本文選題：生產(chǎn)調(diào)度 + 流水車(chē)間��； 參考：《華東理工大學(xué)》2014年博士論文

【摘要】：生產(chǎn)調(diào)度問(wèn)題存在于大量實(shí)際制造業(yè)與服務(wù)業(yè)中,如石化業(yè)、煙草業(yè)、紡織業(yè)、造紙業(yè)、制藥業(yè)以及食品業(yè)等,并在其中發(fā)揮著非常重要的作用。簡(jiǎn)單來(lái)說(shuō),生產(chǎn)調(diào)度問(wèn)題就是如何在給定的時(shí)間約束內(nèi)合理地安排分配有限的資源,使得一個(gè)或多個(gè)目標(biāo)達(dá)到最優(yōu)。從本質(zhì)上來(lái)說(shuō)它是一個(gè)決策過(guò)程。同時(shí),生產(chǎn)調(diào)度問(wèn)題也是一類(lèi)非常典型的組合優(yōu)化問(wèn)題,當(dāng)中很多類(lèi)型的子問(wèn)題都是NP-hard問(wèn)題。使用傳統(tǒng)的方法進(jìn)行求解很難得到令人滿(mǎn)意的結(jié)果,特別是對(duì)一些極為復(fù)雜的問(wèn)題,甚至根本得不到有效的解。因此,無(wú)論在實(shí)際工業(yè)生產(chǎn)方面,還是在理論學(xué)術(shù)研究方面,對(duì)生產(chǎn)調(diào)度問(wèn)題的研究都有著非常重要的意義。本文深入研究了四種典型的流水車(chē)間調(diào)度問(wèn)題：帶阻塞流水車(chē)間調(diào)度問(wèn)題、中間存儲(chǔ)有限流水車(chē)間調(diào)度問(wèn)題、混合流水車(chē)間調(diào)度問(wèn)題和機(jī)器故障情況下混合流水車(chē)間調(diào)度問(wèn)題,建立了相應(yīng)的數(shù)學(xué)模型,提出了幾種群智能優(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(Discrete Group Search Optimizer, DGSO)用來(lái)最小化它的總流水時(shí)間。在DGSO算法中,種群初始化階段使用了隨機(jī)初始化與兩種啟發(fā)式算法(NEH和NEH-WPT)相結(jié)合的方法,保證了初始種群既具有一定的質(zhì)量,又兼?zhèn)涠鄻有�；接著將基于插入操作的鄰域搜索、離散差分進(jìn)化策略以及破壞重建過(guò)程嵌入到算法中,提高了算法的性能；最后使用了一種正交實(shí)驗(yàn)設(shè)計(jì)的方法選取了合適的算法參數(shù)值�；跇�(biāo)準(zhǔn)算例的大量仿真測(cè)試結(jié)果表明,提出的DGSO算法具有明顯的可行性和有效性。 (2)針對(duì)中間存儲(chǔ)有限流水車(chē)間調(diào)度問(wèn)題(Flowshop Scheduling Problem with Limited Buffers, LBFSP),提出了一種混合離散和聲搜索算法(Hybrid Discrete Harmony Search, HDHS)對(duì)其進(jìn)行求解。此算法基于工件排列的編碼方式,設(shè)計(jì)了一種構(gòu)造離散和聲的新方法以及離散差分進(jìn)化策略；同時(shí)將此離散和聲搜索策略與基于插入操作的局部搜索操作相結(jié)合,很好地平衡了算法的全局搜索能力與局部搜索能力;并使用正交實(shí)驗(yàn)的方法確定HDHS算法的參數(shù)值�；赥aillard標(biāo)準(zhǔn)算例的仿真實(shí)驗(yàn)表明提出的HDHS算法具有明顯的優(yōu)越性。 (3)針對(duì)混合流水車(chē)間調(diào)度問(wèn)題(Hybrid Flowshop Scheduling, HFS),使用向量表述的方式進(jìn)行數(shù)學(xué)建模,并提出了一種改進(jìn)離散人工蜂群算法(Improved Discrete Artificial Bee Colony, IDABC)來(lái)最小化其最大完工時(shí)間makespan。IDABC算法在引領(lǐng)蜂和跟隨蜂階段分別使用了一種全新設(shè)計(jì)的差分進(jìn)化策略和改進(jìn)變鄰域搜索策略,實(shí)現(xiàn)了個(gè)體的更新；在偵察蜂階段使用破壞重建操作提高了算法的全局搜索能力。此外也使用了正交設(shè)計(jì)的方法,僅僅通過(guò)少量次數(shù)的實(shí)驗(yàn)就獲得了很好的算法參數(shù)值。大量的仿真實(shí)驗(yàn)表明,在求解相同的標(biāo)準(zhǔn)算例時(shí),提出的IDABC算法的求解效果明顯優(yōu)于參與比較的當(dāng)前其他幾種高性能算法。 (4)在以往對(duì)生產(chǎn)調(diào)度問(wèn)題的研究中,常常假設(shè)所有的機(jī)器一直可用,不會(huì)出現(xiàn)故障。然而在實(shí)際生產(chǎn)中,加工機(jī)器由于各種原因不可避免地會(huì)發(fā)生故障。針對(duì)機(jī)器發(fā)生故障情況下的混合流水車(chē)間調(diào)度問(wèn)題(Hybrid Flowshop Scheduling with Random Breakdown, RBHFS),分析了機(jī)器發(fā)生故障后的兩種加工情況：preempt-resume情況和preempt-repeat'隋況,并提出了一種改進(jìn)離散群搜索優(yōu)化算法(Improved Discrete Group Search Optimizer, IDGSO)來(lái)求解。IDGSO算法采用向量表述方式來(lái)描述問(wèn)題,并使用一些離散操作進(jìn)行迭代進(jìn)化,包括分布在發(fā)現(xiàn)者、追隨者和游蕩者階段中的插入操作、交換操作、差分進(jìn)化操作以及破壞重建操作等。仿真計(jì)算結(jié)果表明,在preempt-resume和preempt-repeat兩種情況下,提出的IDGSO算法比其他高性能算法具有更好的效果。
[Abstract]:Production scheduling problems exist in a large number of actual manufacturing and service industries, such as petrochemical industry, tobacco industry, textile industry, paper industry, pharmaceutical industry and food industry, and play a very important role in it. In simple terms, the problem of scheduling is how to allocate limited resources in a given time constraint, so that a Or multiple objectives are optimal. In essence, it is a decision-making process. At the same time, production scheduling problem is also a kind of very typical combinatorial optimization problem. Many of the types of sub problems are NP-hard problems. It is difficult to obtain satisfactory results by using traditional methods, especially for some extremely complex problems, The research on production scheduling problem is very important in both practical industrial production and theoretical academic research. Four typical flow shop scheduling problems are studied in this paper: the problem of traffic shop scheduling with blocking and the limited flow shop in the middle of this paper. Scheduling problem, mixed flow shop scheduling problem and hybrid flow shop scheduling problem under machine fault conditions, a corresponding mathematical model is established, and several swarm intelligence optimization algorithms are proposed and applied to these problems successfully. The main research results of this paper are as follows:
