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To Solve the Job Shop Scheduling Problem with the Improve Quantum Genetic Algorithm

机译:用改进的量子遗传算法解决作业车间调度问题

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摘要

Job shop scheduling problem has been a typical scheduling problem that has been thoroughly studied over the last few decades. It has been proven to be a NP-hard problem. The purpose of job scheduling is to assign the work pieces to each machine according to a certain sequence and accomplish the work process with the minimum time. This paper, based on the quantum algorithm theory and quantum chromosome coding knowledge as well as the traditional genetic algorithm, raises an improve quantum genetic algorithm for job shop scheduling. Under the process expression form, it suggests to present the codes as quantum chromosome in order to solve the job shop scheduling problem and make it easy for the information of the elitist to be used to control the variation and make the population to evolve towards the excellent pattern with a large probability and accelerate the convergence rate. The simulation results indicate that the algorithm has better searching and convergence performances.
机译:作业车间调度问题一直是典型的调度问题,在过去的几十年中已经进行了深入研究。已经证明这是一个NP难题。作业计划的目的是按照一定的顺序将工件分配给每台机器,并以最短的时间完成工作过程。本文基于量子算法理论,量子染色体编码知识以及传统遗传算法,提出了一种改进的量子遗传算法用于车间调度。在过程表达形式下,建议将代码显示为量子染色体,以解决车间调度问题,并使精英人士的信息易于控制变异并使种群向优良人群进化。模式的可能性很大,并加快了收敛速度。仿真结果表明,该算法具有较好的搜索和收敛性能。

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