【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(jìn)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(duì)象復(fù)雜。調(diào)度優(yōu)化問題通常是多目標(biāo)的,而且各目標(biāo)之間往往存在沖突。此外,實(shí)際生產(chǎn)過程中還存在著不確定的擾動(dòng)因素,比如：機(jī)器故障、加工時(shí)間改變,緊急插單等。因此,對(duì)車間調(diào)度問題進(jìn)行深入的研究,能夠更好的指導(dǎo)生產(chǎn)。 論文正是在這樣的背景下,結(jié)合實(shí)際生產(chǎn)調(diào)度問題所面臨的多目標(biāo)和動(dòng)態(tài)性等問題,對(duì)柔性作業(yè)車間的多目標(biāo)調(diào)度問題進(jìn)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(duì)車間調(diào)度問題的研究背景,研究現(xiàn)狀以及研究趨勢(shì)進(jìn)行總結(jié)；對(duì)現(xiàn)有的車間調(diào)度算法進(jìn)行對(duì)比分析；闡述了本課題的研究意義和研究目的。 (2)對(duì)多目標(biāo)優(yōu)化算法進(jìn)行分析,強(qiáng)調(diào)進(jìn)化算法相對(duì)于傳統(tǒng)多目標(biāo)算法的優(yōu)勢(shì)。并基于工件目標(biāo)的不同,提出了柔性作業(yè)車間的多目標(biāo)調(diào)度問題的評(píng)價(jià)指標(biāo)體系。該體系包含時(shí)間、機(jī)器負(fù)荷、成本、交貨期在內(nèi)的柔性作業(yè)車間的調(diào)度目標(biāo),并討論了各目標(biāo)的計(jì)算方法。 (3)根據(jù)實(shí)際制造系統(tǒng)中關(guān)注最多的最大完成時(shí)間最小和提前／拖期懲罰最小為目標(biāo),建立了柔性作業(yè)車間的多目標(biāo)的調(diào)度模型。另外,論文提出一種包含擾動(dòng)事件評(píng)估、緩沖整合、局部更新、完全重調(diào)度的多級(jí)動(dòng)態(tài)穩(wěn)健調(diào)度策略,彌補(bǔ)了當(dāng)前對(duì)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(duì)求解柔性作業(yè)車間的多目標(biāo)調(diào)度問題的遺傳算法進(jìn)行改進(jìn),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(duì)多目標(biāo)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(shí)例驗(yàn)證了算法的可行性。 (5)針對(duì)實(shí)際制造車間動(dòng)態(tài)性的特點(diǎn),提出了一種基于滾動(dòng)窗口的多目標(biāo)免疫遺傳算法策略。該策略基于周期和事件驅(qū)動(dòng)的再調(diào)度機(jī)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(gè)區(qū)間內(nèi)用基于Pareto概念的多目標(biāo)免疫遺傳算法進(jìn)行優(yōu)化調(diào)度。并根據(jù)調(diào)度模型目標(biāo)的設(shè)置,提出了相對(duì)應(yīng)的窗口工件選取原則。 (6)對(duì)完全重調(diào)度的穩(wěn)健性進(jìn)行分析、設(shè)計(jì)。根據(jù)柔性作業(yè)車間的特點(diǎn),設(shè)計(jì)了擴(kuò)展的偏離度指標(biāo),該指標(biāo)充分考慮了工件和機(jī)器在保持調(diào)度穩(wěn)健性方面的作用。與多級(jí)動(dòng)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(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TB497

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