本文關(guān)鍵詞： 柔性流水車間調(diào)度 批處理特征 總加權(quán)完成時(shí)間 自適應(yīng)遺傳算法 自適應(yīng)調(diào)節(jié) 煉鋼-連鑄-熱軋 鋼鐵生產(chǎn)　出處：《鄭州大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：在鋼鐵行業(yè),煉鋼、連鑄、熱軋作為煉鋼的主要工序,生產(chǎn)出的鐵道鋼材、鋼板樁及大中小型鋼等極大地促進(jìn)了國(guó)民經(jīng)濟(jì)的發(fā)展,在整個(gè)流程中起著重要作用。從煉鋼-連鑄-熱軋生產(chǎn)過(guò)程中提煉出的多階段柔性流水車間調(diào)度問(wèn)題(Flexible Flowshop Scheduling Problem,FFSP),不僅需要滿足鋼鐵生產(chǎn)的一系列約束條件,而且具有批處理的特征。帶有批處理特征的多階段FFSP是經(jīng)典FFSP的延伸,要求同一批次內(nèi)所有工件都要按照已知的優(yōu)先級(jí)順序,在同一臺(tái)機(jī)器上進(jìn)行無(wú)間斷地加工。本文結(jié)合實(shí)際情況,對(duì)FFSP進(jìn)行相關(guān)理論分析,并對(duì)國(guó)內(nèi)外相關(guān)領(lǐng)域的研究進(jìn)行學(xué)習(xí)。通過(guò)分析,對(duì)FFSP的應(yīng)用現(xiàn)狀進(jìn)行總結(jié),確定本文的研究問(wèn)題。從鋼鐵生產(chǎn)的煉鋼-連鑄-熱軋工藝中提煉出含有串行批處理特征的多階段FFSP,綜合考慮實(shí)際生產(chǎn)中的各種約束條件,以總加權(quán)完成時(shí)間最小化為目標(biāo)建立數(shù)學(xué)模型。首先對(duì)連鑄-熱軋結(jié)構(gòu)進(jìn)行分析,可以將其看做為第一階段有多臺(tái)串行批處理機(jī)而其它階段為離散機(jī)的FFSP,考慮工件在各加工階段間的運(yùn)輸時(shí)間,利用本文提出的改進(jìn)的自適應(yīng)遺傳算法進(jìn)行優(yōu)化求解。其次將連鑄-熱軋工藝向上游延伸,剖析煉鋼-連鑄-熱軋生產(chǎn)過(guò)程的特點(diǎn),歸納出中間階段有多臺(tái)批處理機(jī),其它階段為離散機(jī)的多階段柔性流水車間調(diào)度問(wèn)題。結(jié)合工件動(dòng)態(tài)到達(dá),各加工階段間的運(yùn)輸時(shí)間以及機(jī)器的調(diào)整時(shí)間等生產(chǎn)特征,對(duì)問(wèn)題進(jìn)行數(shù)學(xué)描述并求解。針對(duì)不同的問(wèn)題,本文分別對(duì)多達(dá)240個(gè)工件和150個(gè)工件的不同規(guī)模的大量隨機(jī)數(shù)據(jù)進(jìn)行仿真測(cè)試。并將拉格朗日松弛算法以及傳統(tǒng)的遺傳算法與本文所提出的改進(jìn)的自適應(yīng)遺傳算法進(jìn)行比較,結(jié)果表明,與常規(guī)遺傳算法相比,所提出的自適應(yīng)遺傳算法能在較短的計(jì)算時(shí)間內(nèi)得到更好的解;與拉格朗日松弛算法對(duì)比,當(dāng)所要求解的問(wèn)題為中大規(guī)模時(shí),所提算法在解的質(zhì)量方面優(yōu)勢(shì)較為明顯。
[Abstract]:In the steel industry, steelmaking, continuous casting, hot rolling as the main process of steelmaking, railway steel production of steel sheet pile and the small and medium-sized steel has greatly promoted the development of the national economy, plays an important role in the whole process. Multi stage flexible flow shop scheduling problem derived from steelmaking continuous casting hot rolling production process (the Flexible Flowshop Scheduling Problem, FFSP), not only need to meet a series of constraints of steel production, but also has the characteristics of batch processing. Multi stage FFSP with batch characteristics is the extension of the classic FFSP requirements within the same batch of all jobs according to the known priority of uninterrupted processing in the same on a single machine. Combining with the actual situation, analyzes the related theories of FFSP, and the domestic and foreign research related fields of study. Through the analysis, the application of FFSP are summarized, indeed Study on the problem in this paper. From the production of iron and steel steelmaking continuous casting hot rolling process to extract containing multi stage FFSP serial batch processing feature, considering various constraints in actual production, to minimize the total weighted completion time to establish the mathematical model for the goal. Firstly, continuous casting and hot rolling structure analysis, can be seen as for the first stage of a serial batching machine and other stage for discrete machine FFSP, considering the workpiece in each processing stage of the transport time, optimize the use of the improved adaptive genetic algorithm is proposed in this paper. Secondly, continuous casting and hot rolling process to extend upstream, analyze the characteristics of steelmaking continuous casting hot rolling production process, summed up the intermediate stage of a plurality of batch processing machines, other stages of multistage flexible flow shop scheduling problem of discrete machine. Combined with dynamic job arrivals, each processing stage between transportation And adjust the time machine production characteristics, mathematical description and solving the problem. According to different problems, this paper respectively up to 240 pieces and 150 workpieces of different sizes in a random data simulation test. And the comparison of improved adaptive genetic algorithm Lagrange relaxation algorithm and traditional genetic algorithm and the the results show that compared with the conventional genetic algorithm, the proposed adaptive genetic algorithm can get a better solution within a short time; compared with the Lagrange relaxation algorithm to solve the problem when in large scale, the proposed algorithm is more obvious in the solution quality advantages.

【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TF758

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本文編號(hào)：1535029