【摘要】：生產(chǎn)調(diào)度問(wèn)題作為企業(yè)生產(chǎn)管理和計(jì)算機(jī)集成制造系統(tǒng)的核心部分,近年來(lái)一直受到廣大學(xué)者的密切關(guān)注。其主要任務(wù)是分配有限的企業(yè)資源,達(dá)到經(jīng)濟(jì)或性能上的需求目標(biāo)。顯而易見(jiàn),系統(tǒng)、全面、合理、優(yōu)化的生產(chǎn)調(diào)度方案不僅有助于提高企業(yè)的綜合管理水平,而且可以為企業(yè)帶來(lái)顯著的經(jīng)濟(jì)效益。生產(chǎn)調(diào)度問(wèn)題己被證明屬于NP-hard問(wèn)題,因此傳統(tǒng)的優(yōu)化方法己不能有效地求解大規(guī)模復(fù)雜的調(diào)度問(wèn)題�；诖�,近年來(lái)各種不同的人工智能方法逐漸被引入到調(diào)度領(lǐng)域中,取得了很大進(jìn)展。其中隨著計(jì)算機(jī)技術(shù)以及人工智能技術(shù)的迅猛發(fā)展,群智能優(yōu)化算法應(yīng)運(yùn)而生。它可以在較短的時(shí)間內(nèi)得到令人滿(mǎn)意的近似最優(yōu)解,已經(jīng)成為了一類(lèi)能夠有效解決生產(chǎn)調(diào)度問(wèn)題的新型方法。 本文深入研究了經(jīng)典的和帶阻塞的流水車(chē)間調(diào)度問(wèn)題,建立了相應(yīng)的數(shù)學(xué)模型,提出了兩種群智能優(yōu)化算法并成功應(yīng)用到這些問(wèn)題中。本文的主要研究成果如下： (1)針對(duì)帶阻塞流水車(chē)間調(diào)度問(wèn)題(Blocking Flowshop Scheduling Problem, BFSP),提出了一種離散群搜索優(yōu)化算法(New Modified Shuffled Frog Leaping Algorithm, NMSFLA)用來(lái)最小化最大完工時(shí)間。NMSFLA在基本蛙跳算法的局部搜索步驟中引入帶約束的交叉變異思想,針對(duì)調(diào)度問(wèn)題對(duì)青蛙的跳躍規(guī)則做出了改進(jìn),有效地解決了傳統(tǒng)蛙跳算法局部搜索易出現(xiàn)不合法解導(dǎo)致算法效率不高的問(wèn)題�；跇�(biāo)準(zhǔn)算例的大量仿真測(cè)試結(jié)果表明,提出的NMSFLA算法具有明顯的可行性和有效性。 (2)針對(duì)流水車(chē)間調(diào)度問(wèn)題(Flowshop Scheduling Problem, FSP)提出了一種極值蛙跳算法(EO-SFLA)用來(lái)最小化總流水時(shí)間。在EO-SFLA算法中,細(xì)化了分配子種群個(gè)體的規(guī)則；對(duì)于局部搜索過(guò)程,簡(jiǎn)化了傳統(tǒng)蛙跳算法的跳躍公式；同時(shí)引入了τ-EO算法的思想；最后,引入了新的疊加跳躍公式,認(rèn)為每個(gè)個(gè)體都會(huì)保留他們自己前一時(shí)刻的跳躍狀態(tài)�；赥aillard標(biāo)準(zhǔn)算例的仿真實(shí)驗(yàn)表明,提出的EO-SFLA算法具有明顯的優(yōu)越性。
[Abstract]:As the core part of enterprise production management and computer integrated manufacturing system, production scheduling problem has been paid close attention by many scholars in recent years. Its main task is to allocate limited enterprise resources to achieve economic or performance requirements. It is obvious that the systematic, comprehensive, reasonable and optimized production scheduling scheme can not only help to improve the comprehensive management level of the enterprise, but also bring remarkable economic benefits to the enterprise. Production scheduling problem has been proved to be a NP-hard problem, so the traditional optimization method can not effectively solve large-scale complex scheduling problem. Based on this, various artificial intelligence methods have been gradually introduced into the field of scheduling in recent years, and great progress has been made. With the rapid development of computer technology and artificial intelligence technology, swarm intelligence optimization algorithm emerges as the times require. It can obtain a satisfactory approximate optimal solution in a short time. It has become a new method which can effectively solve the production scheduling problem. In this paper, the classical and blocked flow shop scheduling problems are studied in depth, the corresponding mathematical models are established, and a two-species intelligent optimization algorithm is proposed and successfully applied to these problems. The main results of this paper are as follows: (1) A discrete group search optimization algorithm (New Modified Shuffled Frog Leaping Algorithm, NMSFLA) is proposed to minimize the maximum completion time for (Blocking Flowshop Scheduling Problem, BFSP), with blocking flow scheduling problem. The idea of crossover mutation with constraints is introduced into the local search steps of the basic leapfrog algorithm. The jumping rules of frog are improved to solve the problem that the local search of the traditional leapfrog algorithm is easy to produce illegal solution which leads to the low efficiency of the algorithm. A large number of simulation results based on standard examples show that, The proposed NMSFLA algorithm is feasible and effective. (2) an extremum leapfrog algorithm (EO-SFLA) is proposed to minimize the total running time for the flow shop scheduling problem (Flowshop Scheduling Problem, FSP). In the EO-SFLA algorithm, the rules of assigning individual subpopulations are refined; for the local search, the jumping formula of the traditional leapfrog algorithm is simplified; at the same time, the idea of 蟿-EO algorithm is introduced. Finally, a new superposition jump formula is introduced. Think that each individual will retain their own jumping state of the previous moment. The simulation results based on Taillard standard examples show that the proposed EO-SFLA algorithm has obvious advantages.
【學(xué)位授予單位】：華東理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TB497

