【摘要】：生產(chǎn)調(diào)度問題作為企業(yè)生產(chǎn)管理和計算機(jī)集成制造系統(tǒng)的核心部分,近年來一直受到廣大學(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,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)帶來顯著的經(jīng)濟(jì)效益。生產(chǎn)調(diào)度問題己被證明屬于NP-hard問題,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)度問題。基于此,近年來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。它可以在較短的時間內(nèi)得到令人滿意的近似最優(yōu)解,已經(jīng)成為了一類能夠有效解決生產(chǎn)調(diào)度問題的新型方法。 本文深入研究了經(jīng)典的和帶阻塞的流水車間調(diào)度問題,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)用到這些問題中。本文的主要研究成果如下： (1)針對帶阻塞流水車間調(diào)度問題(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)用來最小化最大完工時間。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,針對調(diào)度問題對青蛙的跳躍規(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題。基于標(biāo)準(zhǔn)算例的大量仿真測試結(jié)果表明,提出的NMSFLA算法具有明顯的可行性和有效性。 (2)針對流水車間調(diào)度問題(Flowshop Scheduling Problem, FSP)提出了一種極值蛙跳算法(EO-SFLA)用來最小化總流水時間。在EO-SFLA算法中,細(xì)化了分配子種群個體的規(guī)則；對于局部搜索過程,簡化了傳統(tǒng)蛙跳算法的跳躍公式；同時引入了τ-EO算法的思想；最后,引入了新的疊加跳躍公式,認(rèn)為每個個體都會保留他們自己前一時刻的跳躍狀態(tài)�；赥aillard標(biāo)準(zhǔn)算例的仿真實驗表明,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)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TB497

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