本文關(guān)鍵詞：差異工件的單機(jī)批處理在線優(yōu)化控制方法　出處：《合肥工業(yè)大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 批處理 差異工件 單機(jī) 在線優(yōu)化

【摘要】：在現(xiàn)代化大規(guī)模生產(chǎn)環(huán)境下,批量化工件處理可以有效地提高生產(chǎn)效率,從而滿足現(xiàn)代化生產(chǎn)需求。因此,研究批處理的優(yōu)化控制問(wèn)題,具有重要的理論和現(xiàn)實(shí)意義。該問(wèn)題也是生產(chǎn)優(yōu)化領(lǐng)域的一個(gè)重要研究方向。本文將預(yù)測(cè)控制機(jī)制引入批處理問(wèn)題中,提出一種傳送帶給料分批加工模型。在該模型中,加工主體是具有批量化工件處理能力的設(shè)備,且系統(tǒng)能夠沿著工件到達(dá)的方向向前觀察一段距離,即前視距離,并能通過(guò)傳感器獲知前視距離內(nèi)的工件信息。本文在考慮工件尺寸和加工時(shí)間存在差異的情形下,重點(diǎn)研究單機(jī)模型的在線優(yōu)化控制問(wèn)題。首先,以前視距離為控制變量,無(wú)窮時(shí)段內(nèi)的工件處理率最大為優(yōu)化目標(biāo),建立了系統(tǒng)的優(yōu)化模型。該模型包含兩個(gè)決策優(yōu)化過(guò)程,即工件分批決策和行動(dòng)選擇決策。針對(duì)工件分批決策,本文根據(jù)系統(tǒng)的運(yùn)行模式,提出一種以批處理機(jī)加工周期內(nèi)加工能力浪費(fèi)比最小為準(zhǔn)則的工件分批規(guī)則；對(duì)于行動(dòng)選擇決策,考慮到系統(tǒng)的復(fù)雜性,文中引入模型無(wú)關(guān)的Q學(xué)習(xí)優(yōu)化方法,以求解最優(yōu)前視控制策略。另外,本文還考慮了需求驅(qū)動(dòng)的生產(chǎn)模式,提出一種基于需求驅(qū)動(dòng)的傳送帶給料分批加工模型,研究了其在線優(yōu)化控制問(wèn)題。首先,以無(wú)窮時(shí)段內(nèi)的利潤(rùn)損失率最小為優(yōu)化目標(biāo),建立了系統(tǒng)的實(shí)時(shí)動(dòng)態(tài)分批優(yōu)化模型。然后,給出了一種基于Q學(xué)習(xí)的在線優(yōu)化控制方法。同時(shí),本文進(jìn)行了相應(yīng)的仿真分析,結(jié)果驗(yàn)證了所構(gòu)建模型的合理性以及在線優(yōu)化控制方法的有效性。仿真實(shí)驗(yàn)中還分析了不同分批規(guī)則以及一些物理參數(shù)對(duì)系統(tǒng)性能的影響,為系統(tǒng)的設(shè)計(jì)和優(yōu)化控制提供了參考。
[Abstract]:In the modern large-scale production environment, batch processing can effectively improve the production efficiency and meet the needs of modern production. Therefore, the optimization control problem of batch processing is studied. It has important theoretical and practical significance. This problem is also an important research direction in the field of production optimization. In this paper, predictive control mechanism is introduced into batch processing. In this model, the processing agent is the equipment with the ability of batch processing, and the system can observe a distance along the direction of the work piece. In this paper, considering the difference of workpiece size and processing time, we focus on the on-line optimization control of single machine model. In this paper, the system optimization model is established, in which the distance is regarded as the control variable and the maximum processing rate of the workpiece in infinite period is considered as the optimization objective. The model consists of two decision-making and optimization processes. In this paper, according to the operation mode of the system, a rule of job batching is proposed, which is based on the minimum ratio of processing capacity waste in the machining cycle of batch processing. Considering the complexity of the system, a model-independent Q-learning optimization method is introduced to solve the optimal forward view control strategy. In addition, the demand-driven production mode is also considered in this paper. This paper presents a demand-driven feeding batch model for conveyors, and studies its on-line optimal control problem. Firstly, the minimum profit loss rate in an infinite period of time is taken as the optimization objective. The real-time dynamic batch optimization model of the system is established. Then, an on-line optimization control method based on Q-learning is presented. At the same time, the corresponding simulation analysis is carried out. The results verify the rationality of the proposed model and the effectiveness of the on-line optimization control method. The effects of different batch rules and some physical parameters on the performance of the system are also analyzed in the simulation experiment. It provides a reference for the design and optimization control of the system.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TB497

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