基于遺傳算法的綠色制造車間調(diào)度方法研究（本科畢業(yè)設(shè)計(jì)）

摘   要

綠色制造是一種具有顯著社會(huì)效益和經(jīng)濟(jì)效益的現(xiàn)代制造模式，因此是當(dāng)前國(guó)內(nèi)外研究的前沿和熱點(diǎn)課題。車間調(diào)度作為綠色制造系統(tǒng)運(yùn)籌技術(shù)，管理技術(shù)與優(yōu)化技術(shù)發(fā)展的核心，在綠色制造相關(guān)理論研究中占有重要的地位。車間調(diào)度問(wèn)題是組合優(yōu)化問(wèn)題，屬于N-P難題，近年來(lái)各種智能優(yōu)化方法逐漸應(yīng)用到車間調(diào)度問(wèn)題中來(lái)，遺傳算法是其中一種重要而有效的算法。
    本文應(yīng)用遺傳算法求解面向綠色制造的車間調(diào)度問(wèn)題，從作業(yè)車間調(diào)度和流水車間調(diào)度兩個(gè)方面，主要做了以下的研究工作;
    1.針對(duì)傳統(tǒng)遺傳算法在解決大規(guī)模作業(yè)車間調(diào)度問(wèn)題時(shí)進(jìn)化速度過(guò)慢和過(guò)早收斂問(wèn)題，提出了一種嫁接遺傳算法成功應(yīng)用于作業(yè)車間調(diào)度。應(yīng)用混合式嫁接遺傳算法解決多資源作業(yè)車間調(diào)度。針對(duì)面向綠色制造的作業(yè)車間雙向調(diào)度問(wèn)題，提出了一種基于遺傳算法的解決策略，并對(duì)不同規(guī)模的問(wèn)題進(jìn)行了求解。
    2.對(duì)面向綠色制造的確定型及模糊型流水車間調(diào)度問(wèn)題，分別應(yīng)用遺傳算法進(jìn)行求解和仿真。
    3.利用遺傳算法解決面向綠色制造的流水車間作業(yè)排序問(wèn)題。

關(guān)鍵詞:車間調(diào)度遺傳算法作業(yè)車間調(diào)度流水車間調(diào)度綠色制造

ABSTRACT

Green manufacturing system is a contemporary manufacturing mode which can greatly promote the synthesized economic profit of the enterprise, thus it has become the hotspot of the research and application of all kinds of enterprise. It is very important to study shop floor scheduling problems in Green Manufacturing basic theory for fully exerting its high efficiency and flexibility. The shop floor scheduling is principle problem for the development of operation, management and optimization techniques in manufacturing system. Shop floor scheduling is a combinatorial optimization problem, which belongs to N-P problem. Many intelligent computation methods are introduced into scheduling problem in recent years, genetic algorithm(GA) is one of the most important methods.
  In this paper, GA is applied to solve complex shop floor scheduling problem for green manufacturing. I have made some research in the following aspects:
  1. In order to overcome the limitations of low convergence rate and premature convergence appearing in standard GA, an Grafted Genetic Algorithm is proposed and applied in the Job-shop scheduling problem constrained by machines, workers，robots and green factors. This work presents a bi-directional scheduling approach on the basis of combining genetic algorithm to address the job-shop scheduling problem in green manufacturing system.
  2. To the standard flow-shop scheduling problem and fuzzy flow-shop scheduling problem, a solution method based on hybrid Genetic Algorithm is proposed.
  3. A kind of Genetic Algorithm is made for solving the flow-shop sequencing problems and proved suitable to the problems.

Key Words:Shop Floor Scheduling Genetic Algorithm job-shop Scheduling Flow-shop Scheduling Green Manufacturing

目  錄
摘   要 Ⅰ
ABSTRACT Ⅱ
1  緒論 1
1.1 課題的研究意義 1
1.2車間調(diào)度方法的國(guó)內(nèi)外研究狀況及分析 1
1.2.1車間調(diào)度問(wèn)題的建模方法 2
1.2.2車間調(diào)度問(wèn)題的研究方法 3
1.2.3.車間調(diào)度問(wèn)題的研究策略 6
2  遺傳算法技術(shù)研究 9
2.1引言 9
2.2遺傳算法的基礎(chǔ)理論 9
2.2.1遺傳算法的生物學(xué)背景 9
2.2.2遺傳算法的基本概念和處理步驟 10
2.3適應(yīng)度函數(shù) 11
2.3.1目標(biāo)函數(shù)映射成適應(yīng)度函數(shù) 11
2.3.2適應(yīng)度定標(biāo) 12
2.4遺傳算法的基因操作 13
2.4.1選擇算子 13
2.4.2交叉算子 14
2.4.3變異算子 15
2.5遺傳算法控制參數(shù)設(shè)定 16
2.6遺傳算法的特點(diǎn)及相關(guān)理論 16
2.6.1遺傳算法的特點(diǎn) 16
2.6.2遺傳算法相關(guān)理論問(wèn)題 17
2.7本章小結(jié) 18
3  面向綠色制造的作業(yè)車間調(diào)度方法研究 19
3.1引言 19
3.2作業(yè)車間調(diào)度問(wèn)題描述 19
3.3嫁接遺傳算法解決面向綠色制造的作業(yè)車間調(diào)度 20
3.3.1嫁接遺傳算法設(shè)計(jì) 20
3.3.2嫁接遺傳算法在車間作業(yè)調(diào)度問(wèn)題中的應(yīng)用 22
3.4面向綠色制造的多資源作業(yè)車間調(diào)度問(wèn)題 26
3.4.1問(wèn)題描述 27
3.4.2混合式嫁接遺傳算法 27
3.4.3仿真分析 30
3.5面向綠色制造的作業(yè)車間雙向調(diào)度優(yōu)化問(wèn)題 33
3.5.1雙向調(diào)度問(wèn)題描述 33
3.5.2遺傳算法設(shè)計(jì) 33
3.5.3仿真設(shè)計(jì) 36
3.6本章小結(jié) 37
4  面向綠色制造的流水車間調(diào)度方法研究 39
4.1引言 39
4.2流水車間調(diào)度問(wèn)題描述 39
4.3解決流水車間調(diào)度問(wèn)題的啟發(fā)式算法和遺傳算法 40
4.3.1解決流水車間調(diào)度問(wèn)題的幾種啟發(fā)式算法 40
4.3.2解決面向綠色制造的流水車間調(diào)度的遺傳算法 42
4.3.3流水車間調(diào)度的遺傳算法總體設(shè)計(jì)思路 43
4.4混合式遺傳算法解決面向綠色制造的確定型流水車間調(diào)度問(wèn)題 45
4.5面向綠色制造的模糊型流水車間調(diào)度問(wèn)題 51
4.5.1模糊型流水車間調(diào)度數(shù)學(xué)模型 51
4.5.2模糊型流水車間調(diào)度遺傳算法設(shè)計(jì)描述 54
4.6面向綠色制造的流水車間作業(yè)排序問(wèn)題 56
4.6.1算法描述 57
4.6.2算例分析 59
4.7本章小結(jié) 62
5論文總結(jié)與展望 63
致  謝 64
參考文獻(xiàn) 65