied the solution space distribution of FSPs
Based on the above analysis, we developed a niche genetic algorithm based on NEH to search for the global optimum of FSP, which is called NEH-NGA for short

Summary

Introduction

Flow shop scheduling problems are NP-hard problems. Heuristic algorithms and evolutionary metaheuristic algorithms are commonly used to solve this kind of problem. In order to improve the solving accuracy of flow shop scheduling problems, a computational efficient optimization approach combining NEH and niche genetic algorithm (NEH-NGA) is developed. Due to the limitation of precise methods in such large-scale problems, a large number of heuristic methods have been widely used, e.g., Gupta, Johnson, Palmer, NEH, RA These algorithms generate solution based on problem-specific experience and construction rules, which may not get the optimal operation sequence, but can guarantee the local optimality of the processing sequence to a certain extent. Fernando et al.[7] improved the algorithm based on LR-NEH(x) and the new method provided high-quality solutions with computational efficiency, significantly outperforming the best simple heuristics. Considering cost active adoption of dynamic scheduling and predictive scheduling is an extension of FSP when machine is failure and maintenance during the production, and some achievements have been made in the related literature[21–23] research

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