本文選題：粒子群算法 + 緩沖區(qū)容量分配�。� 參考：《蘭州理工大學(xué)》2017年碩士論文

【摘要】：隨著現(xiàn)代市場(chǎng)的發(fā)展,制造企業(yè)內(nèi)部以及企業(yè)間的相互協(xié)作越來(lái)越頻繁,形成一個(gè)連接緊密的網(wǎng)絡(luò)系統(tǒng),追求系統(tǒng)整體最優(yōu)或者共贏成為企業(yè)最終贏得生存發(fā)展和市場(chǎng)競(jìng)爭(zhēng)的關(guān)鍵。緩沖區(qū)被設(shè)計(jì)臨時(shí)用來(lái)存儲(chǔ)半成品、成品,以降低系統(tǒng)不良狀態(tài)對(duì)其它系統(tǒng)單元乃至整個(gè)系統(tǒng)性能的影響或破壞,關(guān)系到企業(yè)制造系統(tǒng)的整體效能與成敗。就現(xiàn)代市場(chǎng)環(huán)境而言,無(wú)論從供應(yīng)鏈物流角度還是企業(yè)內(nèi)部實(shí)際生產(chǎn)需要看,均不能被完全取消。以往企業(yè)在進(jìn)行緩沖區(qū)容量分配時(shí),由于缺乏成熟理論指導(dǎo)很大程度上依賴于經(jīng)驗(yàn),往往造成生產(chǎn)不平衡甚至經(jīng)營(yíng)失敗的結(jié)果。現(xiàn)代企業(yè)急需從理論基礎(chǔ)上解決在生產(chǎn)要素存在隨機(jī)事件擾動(dòng)情況下,如何從系統(tǒng)整體的角度出發(fā)對(duì)制造系統(tǒng)緩沖區(qū)容量進(jìn)行優(yōu)化分配的問(wèn)題,以便達(dá)到降低成本和整體最優(yōu)的目的。基于此,本文針對(duì)受隨機(jī)故障等隨機(jī)事件影響的流水線系統(tǒng),深入研究生產(chǎn)線在緩沖區(qū)總量固定、生產(chǎn)率最大目標(biāo)條件下的緩沖區(qū)容量?jī)?yōu)化分配問(wèn)題。主要的工作內(nèi)容如下:(1)針對(duì)流水線,對(duì)分解方法和綜合方法等系統(tǒng)性能分析技術(shù)進(jìn)行研究,掌握其思想以及程序算法,通過(guò)數(shù)值仿真實(shí)驗(yàn)了解其優(yōu)缺點(diǎn),為后期解決緩沖區(qū)容量?jī)?yōu)化分配問(wèn)題奠定性能分析技術(shù)基礎(chǔ)。(2)結(jié)合分解方法提出一種改進(jìn)型多種群粒子群算法的緩沖區(qū)容量分配技術(shù),不同于傳統(tǒng)僅靠單一種群搜索的進(jìn)化機(jī)制,該算法將粒子群算法引入多個(gè)種群中,分別對(duì)各種群實(shí)行ω線性遞減策略,并對(duì)各種群附以不同的慣性權(quán)重。在與傳統(tǒng)粒子群算法的比較中說(shuō)明該算法的優(yōu)缺點(diǎn)。(3)以上述研究為基礎(chǔ),針對(duì)上述算法收斂速度慢、運(yùn)算時(shí)間長(zhǎng)的缺點(diǎn),提出了一種高斯混沌變異自適應(yīng)粒子群算法的緩沖區(qū)容量分配技術(shù),該算法利用多樣性測(cè)度函數(shù)作為反饋策略,通過(guò)平均粒距的引入,使慣性權(quán)重隨著種群多樣性的變化自適應(yīng)調(diào)整,達(dá)到較高的收斂速度;并利用高斯混沌變異對(duì)算法進(jìn)行擾動(dòng),使其跳出局部最優(yōu),從而更好地解決流水線緩沖區(qū)容量?jī)?yōu)化分配技術(shù)問(wèn)題,在與上述算法以及傳統(tǒng)算法的比較中驗(yàn)證了該算法的有效性及優(yōu)越性。(4)最后,結(jié)合以上算法,基于MATLAB的GUI圖形用戶界面開發(fā)了“流水線緩沖區(qū)容量分配”軟件工具,為后期結(jié)構(gòu)更復(fù)雜的混雜制造系統(tǒng)的緩沖區(qū)容量分配奠定軟件模塊開發(fā)基礎(chǔ)。
[Abstract]:With the development of modern market, the cooperation between manufacturing enterprises and enterprises is becoming more and more frequent, forming a closely connected network system. The pursuit of overall optimal or win-win system is the key to win survival and development and market competition. Buffer zones are designed to store semi-finished products temporarily to reduce the impact or destruction of the bad state of the system on the performance of other system units and even the whole system, which is related to the overall efficiency and success of the enterprise manufacturing system. As far as the modern market environment is concerned, it can not be completely cancelled either from the point of view of supply chain logistics or from the point of view of the actual production needs of enterprises. In the past, in the process of buffer capacity allocation, the lack of mature theoretical guidance depended on experience to a great extent, which often resulted in unbalanced production and even failure in operation. Modern enterprises urgently need to solve the problem of how to optimize the buffer capacity allocation of manufacturing system from the point of view of the whole system under the condition of random event disturbance of production factors on the basis of theory. In order to achieve the goal of cost reduction and overall optimization. Based on this, this paper studies the optimal allocation of buffer capacity under the condition of fixed total buffer volume and maximum productivity for pipeline systems affected by random events such as random faults. The main contents of the work are as follows: (1) in view of the pipeline, the system performance analysis techniques, such as decomposition method and synthesis method, are studied, their ideas and program algorithms are grasped, and their advantages and disadvantages are understood through numerical simulation experiments. It lays the foundation of performance analysis for solving the problem of optimizing buffer capacity allocation in the later stage. Combining with decomposition method, a new buffer capacity allocation technique based on improved multi-swarm particle swarm optimization algorithm is proposed. Different from the traditional evolutionary mechanism of single population search, particle swarm optimization (PSO) is introduced into multiple populations, and 蠅 linear decrement strategy is applied to each group, and different inertial weights are attached to each group. In comparison with the traditional particle swarm optimization algorithm, the advantages and disadvantages of the algorithm are illustrated. Based on the above research, the algorithm has the disadvantages of slow convergence and long operation time. A buffer capacity allocation technique for Gao Si chaotic mutation adaptive particle swarm optimization algorithm is proposed. The diversity measure function is used as a feedback strategy and the average particle distance is introduced. The inertial weight can be adjusted adaptively with the variety of population diversity to achieve a higher convergence rate, and the algorithm is disturbed by Gao Si chaos mutation to make it jump out of the local optimum. In order to solve the problem of optimal allocation of pipeline buffer capacity better, compared with the above algorithm and traditional algorithm, the effectiveness and superiority of the algorithm are verified. Finally, the algorithm is combined with the above algorithm. The GUI graphical user interface (GUI) based on MATLAB has developed the "pipeline buffer capacity allocation" software tool, which lays the foundation of the software module development for the buffer capacity allocation of the hybrid manufacturing system with more complex structure in the later stage.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18;TH186

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