本文選題：學(xué)習(xí)效應(yīng)　切入點(diǎn)：流水車間調(diào)度　出處：《系統(tǒng)管理學(xué)報(bào)》2017年06期

【摘要】：考慮了學(xué)習(xí)效應(yīng)對(duì)流水車間調(diào)度問(wèn)題的影響,以最大完工時(shí)間和總加權(quán)滯后時(shí)間為優(yōu)化目標(biāo),建立了與加工順序相關(guān)的對(duì)數(shù)線性調(diào)度模型,設(shè)計(jì)了LNEH(NEH heuristic with Learning effect)啟發(fā)式算法和MCF(Membrane Computing for multi-objective Flow-shop scheduling)算法進(jìn)行求解。LNEH算法根據(jù)對(duì)學(xué)習(xí)效應(yīng)下問(wèn)題性質(zhì)的分析,在初始序列和工件插入兩個(gè)環(huán)節(jié)進(jìn)而達(dá)到對(duì)原有NEH算法的改進(jìn),同時(shí)采用隨機(jī)策略以獲得多個(gè)解。MCF算法是采用膜計(jì)算理論設(shè)計(jì)的一種近似調(diào)度優(yōu)化算法,針對(duì)調(diào)度問(wèn)題設(shè)計(jì)了字符對(duì)象的編碼方式,根據(jù)前端等級(jí)大小將字符對(duì)象往復(fù)地分配為均勻的對(duì)象集,借鑒PSO算法制訂膜內(nèi)規(guī)則:從外部檔案和所在的基本膜內(nèi)中分別選擇作為參考的選擇規(guī)則和類似于PSO算法的移動(dòng)規(guī)則。數(shù)值仿真顯示,不同學(xué)習(xí)系數(shù)對(duì)調(diào)度結(jié)果具有較大影響,并對(duì)比證明了所提兩種算法的有效性。
[Abstract]:Considering the influence of learning effect on income job shop scheduling problem, a logarithmic linear scheduling model related to machining sequence is established with the maximum completion time and total weighted delay time as the optimization objectives.In this paper, LNEH(NEH heuristic with Learning effect-) heuristic algorithm and MCF(Membrane Computing for multi-objective Flow-shop scheduling algorithm are designed to solve .LNEH algorithm. According to the analysis of the nature of the problem under the learning effect, two links are inserted in the initial sequence and the workpiece to improve the original NEH algorithm.At the same time, using random strategy to obtain multiple solutions. MCF algorithm is an approximate scheduling optimization algorithm based on membrane computing theory. The encoding method of character object is designed for scheduling problem.The character objects are allocated back and forth as a uniform set of objects based on the size of the front end level,Drawing on the PSO algorithm to formulate the intramembrane rules: select the selection rules for reference from the external files and the basic film and the moving rules similar to the PSO algorithm respectively.Numerical simulation shows that different learning coefficients have great influence on the scheduling results, and the effectiveness of the two algorithms is proved by comparison.
【作者單位】： 上海工程技術(shù)大學(xué)管理學(xué)院;上海交通大學(xué)安泰經(jīng)濟(jì)與管理學(xué)院;伯明翰大學(xué)商學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(71371123,71502101,71632008,1271138) 上海市哲學(xué)社會(huì)科學(xué)規(guī)劃課題(2017EGL009) 上海工程技術(shù)大學(xué)科研啟動(dòng)項(xiàng)目(校啟2017-27) Europe-China High Value Engineering Network(EC-HVEN:295130)
【分類號(hào)】：TB497

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