本文選題：生產(chǎn)調(diào)度 + 粒子群算法��； 參考：《中國礦業(yè)大學》2014年碩士論文

【摘要】：生產(chǎn)調(diào)度是現(xiàn)代企業(yè)生產(chǎn)管理的核心，也是工業(yè)生產(chǎn)過程實現(xiàn)高效可靠運行的基礎(chǔ)和關(guān)鍵。企業(yè)的生產(chǎn)過程需要利用有效的優(yōu)化技術(shù)和生產(chǎn)調(diào)度方法來降低成本、減少浪費，增強企業(yè)的整體競爭力。實際的生產(chǎn)調(diào)度問題大都是動態(tài)、不確定、多約束的組合優(yōu)化問題，已被證明為NP-hard問題，在工業(yè)生產(chǎn)、現(xiàn)代物流、計算機科學等領(lǐng)域有著廣發(fā)的應用，對該類問題的研究對實際生產(chǎn)活動具有很大的理論意義和實用價值。 現(xiàn)有文獻對于不確定調(diào)度問題的研究仍然存在著不足之處。一是研究的不確定性問題約束條件比較單一，較少學者將多種約束融合在一起研究。二是對該類問題的求解主要是利用確定性精確求解方法，在工程應用大規(guī)模的調(diào)度問題中確定性問題的研究并不能完全表達問題模型，對不確定批調(diào)度問題模型構(gòu)建方式的研究很少。本文的主要工作如下： （1）對于工件動態(tài)到達，尺寸有差異，加工時間以及交貨期的不確定等多種約束單機批調(diào)度問題進行了研究，并且將該類問題擴展到更接近實際生產(chǎn)情況的模糊環(huán)境當中，利用模糊數(shù)學理論對單機批調(diào)度不確定性問題進行建模分析，采用基于工件序列的編碼方式，利用分批策略等改善算法的整體性能。 （2）利用粒子群算法(PSO)求解了不確定條件單機批調(diào)度問題。針對標準PSO算法容易陷入局部最優(yōu)造成早熟收斂的問題，提出了一種非線性自適應慣性權(quán)重因子，并在算法后期對全局最優(yōu)值做了自適應變異策略的改進。通過實驗仿真驗證了兩種算法的有效性。 （3）利用差分進化算法(DE)求解了不確定條件單機批調(diào)度問題。對DE算法的差分策略提出了自適應變異算子和隨迭代次數(shù)遞增二次函數(shù)的交叉算子，交叉操作采用基于參數(shù)交配的交叉方法，變異操作采用替換變異方法。通過實驗仿真驗證了兩種算法的有效性。 （4）由于PSO算法存在易陷入局部最優(yōu)的問題，而差分進化算法是一種基于啟發(fā)式算法的全局搜索技術(shù)。為了更好求解不確定條件單機批調(diào)度問題，保持PSO和DE算法種群的多樣性和全局搜索能力，，本文在改進PSO和DE算法的基礎(chǔ)上，提出了基于雙種群的搜索策略的一種混合的差分粒子群算法（DEPSO）。利用DEPSO算法求解不確定條件的單機批調(diào)度問題。通過幾組仿真實驗對比，改進的混合算法(DEPSO)在求解不確定條件單機批調(diào)度問題時取得更優(yōu)的效果。最后，總結(jié)全文并提出對今后不確定條件調(diào)度問題的展望。 該論文有圖16幅，表16個，參考文獻79篇。
[Abstract]:Production scheduling is the core of modern enterprise production management, and it is also the foundation and key to realize efficient and reliable operation of industrial production process. The production process of enterprises needs to use effective optimization technology and production scheduling methods to reduce costs, reduce waste, and enhance the overall competitiveness of enterprises. The actual production scheduling problems are mostly dynamic, uncertain and multi-constrained combinatorial optimization problems, which have been proved to be NP-hard problems, and have been widely used in industrial production, modern logistics, computer science and other fields. The study of this kind of problems has great theoretical significance and practical value for practical production activities. There are still some shortcomings in the research of uncertain scheduling problems in the existing literature. The first is that the uncertainty constraints are relatively simple, and few scholars study them together. Second, the solution of this kind of problem is mainly using deterministic exact solution method. The research of deterministic problem in large-scale scheduling problem in engineering application can not completely express the model of the problem. There is little research on how to construct uncertain batch scheduling model. The main work of this paper is as follows: (1) the dynamic arrival of the workpiece, the difference in size, the uncertainty of processing time and delivery date, and so on, are studied. And the problem is extended to the fuzzy environment which is closer to the actual production situation, and the uncertainty problem of single machine batch scheduling is modeled and analyzed by using fuzzy mathematics theory, and the coding method based on workpiece sequence is adopted. The whole performance of the algorithm is improved by using batch strategy. (2) Particle Swarm Optimization (PSO) is used to solve the uncertain single-machine batch scheduling problem. Aiming at the problem that standard PSO algorithm is easy to fall into local optimum and cause premature convergence, a nonlinear adaptive inertial weight factor is proposed, and the adaptive mutation strategy for global optimal value is improved in the later stage of the algorithm. The experimental results show that the two algorithms are effective. (3) differential evolutionary algorithm (DE) is used to solve the uncertain single-machine batch scheduling problem. For the difference strategy of DE algorithm, the adaptive mutation operator and the crossover operator with quadratic function increasing with the number of iterations are proposed. The crossover method based on parameter mating and the substitution mutation method are used in the crossover operation. The experimental results show the effectiveness of the two algorithms. (4) the PSO algorithm is easy to fall into the local optimal problem, and the differential evolution algorithm is a global search technology based on heuristic algorithm. In order to solve the uncertain condition single machine batch scheduling problem, and to maintain the diversity of PSO and DE algorithm population and the global search ability, this paper improves the PSO and DE algorithm. A hybrid differential particle swarm optimization (DEPSO) algorithm based on a dual population search strategy is proposed. DEPSO algorithm is used to solve the single machine batch scheduling problem with uncertain conditions. By comparison of several simulation experiments, the improved hybrid algorithm (DEPSO) achieves better results in solving single batch scheduling problem with uncertain conditions. Finally, the paper summarizes the full text and puts forward the prospect of uncertain condition scheduling problem in the future. There are 16 pictures, 16 tables and 79 references.
【學位授予單位】：中國礦業(yè)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TB497;TP18

【參考文獻】

相關(guān)期刊論文 前2條

1 谷峰,陳華平,盧冰原,古春生;粒子群算法在柔性工作車間調(diào)度中的應用[J];系統(tǒng)工程;2005年09期

2 ;Statistical learning makes the hybridization of particle swarm and differential evolution more efficient-A novel hybrid optimizer[J];Science in China(Series F:Information Sciences);2009年07期

本文編號：2110430