發(fā)布時間：2018-11-20 09:00

【摘要】：傳統(tǒng)作業(yè)車間調(diào)度問題的拓展是多目標(biāo)柔性車間調(diào)度,多目標(biāo)柔性車間調(diào)度更符合現(xiàn)在車間的實際生產(chǎn)情況,對該問題的研究具有現(xiàn)實意義。本文依托寧夏某儀表制造有限公司為背景,該企業(yè)是一家離散型制造閥門的企業(yè),實現(xiàn)的是多品種、少批量、多批次的符合現(xiàn)代市場動態(tài)的生產(chǎn)方式,企業(yè)生產(chǎn)通常受到多個方面的因素的限制。在滿足客戶需求的情況下我們從企業(yè)生產(chǎn)實際出發(fā),抽取出企業(yè)需滿足的三個目標(biāo)函數(shù),分別是企業(yè)最大利益下的最小機(jī)器負(fù)載、最短加工時間和最小成本3個目標(biāo)函數(shù)。如果希望3個目標(biāo)達(dá)到預(yù)期的值使企業(yè)盈利,那么就需要一個合理的車間調(diào)度模型和有效地生產(chǎn)調(diào)度算法。本文在綜合分析國內(nèi)外關(guān)于車間調(diào)度問題的基礎(chǔ)上,考慮本研究的柔性作業(yè)車間運(yùn)作的實際情況,對多目標(biāo)作業(yè)車間調(diào)度問題進(jìn)行了一個系統(tǒng)的研究。本篇論文所做的主要工作有:(1)從現(xiàn)有車間調(diào)度模型的不足之處出發(fā),在本文中給出了基于分層的面向?qū)ο蟮挠猩玃etri網(wǎng)的建模方法;以往的Petri網(wǎng)模型會引起空間爆炸,沒有模塊性和缺乏可重用性,本文中Petri網(wǎng)建模通過分層的思想和面向?qū)ο蟮募夹g(shù)可以克服這些缺點(diǎn)。(2)針對該寧夏某企業(yè)存在的車間調(diào)度問題,給出了蟻群粒子群混合車間調(diào)度算法。因為粒子群算法的特點(diǎn)是迭代速度非�？�,而且容易在最優(yōu)解附近震蕩;而蟻群算法的特點(diǎn)是初始信息素匱乏;利用蟻群粒子群算法優(yōu)勢互補(bǔ)的思想進(jìn)行求解車間調(diào)度。首先介紹了算法的編碼解碼,目標(biāo)的歸一化,然后給出了兩種算法求解多目標(biāo)車間調(diào)度的流程圖,最后對流程圖進(jìn)行了詳細(xì)的介紹。(3)將蟻群粒子群兩種算法結(jié)合求解實際生產(chǎn)中的多目標(biāo)柔性車間調(diào)度算例。通過對粒子群算法和兩種混合算法的實驗結(jié)果中的非劣解和甘特圖進(jìn)行分析對比,發(fā)現(xiàn)混合算法更有效。
[Abstract]:The extension of the traditional job shop scheduling problem is multi-objective flexible job shop scheduling, and the multi-objective flexible job shop scheduling is more in line with the actual production situation of the present job shop, so it is of practical significance to study this problem. Based on the background of Ningxia instrument Manufacturing Co., Ltd., this enterprise is a discrete manufacturing valve enterprise, which realizes the production mode of multi-variety, less batch, multi-batch, in line with the modern market dynamics. Enterprise production is usually limited by multiple factors. In the case of satisfying the customer's demand, we extract three objective functions, which are the minimum machine load, the shortest processing time and the minimum cost, which the enterprise needs to satisfy. If the three goals are expected to make the enterprise profitable, a reasonable job shop scheduling model and an effective production scheduling algorithm are needed. Based on the comprehensive analysis of job shop scheduling problems at home and abroad and considering the actual situation of flexible job shop operation in this paper, a systematic study of multi-objective job shop scheduling problem is carried out. The main work of this thesis is as follows: (1) based on the shortcomings of the existing job shop scheduling model, the modeling method of colored Petri nets based on hierarchical object-oriented is presented; Previous Petri net models can cause space explosion, no modularity and lack of reusability. In this paper, Petri net modeling can overcome these shortcomings through hierarchical thinking and object-oriented technology. (2) aiming at the workshop scheduling problem of a certain enterprise in Ningxia, an ant colony particle swarm hybrid job-shop scheduling algorithm is presented. Particle swarm optimization (PSO) algorithm is characterized by fast iteration speed and easy to concussion near the optimal solution; ant colony algorithm is characterized by the lack of initial pheromone; the ant colony PSO algorithm is used to solve job shop scheduling using the idea of complementary advantages of ant colony Particle Swarm Optimization (APSO) algorithm. Firstly, the coding and decoding of the algorithm and the normalization of the target are introduced, and then the flow chart of the two algorithms to solve the multi-objective job shop scheduling is given. Finally, the flow chart is introduced in detail. (3) the ant colony particle swarm optimization algorithm is combined to solve the multi-objective flexible job shop scheduling example. By analyzing and comparing the non-inferior solution and Gantt diagram in the experimental results of particle swarm optimization and two hybrid algorithms, it is found that the hybrid algorithm is more effective.
【學(xué)位授予單位】：寧夏大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18;TB497

