【摘要】：帶等待約束的柔性流水車間調(diào)度問題廣泛存在于現(xiàn)實(shí)的生產(chǎn)環(huán)境中,其調(diào)度方法研究對企業(yè)的生產(chǎn)管理和控制系統(tǒng)有著重要的影響。本文對以最小化總加權(quán)完成時間或最大完工時間為優(yōu)化目標(biāo),對考慮工序間等待時間限制約束的柔性流水車間調(diào)度問題展開研究,探討符合問題特征的求解方法。 針對以最小化總加權(quán)完工時間為目標(biāo)的有限等待FFS問題,建立了整數(shù)規(guī)劃模型,提出了引入懲罰函數(shù)法的混合遺傳算法。懲罰函數(shù)法將迭代種群中每個個體對約束條件違反的次數(shù)進(jìn)行統(tǒng)計(jì),以判斷不同約束的強(qiáng)弱地位,有利于算法在搜索前期盡快達(dá)到可行解區(qū)域,后期尋找最滿意解。通過仿真軟件Matlab開發(fā)調(diào)度程序,仿真結(jié)果表明該算法克服了傳統(tǒng)遺傳方法的缺點(diǎn),不僅具有較強(qiáng)的全局收斂性,而且具有更快的尋優(yōu)速度,是求解柔性流水車間調(diào)度的有效算法。 針對目標(biāo)函數(shù)為最小化最大完工時間的無等待柔性流水車間調(diào)度問題,建立了混合整數(shù)規(guī)劃模型,提出一種遺傳算法來構(gòu)造所求問題的初始解。給出了基于工件的染色體編碼方法使得遺傳算法的操作大大簡化。對兩階段和多階段的零等待FFS調(diào)度問題進(jìn)行仿真實(shí)驗(yàn),其仿真數(shù)據(jù)驗(yàn)證了遺傳算法用于求解大型FFS調(diào)度的可行性和有效性。
[Abstract]:Flexible flow shop scheduling problem with waiting constraints widely exists in the real production environment. The study of scheduling methods has an important impact on the production management and control system of enterprises. In this paper, with the objective of minimizing the total weighted completion time or the maximum completion time, the flexible flow-shop scheduling problem with the constraint of waiting time between processes is studied, and the solution method that accords with the characteristics of the problem is discussed. For the finite wait FFS problem with the goal of minimizing the total weighted completion time, an integer programming model is established, and a hybrid genetic algorithm with penalty function method is proposed. The penalty function method counts the number of times that each individual in the iterative population violates the constraint conditions to judge the position of the different constraints, which is helpful for the algorithm to reach the feasible solution area in the early stage of searching and to find the most satisfactory solution in the later stage. The simulation results show that the algorithm overcomes the shortcomings of the traditional genetic method and not only has a strong global convergence, but also has a faster optimization speed. It is an effective algorithm to solve flexible flow shop scheduling. In this paper, a hybrid integer programming model is established for the job-shop scheduling problem with the minimum maximum completion time and the objective function is to minimize the maximum completion time. A genetic algorithm is proposed to construct the initial solution of the problem. The method of job-based chromosome coding is presented to simplify the operation of genetic algorithm. The simulation results of two-stage and multi-stage zero-wait FFS scheduling problems show that the genetic algorithm is feasible and effective in solving large-scale FFS scheduling problems.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TB497

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