本文選題：機器人制造單元 + FJSP�。� 參考：《廣東工業(yè)大學》2017年碩士論文

【摘要】：隨著市場競爭的加劇和先進制造技術不斷發(fā)展,機器人制造單元被越來越廣泛地應用于生產制造行業(yè),生產調度方案作為機器人制造單元安全高效運轉的基礎,近年來逐漸成為學者研究的重點。為了進一步減低制造成本,均衡利用企業(yè)資源和適應小批量、多品種的生產模式,往往需要考慮工序在不同加工加床之間的搬運工序,對多個可能存在相互沖突的目標進行優(yōu)化。因此,本文在經典車間調度理論基礎上,對兩種具有重要應用意義的機器人制造單元調度問題進行研究。重點研究問題的調度機制和求解算法。本文首先對柔性多機器人制造單元單目標調度問題進行研究,提出一種求解問題的混合蟻群算法。首先建立問題析取圖模型,接著對傳統(tǒng)蟻群算法的狀態(tài)轉移規(guī)則和信息素更新規(guī)則進行優(yōu)化,在此基礎上加入遺傳算子和多機器人調度算法,得到考慮搬運工序的解集,通過迭代優(yōu)化搜索最優(yōu)解。通過多組基準算例測試,驗證了所提算法的有效性和穩(wěn)定性。并且通過對比實驗證明了MMAS信息素更新方式比ACS信息素更新方式更能提升算法求解大規(guī)模問題的能力。本文第二個研究的問題是柔性機器人制造單元多目標調度問題。在分析了NSGA-II算法的局限性后,提出一種改進NSGA-II算法,對調度問題的最大完工時間,機床總負荷和瓶頸機床負荷三個目標進行優(yōu)化。通過種群預篩選機制提升初始解的質量,設計了一種改進帶循環(huán)擁擠度計算的精英選擇策略,提升解的分布性的同時保留種群多樣性,防止算法過早收斂。同樣利用基準算例對算法進行測試,驗證了所提算法的有效性和可靠性。最后,基于提出的混合蟻群算法和改進NSGA-II算法,設計開發(fā)機器人制造單元原型調度系統(tǒng)用于指導實際車間生產。
[Abstract]:With the intensification of market competition and the continuous development of advanced manufacturing technology, robot manufacturing units are more and more widely used in the manufacturing industry. The production scheduling scheme is the basis for the safe and efficient operation of robot manufacturing units. In recent years, scholars have gradually become the focus of research. In order to further reduce manufacturing costs, make balanced use of enterprise resources and adapt to small batch, multi-variety production patterns, it is often necessary to consider the handling process between different processing and adding beds. Optimize multiple objectives that may conflict with each other. Therefore, based on the classical job-shop scheduling theory, this paper studies two kinds of robot manufacturing cell scheduling problems which have important application significance. The scheduling mechanism and solving algorithm of the problem are studied in detail. In this paper, the single-objective scheduling problem for flexible multi-robot manufacturing cells is studied, and a hybrid ant colony algorithm is proposed to solve the problem. Firstly, the problem disjunctive graph model is established, then the state transition rules and pheromone updating rules of the traditional ant colony algorithm are optimized. On this basis, genetic operators and multi-robot scheduling algorithms are added to get the solution set considering the handling process. The optimal solution is searched by iterative optimization. The validity and stability of the proposed algorithm are verified by a number of benchmark examples. The comparison experiments show that the MMAS pheromone updating method is better than the ACS pheromone updating method to improve the ability of the algorithm to solve large-scale problems. The second problem in this paper is the multi-objective scheduling problem of flexible robot manufacturing cells. After analyzing the limitation of NSGA-II algorithm, an improved NSGA-II algorithm is proposed to optimize the maximum completion time of scheduling problem, total load of machine tool and bottleneck load of machine tool. By means of population pre-screening mechanism to improve the quality of initial solution, an elite selection strategy with cyclic congestion calculation is designed to improve the distribution of solution while preserving population diversity, so as to prevent the algorithm from converging prematurely. A benchmark example is also used to test the algorithm, which verifies the validity and reliability of the proposed algorithm. Finally, based on the proposed hybrid ant colony algorithm and improved NSGA-II algorithm, a prototype scheduling system for robot manufacturing units is designed and developed to guide actual workshop production.
【學位授予單位】：廣東工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TH165

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