【參考文獻(xiàn)】

中國期刊全文數(shù)據(jù)庫 前10條

1 劉江;;基于CPN Tools研究綜述[J];信息技術(shù)與信息化;2015年03期

2 屈新懷;劉棟;丁必榮;;柔性作業(yè)車間分批調(diào)度的多樣性可控粒子群優(yōu)化算法[J];計算機(jī)輔助設(shè)計與圖形學(xué)學(xué)報;2014年01期

3 王玲;王新;劉健;王書茂;;基于虛擬儀器的柔性化農(nóng)機(jī)機(jī)群遠(yuǎn)程監(jiān)測系統(tǒng)研究[J];農(nóng)業(yè)機(jī)械學(xué)報;2014年01期

4 楊尚君;王社偉;陶軍;劉學(xué);;基于混合細(xì)菌覓食算法的多目標(biāo)優(yōu)化方法[J];計算機(jī)仿真;2012年06期

5 張靜;王萬良;徐新黎;介婧;;混合粒子群算法求解多目標(biāo)柔性作業(yè)車間調(diào)調(diào)度度問題[J];控制理論與應(yīng)用;2012年06期

6 陶澤;李小軍;劉曉霞;;基于Petri網(wǎng)和GA的多目標(biāo)動態(tài)優(yōu)化調(diào)度問題研究[J];組合機(jī)床與自動化加工技術(shù);2011年10期

7 王云;馮毅雄;譚建榮;李中凱;;基于多目標(biāo)粒子群算法的柔性作業(yè)車間調(diào)度優(yōu)化方法[J];農(nóng)業(yè)機(jī)械學(xué)報;2011年02期

8 谷峰;陳華平;盧冰原;;基于遺傳算法的多目標(biāo)柔性工作車間調(diào)度問題求解[J];運(yùn)籌與管理;2006年01期

9 劉舟,朱齊丹,朱偉,安曉東;面向?qū)ο驪etri網(wǎng)在艦炮武器系統(tǒng)建模中的研究[J];系統(tǒng)仿真學(xué)報;2005年06期

10 夏蔚軍,吳智銘;基于混合微粒群優(yōu)化的多目標(biāo)柔性Job-shop調(diào)度[J];控制與決策;2005年02期

中國碩士學(xué)位論文全文數(shù)據(jù)庫 前1條

1 單修慧;基于高級Petri網(wǎng)的柔性工作流模型映射[D];中國石油大學(xué);2011年

，

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