【摘要】：流水車間調(diào)度問題在實際制造生產(chǎn)中廣泛存在，該類問題的研究一直也是制造業(yè)與學(xué)術(shù)界關(guān)心的熱點問題。對該問題進行研究，可以有效提高流水車間的生產(chǎn)效率、降低在制品庫存、縮短拖后期。在如今制造業(yè)競爭日趨激烈、客戶需求日益?zhèn)�性化和多樣化的時代，顯得更為重要。 絕大多數(shù)流水車間調(diào)度是NP-hard問題。近年來，基于自然規(guī)律提出的元啟發(fā)式算法在求解該問題的過程中顯示出了較好的效果。本文所研究的蜂群繁殖算法是結(jié)合了群體優(yōu)化與局部搜索思想的新型優(yōu)化算法，目前已成功應(yīng)用于許多組合優(yōu)化問題中。本文理論結(jié)合實踐，依次對單目標(biāo)、多目標(biāo)流水車間調(diào)度以及帶有限緩沖區(qū)的多目標(biāo)混合流水車間調(diào)度問題進行研究，具體研究內(nèi)容如下。 (1)在求解單目標(biāo)置換流水車間調(diào)度問題時，提出了一種變鄰域搜索與模擬退化算法結(jié)合的局部搜索機制來模擬工蜂的行為，提出了改進的蜂群繁殖算法。然后通過對相關(guān)基準(zhǔn)實例的求解，獲得了較好的優(yōu)化效果，并與其他文獻中的結(jié)果進行比較驗證了該算法的有效性。 (2)多目標(biāo)優(yōu)化問題是近幾年研究的熱點問題。本文針對最大完工時間、總流程時間和總拖期三個典型目標(biāo)，，研究多目標(biāo)置換流水車間調(diào)度問題。基于Pareto思想提出了一種多蜂王的蜂群繁殖算法，采用聚集距離增加非支配解的分散性以及算法的搜索空間；設(shè)計了一種基于Pareto的變鄰域搜索算法，以及一種基于記憶機制的交叉操作。通過實例測試并與文獻中目前最優(yōu)非支配解集進行比較，驗證了所提出算法的有效性。 (3)混合流水車間調(diào)度是流水車間調(diào)度問題中最貼近實際的一類問題，在上述研究的基礎(chǔ)上，本文針對發(fā)動機5C件加工實際應(yīng)用，基于蜂群繁殖算法研究帶有限緩沖區(qū)的多目標(biāo)混合流水車間調(diào)度問題。采用了基于置換工件的編碼方式，然后通過完整調(diào)度構(gòu)造程序生成可行調(diào)度，獲得較好的調(diào)度結(jié)果。通過與已有文獻中的實例進行測試比較，驗證了算法的有效性。 最后，基于上述研究成果，開發(fā)出相應(yīng)的原型系統(tǒng)，進行了全文總結(jié)，并對蜂群算法在流水車間調(diào)度問題上的深入研究進行了展望。
[Abstract]:The flow shop scheduling problem exists widely in the actual manufacturing, and the research of this kind of problem has always been a hot issue of concern in the manufacturing and academic circles. The research on this problem can effectively improve the production efficiency of the flow shop, reduce the inventory of in-process products, and shorten the late period. In today's manufacturing industry increasingly fierce competition, customer demand increasingly personalized and diversified era, appears more and more important. Most of the flow shop scheduling is NP-hard problem. In recent years, the meta-heuristic algorithm based on natural law has shown a good effect in the process of solving the problem. The swarm breeding algorithm studied in this paper is a new optimization algorithm which combines the idea of population optimization and local search and has been successfully applied to many combinatorial optimization problems. In this paper, the single-objective, multi-objective flow shop scheduling and multi-objective hybrid flow-shop scheduling with limited buffers are studied in turn by combining theory and practice. The details of the research are as follows. The main results are as follows: (1) when solving the single objective permutation flow shop scheduling problem, a local search mechanism combining variable neighborhood search and simulated degradation algorithm is proposed to simulate the behavior of worker bees, and an improved colony reproduction algorithm is proposed. Then by solving the related benchmark examples, a better optimization effect is obtained, and the effectiveness of the algorithm is verified by comparing with the results in other literatures. (2) Multi-objective optimization is a hot topic in recent years. In this paper, a multi-objective displacement flow shop scheduling problem is studied for three typical objectives: maximum completion time, total process time and total delay. Based on the idea of Pareto, a colony reproduction algorithm for multi-queen bee is proposed. The clustering distance is used to increase the dispersion of the non-branching solution and the search space of the algorithm. A variable neighborhood search algorithm based on Pareto and a cross operation based on memory mechanism are designed. The effectiveness of the proposed algorithm is verified by example testing and comparing with the current optimal non-dominated solution set in the literature. (3) the mixed flow shop scheduling is the most practical one in the flow shop scheduling problem. On the basis of the above research, this paper aims at the practical application of engine 5C parts processing. The multi-objective hybrid flow shop scheduling problem with limited buffer is studied based on colony reproduction algorithm. The coding method based on the replacement workpiece is adopted, and then the feasible scheduling is generated by the complete scheduling construction program, and the better scheduling results are obtained. The validity of the algorithm is verified by testing and comparing with the existing examples in the literature. Finally, based on the above-mentioned research results, the corresponding prototype system is developed, and the whole paper is summarized, and the in-depth research of swarm algorithm in flow shop scheduling is prospected.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH186;TP301.6

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相關(guān)期刊論文 前2條

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