發(fā)布時間：2018-05-29 02:33

  本文選題：零等待流水車間 + 量子進化算法　； 參考：《昆明理工大學》2017年碩士論文

【摘要】：生產(chǎn)調(diào)度是制造系統(tǒng)的一個研究熱點,也是理論研究中最為困難的問題之一。本文通過回顧國內(nèi)外調(diào)度問題研究進展,對序不相關零等待流水車間調(diào)度問題和序相關零等待流水車間調(diào)度問題進行了詳細研究。通過分析問題的特性,設計了針對問題的混合量子進化算法及有針對性的啟發(fā)式方法,從而有效求解所研究的問題。仿真實驗結(jié)果顯示了算法的有效性能。本文的主要工作總結(jié)如下:(1)針對帶序不相關設置時間和釋放時間的零等待流水車間調(diào)度問題,提出了一種有效的混合量子進化算法進行求解,優(yōu)化目標為最小化最大完工時間。該算法在全局搜索環(huán)節(jié)中采用自行設計的快速量子概率幅矩陣更新操作以提高算法的進化速度,同時加入4種鄰域搜索機制以提高算法的局部搜索能力。仿真實驗和算法比較驗證了所提算法的有效性。(2)針對帶序相關設置時間和釋放時間的零等待流水車間調(diào)度問題,本文提出了一種高效的混合量子進化算法進行求解,優(yōu)化目標為最小化總體延遲時間。首先,根據(jù)問題的結(jié)構(gòu)性質(zhì),給出了解的快速評價方法,使得算法在相同時間內(nèi)可搜索更多區(qū)域;然后,提出了改進的量子概率幅觀測操作以增強算法全局搜索對解空間的搜索效率;最后,設計了前端省略快速鄰域搜索機制、首次改進跳出策略和子鄰域優(yōu)質(zhì)解即時更新策略,并將其融入基于Interchange子鄰域的局部搜索中,提高了算法對全局搜索發(fā)現(xiàn)的優(yōu)質(zhì)區(qū)域進行細致搜索的能力。在性能測試環(huán)節(jié)對算法進行了詳細的測試,包括參數(shù)優(yōu)化、僅包括全局算法的對比測試、完整算法的對比測試等。通過和6種國際期刊中的有效算法進行仿真比較,驗證了所提算法的高效性和魯棒性。
[Abstract]:Production scheduling is a hot topic in manufacturing system, and it is also one of the most difficult problems in theoretical research. By reviewing the research progress of scheduling problem at home and abroad, this paper makes a detailed study on the scheduling problem of order uncorrelated zero wait flow shop and order-dependent zero wait flow shop scheduling problem. By analyzing the characteristics of the problem, a hybrid quantum evolutionary algorithm and a targeted heuristic method are designed to solve the problem effectively. Simulation results show the effectiveness of the algorithm. The main work of this paper is summarized as follows: (1) aiming at the zero-waiting flow shop scheduling problem with uncorrelated setup time and release time, an effective hybrid quantum evolutionary algorithm is proposed to solve the problem. The optimization goal is to minimize the maximum completion time. In order to improve the evolutionary speed of the algorithm, the algorithm adopts the self-designed fast quantum probability amplitude matrix update operation in the global search link, and adds four neighborhood search mechanisms to improve the local search ability of the algorithm. Simulation experiment and algorithm comparison verify the effectiveness of the proposed algorithm. (2) for the zero-waiting flow shop scheduling problem with sequential correlation setting time and releasing time, this paper proposes an efficient hybrid quantum evolutionary algorithm to solve the problem. The optimization goal is to minimize the total delay time. First of all, according to the structural properties of the problem, a fast evaluation method of solution is given, so that the algorithm can search more areas in the same time. An improved quantum probability amplitude observation operation is proposed to enhance the search efficiency of the global search algorithm for solution space. Finally, a front-end ellipsis fast neighborhood search mechanism is designed, which for the first time improves the jump out strategy and the immediate updating strategy of the sub-neighborhood high quality solution. The algorithm is integrated into the local search based on the Interchange sub-neighborhood, which improves the ability of the algorithm to search the high quality regions found in the global search. In the performance test, the algorithm is tested in detail, including parameter optimization, global algorithm contrast test, complete algorithm contrast test and so on. The effectiveness and robustness of the proposed algorithm are verified by simulation and comparison with the effective algorithms in 6 international journals.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18;TB497

【參考文獻】

相關期刊論文 前1條

1 武妍;包建軍;;一種新的求解TSP的混合量子進化算法[J];計算機應用;2006年10期

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本文編號：1949164