【摘要】：在現(xiàn)代制造模式中多品種、小批量生產愈來愈多，對產品成本和質量的要求越來越高，因此對車間運作管理也提出了標準化、精細化的要求，致使管理者愈發(fā)關注生產中存在的不確定性及其對生產的影響。在實際工作中信息的獲得具有不及時和不完整的特點。生產調度需及時了解、充分考慮這些影響因素，在調度方案制定前需防范此因素對生產造成的不平衡隱患，在執(zhí)行過程中調度方案需隨時動態(tài)調整以適應這些變化。在總結以往工作的基礎上，本文研究混裝和作業(yè)車間調度時處理不確定的框架、機制和措施，提出在不確定條件下的魯棒調度方法和動態(tài)自適應反應式策略，并對生產過程中的不確定信息處理和參數(shù)校正方法進行探討。本文的主要工作如下： 以系統(tǒng)性消除不確定因素的影響為目標，構造了結合預防式調度、反應式調度與不確定推理于一體的整體調度框架。以具有不確定吸收能力的魯棒調度方案作為生產開始前的預調度方案，基于調度結果通過貝葉斯推理對預估的不確定參數(shù)分布進行再處理和修正；利用具有不確定反饋能力的反應式調度，應對生產中各種突發(fā)事件，評估并修正應對策略，，為下一階段的預防式和反應式調度提供更可靠的決策依據(jù)。 基于預防式調度思想，研究具有不確定吸收能力的魯棒調度方法。針對具有不確定操作時間的混合裝配線平衡問題，基于混合整型線性規(guī)劃建立相應的魯棒對等模型；針對具有不確定操作時間的作業(yè)車間調度問題，建立基于調度目標期望值的目標規(guī)劃模型并開發(fā)相應的智能算法對模型進行求解。 針對生產過程中出現(xiàn)的設備故障、訂單改變等突發(fā)事件，通過調整系統(tǒng)參數(shù)中的設備和工件等，提出具有自適應能力的反應式調度方法。并針對柔性作業(yè)車間調度問題，開發(fā)具有雙層編碼的遺傳算法。 研究不確定信息的處理及不確定參數(shù)的校正方法。以隨機變量的上界、下界、均值和方差為不確定參數(shù)描述手段，建立基于隨機變量的魯棒解與基于均值的確定解之間的對應關系。以貝葉斯網(wǎng)絡為工具，結合后驗信息與先驗統(tǒng)計進行分布參數(shù)的校正處理，以獲得更符合實際情況的分布參數(shù)。 為降低調度問題的計算復雜性，研究兩種快速算法——針對裝配線平衡問題的摹加代數(shù)方法和針對作業(yè)車間調度的Hopfield-神經網(wǎng)絡算法。對于前者，通過數(shù)學命題證明在摹加代數(shù)意義上，簡單裝配線平衡問題可等價于旅行商問題；對于后者，基于Lyapunov穩(wěn)定性理論證明方法的收斂性。并通過實際算例驗證兩種方法的有效性。
[Abstract]:In the modern manufacturing mode, there are more and more varieties, more and more small batch production, higher and higher cost and quality of the product, so the management of the workshop operation is also put forward the standardization, the refined request. Managers pay more attention to the uncertainty in production and its influence on production. In practical work, the acquisition of information is not timely and incomplete. Production scheduling needs to be understood in time, fully considering these factors, and the hidden danger of imbalance caused by this factor should be prevented before the scheduling plan is formulated. The scheduling scheme should be dynamically adjusted to adapt to these changes in the process of execution. On the basis of summarizing previous work, this paper studies the framework, mechanism and measures for dealing with uncertainty in mixed-loading and job-shop scheduling, and proposes robust scheduling method and dynamic adaptive reactive strategy under uncertain conditions. The methods of uncertain information processing and parameter correction in production process are also discussed. The main work of this paper is as follows: aiming at systematically eliminating the influence of uncertain factors, a whole scheduling framework is constructed, which combines preventive scheduling, reactive scheduling and uncertain reasoning. The robust scheduling scheme with uncertain absorption ability is used as the pre-scheduling scheme before the production start. Based on the scheduling results, the estimated uncertain parameter distribution is re-processed and modified by Bayesian reasoning. The reactive scheduling with uncertain feedback ability is used to deal with all kinds of emergency events in production and to evaluate and revise the response strategies so as to provide more reliable decision-making basis for preventive and reactive scheduling in the next stage. Based on the idea of preventive scheduling, a robust scheduling method with uncertain absorbing ability is studied. To solve the problem of hybrid assembly line balance with uncertain operating time, a robust equivalent model based on hybrid integral linear programming is established. In order to solve the job shop scheduling problem with uncertain operation time, the objective programming model based on scheduling target expectation is established and the corresponding intelligent algorithm is developed to solve the model. Aiming at the emergency events such as equipment failure and order change in the production process, a reactive scheduling method with adaptive ability is proposed by adjusting the equipment and workpieces in the system parameters. In order to solve the flexible job shop scheduling problem, a two-level coded genetic algorithm is developed. The processing of uncertain information and the correction method of uncertain parameters are studied. In this paper, the upper bound, lower bound, mean value and variance of random variables are used to describe the uncertain parameters, and the corresponding relation between the robust solutions based on random variables and the definite solutions based on mean is established. The Bayesian network is used as a tool, and the posterior information and prior statistics are used to correct the distribution parameters in order to obtain the distribution parameters which are more in line with the actual situation. In order to reduce the computational complexity of scheduling problem, two fast algorithms are studied-the modeling algebra method for assembly line balance problem and the Hopfield- neural network algorithm for job shop scheduling. For the former, it is proved by mathematical propositions that the simple assembly line equilibrium problem can be equivalent to the traveling salesman problem in the sense of mimetic algebra, and for the latter, the convergence of the method is proved based on the Lyapunov stability theory. The effectiveness of the two methods is verified by a practical example.
【學位授予單位】：武漢科技大學
【學位級別】：博士
【學位授予年份】：2013
【分類號】：TH186

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