本文選題：典型車(chē)間調(diào)度問(wèn)題 + 不相關(guān)并行機(jī)調(diào)度��； 參考：《昆明理工大學(xué)》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(jī)車(chē)間調(diào)度問(wèn)題和置換流水車(chē)間調(diào)度問(wèn)題是兩類(lèi)典型的車(chē)間調(diào)度問(wèn)題,它們是n個(gè)工件在m臺(tái)機(jī)器上加工的許多實(shí)際生產(chǎn)系統(tǒng)生產(chǎn)調(diào)度問(wèn)題的簡(jiǎn)化模型。其中,前者的特征是n個(gè)的工件中的每個(gè)工件可以在m臺(tái)機(jī)器中的任意一臺(tái)上進(jìn)行加工,而后者的特征是n個(gè)工件以相同順序經(jīng)過(guò)m臺(tái)機(jī)器進(jìn)行加工。業(yè)已證明3臺(tái)以上機(jī)器的兩類(lèi)典型車(chē)間調(diào)度問(wèn)題即為NP難題,也是目前生產(chǎn)調(diào)度研究的熱點(diǎn)問(wèn)題。近年來(lái),隨著計(jì)算機(jī)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(lái)越廣泛的關(guān)注。灰狼算法就是一種新近提出的智能優(yōu)化算法,由于其有效性高效性,已被應(yīng)用于求解多種困難的組合優(yōu)化問(wèn)題。本文運(yùn)用灰狼算法對(duì)以上兩種典型車(chē)間調(diào)度問(wèn)題進(jìn)行研究。首先,以最大完工時(shí)間為優(yōu)化目標(biāo),針對(duì)不相關(guān)并行機(jī)調(diào)度問(wèn)題和置換流水車(chē)間調(diào)度問(wèn)題,利用灰狼算法思想,基于工序的編碼方式隨機(jī)產(chǎn)生初始種群,采用高效的更新算子分別實(shí)現(xiàn)對(duì)30個(gè)隨機(jī)產(chǎn)生的實(shí)例和240個(gè)標(biāo)準(zhǔn)測(cè)試實(shí)例的測(cè)試,并將測(cè)試結(jié)果與遺傳算法進(jìn)行對(duì)比,實(shí)驗(yàn)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(shí)間和總流程時(shí)間為優(yōu)化目標(biāo),針對(duì)多目標(biāo)置換流水車(chē)間調(diào)度問(wèn)題,利用多目標(biāo)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(jī)產(chǎn)生兩種方式產(chǎn)生初始種群,實(shí)現(xiàn)對(duì)24個(gè)實(shí)例的測(cè)試,并將結(jié)果與經(jīng)典多目標(biāo)算法——SPEA2算法進(jìn)行比較,測(cè)試結(jié)果表明了多目標(biāo)灰狼算法的優(yōu)越性。最后,將求解置換流水車(chē)間調(diào)度問(wèn)題的灰狼算法應(yīng)用于解決工程實(shí)例,相比回溯搜索算法最優(yōu)解加快430s,使得總完工時(shí)間縮短了 9.75%,進(jìn)一步驗(yàn)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(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TB49

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本文編號(hào)：1863957