發(fā)布時間：2018-05-09 01:24

  本文選題：典型車間調(diào)度問題 + 不相關(guān)并行機調(diào)度�。� 參考：《昆明理工大學》2017年碩士論文

【摘要】：生產(chǎn)調(diào)度是制造業(yè)企業(yè)生產(chǎn)管理的重要工作之一。其中,并行機車間調(diào)度問題和置換流水車間調(diào)度問題是兩類典型的車間調(diào)度問題,它們是n個工件在m臺機器上加工的許多實際生產(chǎn)系統(tǒng)生產(chǎn)調(diào)度問題的簡化模型。其中,前者的特征是n個的工件中的每個工件可以在m臺機器中的任意一臺上進行加工,而后者的特征是n個工件以相同順序經(jīng)過m臺機器進行加工。業(yè)已證明3臺以上機器的兩類典型車間調(diào)度問題即為NP難題,也是目前生產(chǎn)調(diào)度研究的熱點問題。近年來,隨著計算機技術(shù)和人工智能的飛速發(fā)展,生產(chǎn)調(diào)度的智能算法得到了越來越廣泛的關(guān)注�；依撬惴ň褪且环N新近提出的智能優(yōu)化算法,由于其有效性高效性,已被應用于求解多種困難的組合優(yōu)化問題。本文運用灰狼算法對以上兩種典型車間調(diào)度問題進行研究。首先,以最大完工時間為優(yōu)化目標,針對不相關(guān)并行機調(diào)度問題和置換流水車間調(diào)度問題,利用灰狼算法思想,基于工序的編碼方式隨機產(chǎn)生初始種群,采用高效的更新算子分別實現(xiàn)對30個隨機產(chǎn)生的實例和240個標準測試實例的測試,并將測試結(jié)果與遺傳算法進行對比,實驗結(jié)果表明了灰狼算法的可行性與有效性。其次,以最大完工時間和總流程時間為優(yōu)化目標,針對多目標置換流水車間調(diào)度問題,利用多目標灰狼算法思想,采用基于工序的編碼方式,使用構(gòu)造啟發(fā)式算法NEH和隨機產(chǎn)生兩種方式產(chǎn)生初始種群,實現(xiàn)對24個實例的測試,并將結(jié)果與經(jīng)典多目標算法——SPEA2算法進行比較,測試結(jié)果表明了多目標灰狼算法的優(yōu)越性。最后,將求解置換流水車間調(diào)度問題的灰狼算法應用于解決工程實例,相比回溯搜索算法最優(yōu)解加快430s,使得總完工時間縮短了 9.75%,進一步驗證了灰狼算法的優(yōu)越性。
[Abstract]:Production scheduling is one of the important work of manufacturing enterprise production management. Among them, the parallel machine shop scheduling problem and the replacement flow shop scheduling problem are two kinds of typical job shop scheduling problems. They are the simplified models of the production scheduling problems of many practical production systems in which n jobs are processed on m machines. The feature of the former is that each workpiece of n workpieces can be machined on any one of m machines, while the latter is that n workpieces are machined by m machines in the same order. It has been proved that two typical job shop scheduling problems for more than three machines are NP problems and are also hot issues in production scheduling research. In recent years, with the rapid development of computer technology and artificial intelligence, the intelligent algorithm of production scheduling has been paid more and more attention. Grey wolf algorithm is a newly proposed intelligent optimization algorithm. Because of its high efficiency and efficiency, it has been applied to solve a variety of difficult combinatorial optimization problems. In this paper, the gray wolf algorithm is used to study the above two typical job shop scheduling problems. Firstly, aiming at the scheduling problem of unrelated parallel machines and the replacement flow shop scheduling problem, taking the maximum completion time as the optimization goal, the initial population is generated randomly based on the coding method based on the gray wolf algorithm. An efficient update operator is used to test 30 randomly generated and 240 standard test cases, and the test results are compared with the genetic algorithm. The experimental results show the feasibility and effectiveness of the gray wolf algorithm. Secondly, taking the maximum completion time and the total flow time as the optimization goal, aiming at the multi-objective replacement flow shop scheduling problem, using the multi-objective gray wolf algorithm, the coding method based on the working procedure is adopted. The initial population is generated by constructing heuristic algorithm NEH and random generation method, and the test results of 24 instances are realized, and the results are compared with the classical multi-objective algorithm, SPEA2 algorithm. The test results show the superiority of the multi-objective gray wolf algorithm. Finally, the gray wolf algorithm is applied to solve the replacement flow shop scheduling problem. Compared with the backtracking search algorithm, the optimal solution is 430s faster, which shortens the total completion time by 9.75, and further verifies the superiority of the gray wolf algorithm.
【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】：TB49

【參考文獻】

相關(guān)期刊論文 前10條

1 吳楚格;王凌;鄭曉龍;;求解不相關(guān)并行機調(diào)度的一種自適應分布估計算法[J];控制與決策;2016年12期

2 梅攀;吳中元;;多品種非同等并行機作業(yè)車間能效優(yōu)化調(diào)度[J];機電工程技術(shù);2016年09期

3 毛森茂;瞿凱平;陳藝璇;程樂峰;余濤;;基于灰狼多目標算法的電網(wǎng)碳-能復合流優(yōu)化調(diào)度[J];新型工業(yè)化;2016年09期

4 陳昌帥;;二進制灰狼優(yōu)化算法的研究與分析[J];信息系統(tǒng)工程;2016年07期

5 楊紅光;劉建生;;一種結(jié)合灰狼優(yōu)化和K-均值的混合聚類算法[J];江西理工大學學報;2015年05期

6 齊學梅;王宏濤;陳付龍;羅永龍;;求解多目標PFSP的改進遺傳算法[J];計算機工程與應用;2015年11期

7 呂新橋;廖天龍;;基于灰狼優(yōu)化算法的置換流水線車間調(diào)度[J];武漢理工大學學報;2015年05期

8 陳可嘉;周曉敏;;多目標置換流水車間調(diào)度的改進食物鏈算法[J];中國機械工程;2015年03期

9 王騰;周炳海;;Scheduling Multiple Orders per Job with Multiple Constraints on Identical Parallel Machines[J];Journal of Donghua University(English Edition);2013年06期

10 張剛志;周炳海;;帶換模約束的非等效并行機作業(yè)族調(diào)度方法[J];計算機集成制造系統(tǒng);2013年12期

相關(guān)碩士學位論文 前3條

1 林群;回溯搜索算法的改進及其工程應用[D];華中科技大學;2015年

2 歷莉;基于混合離散微粒群算法求解復雜并行機生產(chǎn)調(diào)度問題[D];昆明理工大學;2015年

3 莫禎貞;改進粒子群算法在模糊環(huán)境下平行機批調(diào)度問題中的應用研究[D];中國科學技術(shù)大學;2010年

，

本文編號：1863957