【摘要】：在現(xiàn)代制造模式中多品種、小批量生產(chǎn)愈來(lái)愈多，對(duì)產(chǎn)品成本和質(zhì)量的要求越來(lái)越高，因此對(duì)車(chē)間運(yùn)作管理也提出了標(biāo)準(zhǔn)化、精細(xì)化的要求，致使管理者愈發(fā)關(guān)注生產(chǎn)中存在的不確定性及其對(duì)生產(chǎn)的影響。在實(shí)際工作中信息的獲得具有不及時(shí)和不完整的特點(diǎn)。生產(chǎn)調(diào)度需及時(shí)了解、充分考慮這些影響因素，在調(diào)度方案制定前需防范此因素對(duì)生產(chǎn)造成的不平衡隱患，在執(zhí)行過(guò)程中調(diào)度方案需隨時(shí)動(dòng)態(tài)調(diào)整以適應(yīng)這些變化。在總結(jié)以往工作的基礎(chǔ)上，本文研究混裝和作業(yè)車(chē)間調(diào)度時(shí)處理不確定的框架、機(jī)制和措施，提出在不確定條件下的魯棒調(diào)度方法和動(dòng)態(tài)自適應(yīng)反應(yīng)式策略，并對(duì)生產(chǎn)過(guò)程中的不確定信息處理和參數(shù)校正方法進(jìn)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(biāo)，構(gòu)造了結(jié)合預(yù)防式調(diào)度、反應(yīng)式調(diào)度與不確定推理于一體的整體調(diào)度框架。以具有不確定吸收能力的魯棒調(diào)度方案作為生產(chǎn)開(kāi)始前的預(yù)調(diào)度方案，基于調(diào)度結(jié)果通過(guò)貝葉斯推理對(duì)預(yù)估的不確定參數(shù)分布進(jìn)行再處理和修正；利用具有不確定反饋能力的反應(yīng)式調(diào)度，應(yīng)對(duì)生產(chǎn)中各種突發(fā)事件，評(píng)估并修正應(yīng)對(duì)策略，，為下一階段的預(yù)防式和反應(yīng)式調(diào)度提供更可靠的決策依據(jù)。 基于預(yù)防式調(diào)度思想，研究具有不確定吸收能力的魯棒調(diào)度方法。針對(duì)具有不確定操作時(shí)間的混合裝配線平衡問(wèn)題，基于混合整型線性規(guī)劃建立相應(yīng)的魯棒對(duì)等模型；針對(duì)具有不確定操作時(shí)間的作業(yè)車(chē)間調(diào)度問(wèn)題，建立基于調(diào)度目標(biāo)期望值的目標(biāo)規(guī)劃模型并開(kāi)發(fā)相應(yīng)的智能算法對(duì)模型進(jìn)行求解。 針對(duì)生產(chǎn)過(guò)程中出現(xiàn)的設(shè)備故障、訂單改變等突發(fā)事件，通過(guò)調(diào)整系統(tǒng)參數(shù)中的設(shè)備和工件等，提出具有自適應(yīng)能力的反應(yīng)式調(diào)度方法。并針對(duì)柔性作業(yè)車(chē)間調(diào)度問(wèn)題，開(kāi)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(jī)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(jī)變量的魯棒解與基于均值的確定解之間的對(duì)應(yīng)關(guān)系。以貝葉斯網(wǎng)絡(luò)為工具，結(jié)合后驗(yàn)信息與先驗(yàn)統(tǒng)計(jì)進(jìn)行分布參數(shù)的校正處理，以獲得更符合實(shí)際情況的分布參數(shù)。 為降低調(diào)度問(wèn)題的計(jì)算復(fù)雜性，研究?jī)煞N快速算法——針對(duì)裝配線平衡問(wèn)題的摹加代數(shù)方法和針對(duì)作業(yè)車(chē)間調(diào)度的Hopfield-神經(jīng)網(wǎng)絡(luò)算法。對(duì)于前者，通過(guò)數(shù)學(xué)命題證明在摹加代數(shù)意義上，簡(jiǎn)單裝配線平衡問(wèn)題可等價(jià)于旅行商問(wèn)題；對(duì)于后者，基于Lyapunov穩(wěn)定性理論證明方法的收斂性。并通過(guò)實(shí)際算例驗(yàn)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(xué)位授予單位】：武漢科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：TH186

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