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 徐震浩,顧幸生;具有零等待的flow shop問(wèn)題的免疫調(diào)度算法[J];化工自動(dòng)化及儀表;2005年01期

2 顧幸生;不確定性條件下的生產(chǎn)調(diào)度[J];華東理工大學(xué)學(xué)報(bào);2000年05期

3 劉琦,顧幸生;基于模糊規(guī)劃的處理時(shí)間不確定條件下的Job shop問(wèn)題[J];華東理工大學(xué)學(xué)報(bào);2001年05期

4 徐曉;徐震浩;顧幸生;王雪;;用改進(jìn)的蛙跳算法求解一類(lèi)模糊Flow Shop調(diào)度問(wèn)題[J];華東理工大學(xué)學(xué)報(bào)(自然科學(xué)版);2010年05期

5 李平,顧幸生;不確定條件下不同交貨期窗口的Job Shop調(diào)度[J];管理科學(xué)學(xué)報(bào);2004年02期

6 黃嵐,王康平,周春光,龐巍,董龍江,彭利;粒子群優(yōu)化算法求解旅行商問(wèn)題[J];吉林大學(xué)學(xué)報(bào)(理學(xué)版);2003年04期

7 李英海;周建中;楊俊杰;劉力;;一種基于閾值選擇策略的改進(jìn)混合蛙跳算法[J];計(jì)算機(jī)工程與應(yīng)用;2007年35期

8 張紀(jì)會(huì)，，徐心和;基于遺傳算法的動(dòng)態(tài)調(diào)度知識(shí)獲取[J];計(jì)算機(jī)集成制造系統(tǒng)-CIMS;1999年03期

9 宋書(shū)強(qiáng);葉春明;;一種新的自適應(yīng)小生境粒子群優(yōu)化算法[J];計(jì)算機(jī)仿真;2010年10期

10 謝圣獻(xiàn);潘全科;潘玉霞;賈保先;;蛙跳算法與批量無(wú)等待流水線調(diào)度問(wèn)題的優(yōu)化[J];計(jì)算機(jī)應(yīng)用研究;2010年08期

本文編號(hào)：2271403