發(fā)布時間：2018-06-29 21:53

  本文選題：動態(tài)環(huán)境 + 調(diào)度規(guī)則�。� 參考：《合肥工業(yè)大學(xué)》2017年碩士論文

【摘要】：在現(xiàn)代制造模式下,靜態(tài)的調(diào)度方案已經(jīng)無法適應(yīng)于多變的作業(yè)車間生產(chǎn)環(huán)境,基于知識推理的調(diào)度方法是解決該類問題的有效方式之一。當(dāng)前調(diào)度知識系統(tǒng)多建立領(lǐng)域內(nèi)專家經(jīng)驗基礎(chǔ)之上,多存在主觀性強(qiáng)、決策依賴部分屬性、多源知識沖突和知識滯后等問題。本文針對動態(tài)車間環(huán)境下的調(diào)度知識推理研究現(xiàn)狀,重點研究調(diào)度規(guī)則的動態(tài)選擇的問題,利用企業(yè)制造系統(tǒng)中的生產(chǎn)調(diào)度數(shù)據(jù),運(yùn)用遺傳算法和BP人工神經(jīng)網(wǎng)絡(luò)算法構(gòu)建動態(tài)調(diào)度知識網(wǎng)絡(luò),設(shè)計基于動態(tài)知識網(wǎng)絡(luò)的調(diào)度知識系統(tǒng)。首先,建立靜態(tài)作業(yè)車間數(shù)學(xué)模型;針對一般作業(yè)車間靜態(tài)調(diào)度問題,通過編碼解碼、交叉、變異等遺傳算法操作,獲得問題最優(yōu)解;歸納一般靜態(tài)車間調(diào)度問題求解的遺傳算法流程。其次,分析常見的作業(yè)調(diào)度規(guī)則和幾種復(fù)合規(guī)則調(diào)度方式,確定本文研究方向為自適應(yīng)調(diào)度;利用遺傳算法求解改進(jìn)的4×3調(diào)度問題的最優(yōu)解,基于BP人工神經(jīng)網(wǎng)絡(luò)算法建模,定義網(wǎng)絡(luò)輸入?yún)?shù)和輸出參數(shù),從遺傳算法最優(yōu)解中抽取沖突時間決策點,計算人工神經(jīng)網(wǎng)絡(luò)輸入和輸出,獲得訓(xùn)練樣本;訓(xùn)練樣本數(shù)據(jù)獲得非線性網(wǎng)絡(luò)關(guān)系,指導(dǎo)不確定車間條件下調(diào)度規(guī)則的選擇。最后,分析調(diào)度知識系統(tǒng)實現(xiàn)的關(guān)鍵策略,進(jìn)行調(diào)度知識系統(tǒng)的系統(tǒng)需求分析,歸納總結(jié)系統(tǒng)的業(yè)務(wù)流程;提出調(diào)度知識系統(tǒng)的硬件框架和軟件框架,實現(xiàn)系統(tǒng)的關(guān)鍵數(shù)據(jù)庫設(shè)計和軟件模塊設(shè)計。
[Abstract]:In the modern manufacturing mode, the static scheduling scheme can no longer adapt to the changeable job shop production environment. The scheduling method based on knowledge reasoning is one of the effective ways to solve this kind of problem. At present, most scheduling knowledge systems are based on the experience of experts in the field, and there are many problems, such as strong subjectivity, partial attribute of decision dependence, multi-source knowledge conflict and knowledge lag, etc. Aiming at the present situation of scheduling knowledge reasoning in dynamic workshop environment, this paper focuses on the dynamic selection of scheduling rules, and makes use of the production scheduling data in enterprise manufacturing systems. Genetic algorithm and BP artificial neural network algorithm are used to construct dynamic scheduling knowledge network, and a scheduling knowledge system based on dynamic knowledge network is designed. Firstly, the mathematical model of static job shop is established, and the optimal solution of the problem is obtained by genetic algorithm, such as coding and decoding, crossover, mutation and so on. The genetic algorithm flow of general static job shop scheduling problem is summarized. Secondly, by analyzing the common job scheduling rules and several complex rule scheduling methods, the research direction of this paper is determined as adaptive scheduling, the genetic algorithm is used to solve the optimal solution of the improved 4 脳 3 scheduling problem, and the BP artificial neural network algorithm is used to model the model. The input and output parameters of the network are defined, the conflict time decision points are extracted from the optimal solution of genetic algorithm, the input and output of artificial neural network are calculated, the training sample is obtained, and the nonlinear network relation is obtained from the training sample data. To guide the selection of scheduling rules under uncertain job shop conditions. Finally, the key strategies of scheduling knowledge system are analyzed, the system requirements of scheduling knowledge system are analyzed, the business process of the system is summarized, and the hardware and software framework of scheduling knowledge system is put forward. The key database design and software module design of the system are realized.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18;TB497

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本文編號：2083485