本文選題：調(diào)度 + 開放車間��； 參考：《沈陽工業(yè)大學(xué)》2012年碩士論文

【摘要】：車間調(diào)度問題作為制造系統(tǒng)優(yōu)化的核心問題之一,一直受到人們的廣泛關(guān)注。開放車間調(diào)度問題是生產(chǎn)調(diào)度問題中一個非常重要的課題,是學(xué)術(shù)上熟知的困難度極高的組合最優(yōu)化問題。因而,在應(yīng)用領(lǐng)域也受到人們的廣泛關(guān)注。 在實際的工業(yè)環(huán)境中,加工一個工作之前往往要做一些準備。通常,所做準備的時間長短取決于兩個連續(xù)工作之間的順序。這種情況下,所做的準備叫做順序決定的準備。本文是利用分枝界限法以最小化制造期為優(yōu)化目標,求解順序決定準備時間的開放車間調(diào)度問題。 調(diào)度問題在求最優(yōu)解時,往往利用分枝界限法作為求解方法。但是求最優(yōu)解時,因為求解過程非常復(fù)雜而且耗時多,所以在實際中的應(yīng)用并不十分廣泛,特別是當前,各類智能優(yōu)化算法幾乎成為調(diào)度算法的主流。然而,由于多數(shù)智能優(yōu)化算法為隨機優(yōu)化算法,不能總是保證算法在每一次運行中解的質(zhì)量,而且,算法運行過程中對于各類參數(shù)的選擇、算法結(jié)束的條件等問題也難以得到一般化的規(guī)律。因此,研究分枝界限法等傳統(tǒng)最優(yōu)化方法依然有其重要的意義和價值。 在分枝界限法中,設(shè)計良好的上限值和下限值是獲得最優(yōu)解的關(guān)鍵。因為上限值和下限值的品質(zhì)越好,就可以刪除很多無用的分枝節(jié)點,進而加快求解的速度。本文提出幾個上下限值算法,可得到相當優(yōu)的解,但尚無一個良好的刪除法則,故而仍然要浪費很多時間在無用的分枝節(jié)點上。 通過實例驗證比較了幾個上限值算法的優(yōu)劣,但實際應(yīng)用中主要依賴于數(shù)據(jù)。最后,給出了未來的幾點研究方向。
[Abstract]:As one of the core problems of manufacturing system optimization, job shop scheduling problem has been paid more and more attention. Open job shop scheduling problem is a very important problem in production scheduling problem, and it is also an extremely difficult combinatorial optimization problem. Therefore, in the application field also receives the widespread concern. In a practical industrial environment, some preparation is often required before processing a job. Usually, the length of preparation depends on the order between the two consecutive tasks. In this case, preparation is called sequential preparation. In this paper, the branch and bound method is used to solve the open job shop scheduling problem with the aim of minimizing the manufacturing period and determining the preparation time in order. In order to find the optimal solution, the branching and bound method is often used to solve the scheduling problem. However, because the process of solving the optimal solution is very complex and time-consuming, it is not widely used in practice, especially at present, all kinds of intelligent optimization algorithms almost become the mainstream of scheduling algorithms. However, because most of the intelligent optimization algorithms are random optimization algorithms, the quality of the solution can not always be guaranteed in each operation. Moreover, the selection of various parameters during the operation of the algorithm can not always be guaranteed. It is also difficult to obtain a general rule for the condition of the end of the algorithm. Therefore, the study of traditional optimization methods such as branching and bound method still has important significance and value. In the branching and bound method, well designed upper and lower limit values are the key to obtain the optimal solution. Because the better the quality of upper limit and lower limit, many useless branch nodes can be deleted, and the speed of solution can be accelerated. In this paper, several upper and lower limit algorithms are proposed, which can obtain a fairly good solution, but there is not a good deletion rule, so we still have to waste a lot of time on the useless branch nodes. The advantages and disadvantages of several upper bound value algorithms are verified and compared by an example, but the actual application mainly depends on the data. Finally, some research directions in the future are given.
【學(xué)位授予單位】：沈陽工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH186

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本文編號：2050076