(1) a discrete group search optimization algorithm (Discrete Group Search Optimizer, DGSO) is proposed to minimize its total flow time for the Blocking Flowshop Scheduling Problem (BFSP). In the DGSO algorithm, the initialization phase of the population uses random initialization and two heuristic algorithms (NEH and heuristics). EH-WPT) the combination method ensures the quality and diversity of the initial population. Then, the neighborhood search, the discrete differential evolution strategy and the failure reconstruction process are embedded into the algorithm to improve the performance of the algorithm. Finally, an orthogonal experimental design method is used to select the appropriate calculation. A large number of simulation tests based on standard examples show that the proposed DGSO algorithm is feasible and effective.
(2) a new hybrid discrete harmonic search algorithm (Hybrid Discrete Harmony Search, HDHS) is proposed to solve the problem of Flowshop Scheduling Problem with Limited Buffers (LBFSP) in the middle storage. A new method for constructing discrete harmony is designed based on the coding method of the work-piece scheduling. As well as the discrete differential evolution strategy, combining the discrete harmony search strategy with the local search operation based on the insertion operation, the global search capability and the local search ability of the algorithm are well balanced. The parameter values of the HDHS algorithm are determined by the orthogonal experiment. The simulation experiments based on the Taillard standard example show that the algorithm is proposed. The HDHS algorithm has obvious superiority.
(3) for the mixed flow shop scheduling problem (Hybrid Flowshop Scheduling, HFS), a mathematical model is used by vector expression, and an improved discrete artificial bee colony algorithm (Improved Discrete Artificial Bee Colony, IDABC) is proposed to minimize the maximum completion time makespan.IDABC algorithm in the leading and following wasps stage. A new design of differential evolution strategy and an improved variable neighborhood search strategy are used respectively to update the individual and improve the global search ability of the algorithm in the reconnaissance bee stage. In addition, the orthogonal design method is used to obtain good algorithm parameters by only a small number of experiments. A large number of simulation experiments show that the proposed IDABC algorithm is better than the other high performance algorithms that participate in the comparison when solving the same standard example.
(4) in the previous research on production scheduling problems, it is often assumed that all machines are always available and will not fail. However, in actual production, processing machines will inevitably fail for a variety of reasons. Hybrid Flowshop Scheduling with Random Br Eakdown, RBHFS), analysis of the two processing conditions after the failure of the machine: the preempt-resume situation and the preempt-repeat'Sui state, and proposed an improved discrete group search optimization algorithm (Improved Discrete Group Search Optimizer, IDGSO) to solve the.IDGSO algorithm using vector expression to describe the problem, and use some discrete. The operation is iterative evolution, including the insertion operation in the discoverer, the followers and the wanderer stage, the switching operation, the differential evolution operation and the destruction reconstruction operation. The simulation results show that the proposed IDGSO algorithm has better effect than the other high performance algorithms in the two cases of preempt-resume and preempt-repeat.

【學(xué)位授予單位】：華東理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TB497;TP18

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