【摘要】：日益發(fā)展的經(jīng)濟(jì)形勢(shì)下,不斷變革的生產(chǎn)方式要求企業(yè)必須提高生產(chǎn)資源的利用率和生產(chǎn)工作的效率,而生產(chǎn)調(diào)度的優(yōu)化則是其中的關(guān)鍵問(wèn)題,它是MES系統(tǒng)實(shí)施的關(guān)鍵,是ERP實(shí)施的核心。合理有效的調(diào)度算法是生產(chǎn)調(diào)度領(lǐng)域的一個(gè)重要分支,它是學(xué)術(shù)界與企業(yè)界關(guān)注的熱點(diǎn)問(wèn)題,但大多數(shù)調(diào)度問(wèn)題屬于NP-hard問(wèn)題,對(duì)于該問(wèn)題的求解尚未形成一個(gè)系統(tǒng)的理論體系。 本文系統(tǒng)地研究了生產(chǎn)調(diào)度問(wèn)題和微粒群算法,提出基于微粒群算法的改進(jìn)方法,實(shí)現(xiàn)了該算法在三種典型生產(chǎn)調(diào)度問(wèn)題中的應(yīng)用,并研究了相應(yīng)的軟件應(yīng)用系統(tǒng)。 首先,在流程型、離散型和混合流程型生產(chǎn)調(diào)度問(wèn)題的國(guó)內(nèi)外研究的基礎(chǔ)上,深入研究近年來(lái)日益受到關(guān)注的新型智能算法——微粒群算法,針對(duì)微粒群算法容易陷入局部最優(yōu)解、后期收斂速度慢等缺點(diǎn),對(duì)微粒群算法提出改進(jìn),包括基于混沌的微粒群算法和基于免疫混沌的微粒群算法。 其次,基于冶金工業(yè)項(xiàng)目、西航精密件加工項(xiàng)目和煙草排程項(xiàng)目,歸納出流程型、離散型和混合流程型三種典型生產(chǎn)調(diào)度問(wèn)題的定義和約束條件,建立相應(yīng)的數(shù)學(xué)模型,設(shè)計(jì)出用于求解三種調(diào)度問(wèn)題的微粒群算法,詳細(xì)介紹求解過(guò)程中任務(wù)編碼的實(shí)現(xiàn)。結(jié)合實(shí)際工程項(xiàng)目驗(yàn)證了算法的收斂性,給出相應(yīng)的調(diào)度方案。 最后,本文設(shè)計(jì)了生產(chǎn)調(diào)度應(yīng)用軟件的總體框架和功能,并對(duì)系統(tǒng)各模塊的功能進(jìn)行了研究,在此基礎(chǔ)上嵌入三種微粒群優(yōu)化算法,從系統(tǒng)實(shí)用性的角度將理論研究成果進(jìn)行了恰當(dāng)?shù)厝诤稀?br/>[Abstract]:Under the developing economic situation, the constantly changing mode of production requires enterprises to improve the utilization rate of production resources and the efficiency of production work, and the optimization of production scheduling is the key problem, which is the key to the implementation of MES system. It is the core of ERP implementation. Reasonable and effective scheduling algorithm is an important branch in the field of production scheduling, which is a hot issue concerned by academia and business circles, but most of the scheduling problems belong to NP-hard problem. A systematic theoretical system has not yet been formed for solving the problem. In this paper, the production scheduling problem and particle swarm optimization algorithm are systematically studied, an improved method based on particle swarm optimization algorithm is proposed, the application of the algorithm in three typical production scheduling problems is realized, and the corresponding software application system is studied. First of all, based on the research of process, discrete and hybrid process production scheduling problems at home and abroad, a new intelligent algorithm, particle swarm optimization (Particle Swarm Optimization), which has attracted more and more attention in recent years, is deeply studied. In view of the shortcomings of particle swarm optimization algorithm, such as easy to fall into local optimal solution and slow convergence speed in the later stage, the particle swarm optimization algorithm is improved, including particle swarm optimization algorithm based on chaos and particle swarm optimization algorithm based on immune chaos. Secondly, based on the metallurgical industry project, Xihang precision parts processing project and tobacco scheduling project, the definitions and constraints of three typical production scheduling problems, process type, discrete type and mixed process type, are summarized, and the corresponding mathematical models are established. A particle swarm optimization algorithm for solving three scheduling problems is designed, and the implementation of task coding in the process of solving the problem is introduced in detail. Combined with the actual engineering project, the convergence of the algorithm is verified, and the corresponding scheduling scheme is given. Finally, this paper designs the overall framework and function of the production scheduling application software, and studies the functions of each module of the system, on the basis of which three kinds of particle swarm optimization algorithms are embedded. The theoretical research results are properly integrated from the point of view of system practicability.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：TH186

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 夏正喜;JZIC公司生產(chǎn)調(diào)度優(yōu)化研究[D];南昌大學(xué);2012年

，

本文編號(hào)：2477975