發(fā)布時間：2019-05-17 19:59

【摘要】：車間調(diào)度方法與優(yōu)化技術(shù)的研究已經(jīng)成為先進制造技術(shù)的基礎(chǔ)和關(guān)鍵。在制造業(yè)車間,調(diào)度問題的規(guī)模巨大,所涉及的對象復雜。調(diào)度優(yōu)化問題通常是多目標的,而且各目標之間往往存在沖突。此外,實際生產(chǎn)過程中還存在著不確定的擾動因素,比如：機器故障、加工時間改變,緊急插單等。因此,對車間調(diào)度問題進行深入的研究,能夠更好的指導生產(chǎn)。 論文正是在這樣的背景下,結(jié)合實際生產(chǎn)調(diào)度問題所面臨的多目標和動態(tài)性等問題,對柔性作業(yè)車間的多目標調(diào)度問題進行了研究,并取得了一些有意義的研究成果。 論文的主要工作為： (1)對車間調(diào)度問題的研究背景,研究現(xiàn)狀以及研究趨勢進行總結(jié)；對現(xiàn)有的車間調(diào)度算法進行對比分析；闡述了本課題的研究意義和研究目的。 (2)對多目標優(yōu)化算法進行分析,強調(diào)進化算法相對于傳統(tǒng)多目標算法的優(yōu)勢。并基于工件目標的不同,提出了柔性作業(yè)車間的多目標調(diào)度問題的評價指標體系。該體系包含時間、機器負荷、成本、交貨期在內(nèi)的柔性作業(yè)車間的調(diào)度目標,并討論了各目標的計算方法。 (3)根據(jù)實際制造系統(tǒng)中關(guān)注最多的最大完成時間最小和提前／拖期懲罰最小為目標,建立了柔性作業(yè)車間的多目標的調(diào)度模型。另外,論文提出一種包含擾動事件評估、緩沖整合、局部更新、完全重調(diào)度的多級動態(tài)穩(wěn)健調(diào)度策略,彌補了當前對如何減少完全重調(diào)度的次數(shù),保證調(diào)度方案的連續(xù)性和穩(wěn)健性方面存在的缺陷。 (4)對求解柔性作業(yè)車間的多目標調(diào)度問題的遺傳算法進行改進,將免疫算法引入遺傳算法中,利用免疫和熵原理維持種群的多樣性；另外,針對多目標遺傳算法在精英選擇策略方面的不足,引入了分布函數(shù),最后通過實例驗證了算法的可行性。 (5)針對實際制造車間動態(tài)性的特點,提出了一種基于滾動窗口的多目標免疫遺傳算法策略。該策略基于周期和事件驅(qū)動的再調(diào)度機制將調(diào)度過程分成一系列連續(xù)的靜態(tài)調(diào)度區(qū)間,在每個區(qū)間內(nèi)用基于Pareto概念的多目標免疫遺傳算法進行優(yōu)化調(diào)度。并根據(jù)調(diào)度模型目標的設(shè)置,提出了相對應的窗口工件選取原則。 (6)對完全重調(diào)度的穩(wěn)健性進行分析、設(shè)計。根據(jù)柔性作業(yè)車間的特點,設(shè)計了擴展的偏離度指標,該指標充分考慮了工件和機器在保持調(diào)度穩(wěn)健性方面的作用。與多級動態(tài)穩(wěn)健調(diào)度共同保證了調(diào)度方案的連續(xù)性和穩(wěn)健性。
[Abstract]:The research of job shop scheduling method and optimization technology has become the basis and key of advanced manufacturing technology. In manufacturing workshop, the scale of scheduling problem is huge and the object involved is complex. Scheduling optimization problems are usually multi-objective, and there are often conflicts between the objectives. In addition, there are uncertain disturbance factors in the actual production process, such as machine failure, processing time change, emergency list insertion and so on. Therefore, the in-depth study of job shop scheduling problem can better guide production. Under this background, combined with the multi-objective and dynamic problems faced by the actual production scheduling problem, the multi-objective scheduling problem of flexible job shop is studied, and some meaningful research results are obtained. The main work of this paper is as follows: (1) the research background, research status and research trend of job shop scheduling problem are summarized; the existing job shop scheduling algorithms are compared and analyzed; and the research significance and purpose of this topic are expounded. (2) the multi-objective optimization algorithm is analyzed, and the advantages of evolutionary algorithm over the traditional multi-objective algorithm are emphasized. Based on the difference of workpiece objectives, the evaluation index system of multi-objective scheduling problem for flexible job shop is proposed. The system includes the scheduling objectives of flexible job shop, such as time, machine load, cost and delivery time, and discusses the calculation method of each objective. (3) according to the goal of minimum maximum completion time and minimum penalty of advance / delay in the actual manufacturing system, a multi-objective scheduling model of flexible job shop is established. In addition, this paper proposes a multi-level dynamic robust scheduling strategy, which includes disturbance event evaluation, buffer integration, local update and complete rescheduling, which makes up for the current number of times of complete rescheduling. The defects in ensuring the continuity and robustness of the scheduling scheme. (4) the genetic algorithm for solving the multi-objective scheduling problem in flexible job shop is improved. The immune algorithm is introduced into the genetic algorithm, and the immune and entropy principles are used to maintain the diversity of the population. In addition, aiming at the shortcomings of multi-objective genetic algorithm in elite selection strategy, the distribution function is introduced, and an example is given to verify the feasibility of the algorithm. (5) according to the dynamic characteristics of the actual manufacturing workshop, a multi-objective immune genetic algorithm (IGA) strategy based on rolling window is proposed. Based on the periodic and event-driven rescheduling mechanism, the scheduling process is divided into a series of continuous static scheduling intervals, and the multi-objective immune genetic algorithm based on Pareto concept is used to optimize the scheduling in each interval. According to the setting of the goal of the scheduling model, the corresponding principle of window workpiece selection is put forward. (6) the robustness of complete rescheduling is analyzed and designed. According to the characteristics of flexible job shop, an extended deviation index is designed, which fully takes into account the role of workpiece and machine in maintaining scheduling robustness. Together with multi-level dynamic robust scheduling, the continuity and robustness of the scheduling scheme are guaranteed.
【學位授予單位】：山東大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TB497

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