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A Genetic Programming based Hyper-Heuristic for Production Scheduling in Apparel Industry

机译:基于遗传编程的服装行业生产调度的超启发式

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摘要

The apparel industry is a type of textile industry. One of scheduling problems found in the apparel industry production can be classified as Flow Shop Scheduling Problems (FSSP). GPHH for FSSP is a genetic programming based hyper-heuristic techniques to solve FSSP[1]. The algorithm basically aims to generate new heuristics from two basic (low-level) heuristics, namely Palmer Algorithm and Gupta Algorithm. This paper describes the implementation of the GPHH algorithm and the results of experiments conducted to determine the performance of the proposed algorithm. The experimental results show that the proposed algorithm is promising, has better performance than Palmer Algorithm and Gupta Algorithm.
机译:服装行业是一种纺织业。服装行业生产中发现的调度问题之一可以被归类为流店调度问题(FSSP)。 FSSP的GPHH是一种基于遗传编程的超启发式技术来解决FSSP [1]。该算法基本上旨在从两个基本(低级)启发式,即Palmer算法和GUPTA算法产生新的启发式。本文介绍了GPHH算法的实现和进行的实验结果,以确定所提出的算法的性能。实验结果表明,该算法具有比Palmer算法和GUPTA算法更好的性能。

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