【摘要】：面對生產(chǎn)資源的限制和市場競爭的壓力,MTO生產(chǎn)模式下的制造企業(yè)需要從內(nèi)、外部環(huán)境角度統(tǒng)一考慮市場戰(zhàn)略和生產(chǎn)管理,將企業(yè)的物流倉儲(chǔ)、生產(chǎn)制造與業(yè)務(wù)管理高度集成,使個(gè)性化的產(chǎn)品按時(shí)、按量的交到客戶手中。然而,市場形勢的變化莫測以及生產(chǎn)系統(tǒng)固有的復(fù)雜性,都會(huì)導(dǎo)致實(shí)際生產(chǎn)過程中各種擾動(dòng)因素的產(chǎn)生,一旦某個(gè)環(huán)節(jié)出現(xiàn)錯(cuò)誤,都會(huì)給企業(yè)帶來不必要的損失。因此,設(shè)計(jì)合理的擾動(dòng)檢測方法以及建立穩(wěn)健型調(diào)度問題模型,能夠有效地應(yīng)對各種擾動(dòng)、保持生產(chǎn)系統(tǒng)的穩(wěn)定性、提高企業(yè)的生產(chǎn)效益。重調(diào)度作為穩(wěn)健型調(diào)度的一種,能夠有效地消除擾動(dòng)因素給生產(chǎn)系統(tǒng)帶來的影響,因此,受到當(dāng)今工程界和學(xué)術(shù)界的廣泛關(guān)注。本文在詳細(xì)分析已有重調(diào)度相關(guān)理論的基礎(chǔ)上,圍繞動(dòng)態(tài)不確定環(huán)境下制造企業(yè)擾動(dòng)因素檢測和處理方法,從重調(diào)度因素、重調(diào)度策略以及重調(diào)度方法這三方面,較為全面系統(tǒng)地研究制造企業(yè)穩(wěn)健型調(diào)度問題。首先,本文詳細(xì)描述了生產(chǎn)制造車間常見的擾動(dòng)因素,根據(jù)其對生產(chǎn)系統(tǒng)的影響程度進(jìn)行分類,并采用模糊數(shù)學(xué)理論和概率基神經(jīng)網(wǎng)絡(luò)相結(jié)合的方法,設(shè)計(jì)了一種模糊神經(jīng)網(wǎng)絡(luò)算法,來量化評(píng)估擾動(dòng)因素對生產(chǎn)系統(tǒng)的影響程度。然后,針對現(xiàn)有混合型重調(diào)度策略存在的不足,提出了一種改進(jìn)的混合型重調(diào)度策略,在使用模糊神經(jīng)網(wǎng)絡(luò)對擾動(dòng)因素量化評(píng)估的基礎(chǔ)上,選擇合適的響應(yīng)方式,并通過引入最小重調(diào)度時(shí)間間隔min?T進(jìn)行約束,協(xié)調(diào)了周期性重調(diào)度策略和事件驅(qū)動(dòng)型重調(diào)度策略之間的關(guān)系。其次,針對動(dòng)態(tài)不確定環(huán)境下,生產(chǎn)過程中需要重新生成調(diào)度方案的情況,以目前制造業(yè)廣泛存在的柔性作業(yè)車間為研究對象,構(gòu)建了一種具有自適應(yīng)能力的重調(diào)度模型,并提出一種基于雙層編碼的遺傳算法對模型進(jìn)行求解。最后,總結(jié)歸納了一種制造系統(tǒng)的自適應(yīng)重調(diào)度流程,并在生成調(diào)度方案時(shí),引入智能優(yōu)化算法與人工調(diào)度相結(jié)合的人機(jī)協(xié)同策略,有效地應(yīng)對制造系統(tǒng)中各種常見的擾動(dòng)因素,保證生產(chǎn)的連續(xù)性與均衡性。
[Abstract]:Faced with the limitation of production resources and the pressure of market competition, manufacturing enterprises under MTO production mode need to consider market strategy and production management from the angle of internal and external environment, and integrate the logistics warehousing, manufacturing and business management of enterprises. Make personalized products on time, according to the quantity of the hands of the customer. However, the unpredictable market situation and the inherent complexity of the production system will lead to a variety of disturbance factors in the actual production process. Once a link is wrong, it will bring unnecessary losses to the enterprise. Therefore, the design of reasonable disturbance detection method and the establishment of robust scheduling problem model can effectively deal with all kinds of disturbances, maintain the stability of the production system, and improve the production efficiency of enterprises. As a kind of robust scheduling, rescheduling can effectively eliminate the influence of disturbance factors on production system. Based on the detailed analysis of existing rescheduling theories, this paper focuses on three aspects: detection and processing of disturbance factors in dynamic uncertain environment, rescheduling factors, rescheduling strategies and rescheduling methods. The robust scheduling problem of manufacturing enterprises is studied comprehensively and systematically. First of all, this paper describes the common disturbance factors in manufacturing workshop in detail, classifies them according to their influence on production system, and adopts the method of combining fuzzy mathematics theory with probabilistic neural network. A fuzzy neural network algorithm is designed to quantitatively evaluate the influence of disturbance factors on production system. Then, aiming at the shortcomings of the existing hybrid rescheduling strategy, an improved hybrid rescheduling strategy is proposed. Based on the quantitative evaluation of disturbance factors by using fuzzy neural network, the appropriate response mode is selected. The relationship between periodic rescheduling policy and event-driven rescheduling policy is coordinated by introducing minimum rescheduling interval min?T. Secondly, a rescheduling model with adaptive ability is constructed to solve the problem that scheduling schemes need to be regenerated in the production process under dynamic uncertain environment, and the flexible job shop, which is widely existed in manufacturing industry, is taken as the research object. A genetic algorithm based on double-level coding is proposed to solve the model. Finally, an adaptive rescheduling process of manufacturing system is summarized, and a man-machine coordination strategy combining intelligent optimization algorithm and manual scheduling is introduced in the process of generating scheduling scheme. Effectively deal with the common disturbance factors in manufacturing system to ensure the continuity and balance of production.
【學(xué)位授予單位】：重慶理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：F273;F425

【參考文獻(xiàn)】

相關(guān)博士學(xué)位論文 前1條

1 鞠全勇;智能制造系統(tǒng)生產(chǎn)計(jì)劃與車間調(diào)度的研究[D];南京航空航天大學(xué);2007年

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本文編號(hào)：2232300