本文選題：并行機調(diào)度 + 到達時間　； 參考：《昆明理工大學(xué)》2015年碩士論文

【摘要】：近年來,作為研究熱點的非傳統(tǒng)生產(chǎn)調(diào)度的相關(guān)問題,如復(fù)雜并行機生產(chǎn)調(diào)度問題等,受到了研究學(xué)者的廣泛關(guān)注。離散微粒群(Discrete Partial Swarm Optimization Algorithm, DPSO)作為一種簡單有效的人工智能算法,已受到機械加工和鋼鐵冶煉等多個工業(yè)領(lǐng)域得到了成功應(yīng)用,對于并行機生產(chǎn)調(diào)度問題的DPSO已經(jīng)成為國際上前沿研究課題。因此,本論文對兩類重要并行機生產(chǎn)調(diào)度問題進行基于DPSO的求解算法研究。論文的主要工作歸納如下：(1)針對最大完成時間(makespan)指標(biāo)下的帶多工序和加工約束并行機調(diào)度問題,通過分析問題的結(jié)構(gòu)特性,重新設(shè)計了一種微粒的位置更新公式,進而與DPSO的全局搜索有機結(jié)合,得到IDPSO,通過隨機生成測試數(shù)據(jù)進行仿真實驗并與其他算法進行比較驗證IDPSO的有效性。(2)針對makespan指標(biāo)下的帶到達時間、多工序、加工約束和序相關(guān)設(shè)置時間的復(fù)雜并行機生產(chǎn)調(diào)度問題,將(1)中的微粒位置更新方法應(yīng)用到該問題中,并加入首次改進跳出策略和基于Interchange和Insert的局部搜索方法,進而結(jié)合IDPSO的全局搜索機制,提出HDPSO,通過比較仿真實驗結(jié)果,驗證了HDPSO相對于其他算法的高效性和有效性(3)針對makespan指標(biāo)下的(2)中的復(fù)雜并行機生產(chǎn)調(diào)度問題,分析DPSO中慣性權(quán)重、學(xué)習(xí)因子對算法的影響,提出一種自適應(yīng)微粒群算法,進而與(2)中提出的HDPSO有機的融合,從而得到一種AHDPSO,仿真實驗驗證了AHDPSO加入自適應(yīng)的必要性和有效性。由文獻調(diào)研可知,特別是基于DPSO算法求解復(fù)雜并行機生產(chǎn)調(diào)度問題的研究十分有限,有些較為復(fù)雜的問題甚至處于空白狀態(tài)。本文針對上述調(diào)度問題的數(shù)學(xué)模型將已有的DPSO算法進行改進,因此針對上述的復(fù)雜并行機生產(chǎn)調(diào)度的研究具有實際的工程價值和學(xué)術(shù)價值。
[Abstract]:In recent years, non-traditional production scheduling issues, such as the production scheduling of complex parallel machines, which are the focus of research, have been widely concerned by researchers. As a simple and effective artificial intelligence algorithm, discrete particle swarm optimization (Partial Swarm Optimization Algorithm, DPSO) has been successfully applied in many industrial fields such as mechanical processing and iron and steel smelting. DPSO for parallel machine production scheduling problem has become an international frontier research topic. In this paper, two important parallel machine scheduling problems are studied based on DPSO. The main work of this paper is summarized as follows: (1) aiming at the parallel machine scheduling problem with multiple working procedures and processing constraints under the maximum completion time (MCP) index, a new updating formula for the position of particles is designed by analyzing the structural characteristics of the problem. Then, combining with the global search of DPSO, we get IDPSO. through random generating test data for simulation experiment and comparing with other algorithms to verify the effectiveness of IDPSO. 2) aiming at the makespan index with time of arrival, multi-working procedure, In the production scheduling problem of complex parallel machines with processing constraints and order correlation setting time, the particle position updating method in F-1) is applied to this problem, and the first improved jump out strategy and local search method based on Interchange and Insert are added. Combined with the global search mechanism of IDPSO, HDPSO is proposed. By comparing the simulation results, it is verified that the efficiency and effectiveness of HDPSO compared with other algorithms are 3) for the complex parallel machine scheduling problem under the makespan index. After analyzing the influence of inertia weight and learning factor on the algorithm in DPSO, an adaptive particle swarm optimization algorithm is proposed, which is combined with the HDPSO proposed in HDPSO. The simulation results show that it is necessary and effective to add adaptive AHDPSO. According to the literature investigation, especially the research on the production scheduling problem of complex parallel machines based on DPSO algorithm is very limited, and some more complex problems are even in blank state. In this paper, the existing DPSO algorithm is improved for the mathematical model of the above scheduling problem, so the research on the production scheduling of the complex parallel machines has practical engineering value and academic value.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F425;TP18

【參考文獻】

相關(guān)期刊論文 前4條

1 劉志雄;;并行機調(diào)度問題粒子群優(yōu)化研究[J];機械設(shè)計與制造;2010年10期

2 潘全科;王凌;趙保華;;解決零空閑流水線調(diào)度問題的離散粒子群算法[J];控制與決策;2008年02期

3 羅家祥,唐立新;帶釋放時間的并行機調(diào)度問題的ILS & SS算法[J];自動化學(xué)報;2005年06期

4 張潔;張朋;劉國寶;;基于兩階段蟻群算法的帶非等效并行機的作業(yè)車間調(diào)度[J];機械工程學(xué)報;2013年06期

相關(guān)博士學(xué)位論文 前1條

1 賈兆紅;粒子群優(yōu)化算法在柔性作業(yè)車間調(diào)度中的應(yīng)用研究[D];中國科學(xué)技術(shù)大學(xué);2008年

，

本文編號：1899871