【摘要】：生產(chǎn)調(diào)度作為流程企業(yè)生產(chǎn)管理的核心,對(duì)提升企業(yè)綜合競(jìng)爭(zhēng)力和經(jīng)濟(jì)效益具有重要作用。流程工業(yè)生產(chǎn)調(diào)度是一個(gè)典型的NP-hard優(yōu)化問題,具有復(fù)雜性、多約束性與多目標(biāo)性,因此需要一種高效可行的優(yōu)化算法用于問題的求解。而協(xié)同優(yōu)化(Collaborative Optimization,CO)是一種新興的多學(xué)科優(yōu)化設(shè)計(jì)算法,它將復(fù)雜的模型分解為若干部分,降低了系統(tǒng)的復(fù)雜程度,減小了問題的求解難度,在流程工業(yè)生產(chǎn)調(diào)度領(lǐng)域具有很高的應(yīng)用價(jià)值。本文的主要研究?jī)?nèi)容如下:(1)針對(duì)協(xié)同優(yōu)化算法在學(xué)科級(jí)缺乏對(duì)目標(biāo)函數(shù)的尋優(yōu)能力,提出了一種自適應(yīng)的協(xié)同優(yōu)化算法(Self-adaptive Collaborative Optimization,SCO)。首先,在系統(tǒng)級(jí)引入?yún)f(xié)同不一致性,改進(jìn)動(dòng)態(tài)松弛因子使優(yōu)化設(shè)計(jì)點(diǎn)快速收斂于極值點(diǎn)。其次,在學(xué)科級(jí)以動(dòng)態(tài)權(quán)重將一致性目標(biāo)函數(shù)和子學(xué)科最優(yōu)目標(biāo)函數(shù)相加作為子學(xué)科目標(biāo)函數(shù),考慮一致性同時(shí)又兼顧子學(xué)科獨(dú)立性。最后,采用二階段優(yōu)化過程,在迭代后期去除動(dòng)態(tài)松弛因子與子學(xué)科最優(yōu)目標(biāo)函數(shù),防止收斂過程震蕩。使用經(jīng)典算例進(jìn)行仿真,優(yōu)化結(jié)果證明SCO算法對(duì)初始點(diǎn)不敏感,優(yōu)化效率得到顯著提升且具有較強(qiáng)的魯棒性。(2)針對(duì)復(fù)雜的流程工業(yè)生產(chǎn)調(diào)度問題,建立了基于離散時(shí)間的流程工業(yè)MILP(Mixed Integer Linear Programming)模型,并將其應(yīng)用于啤酒企業(yè)的糖化釀造車間七日生產(chǎn)調(diào)度實(shí)例。使用SCO算法將模型分解為七個(gè)糖化車間單日生產(chǎn)調(diào)度子學(xué)科與一個(gè)釀造車間七日生產(chǎn)調(diào)度子學(xué)科,同時(shí)使用遺傳算法對(duì)SCO算法的學(xué)科級(jí)與系統(tǒng)級(jí)進(jìn)行求解。通過對(duì)該案例的仿真與分析,驗(yàn)證了模型的合理性以及SCO算法用于求解流程工業(yè)生產(chǎn)調(diào)度問題的可行性與高效性。
[Abstract]:Production scheduling, as the core of production management in process enterprises, plays an important role in enhancing the comprehensive competitiveness and economic benefits of enterprises. Production scheduling in process industry is a typical NP-hard optimization problem with complexity, multi-constraint and multi-objective. Therefore, an efficient and feasible optimization algorithm is needed to solve the problem. Collaborative optimization (Collaborative Optimization,CO) is a new multidisciplinary optimization design algorithm, which decomposes the complex model into several parts, reduces the complexity of the system and reduces the difficulty of solving the problem. It has high application value in production scheduling field of process industry. The main contents of this paper are as follows: (1) an adaptive cooperative optimization algorithm (Self-adaptive Collaborative Optimization,SCO) is proposed to solve the problem of the lack of the ability to optimize the objective function at the subject level. Firstly, the cooperative inconsistency is introduced at the system level, and the dynamic relaxation factor is improved to make the optimal design point converge rapidly to the extremum point. Secondly, the consistency objective function and the subdiscipline optimal objective function are added as the subdiscipline objective function with dynamic weight at the subject level, and the consistency is considered and the subdiscipline independence is taken into account. Finally, the two-stage optimization process is used to eliminate the dynamic relaxation factor and the subdiscipline optimal objective function in the late iteration to prevent the convergence process from oscillating. The simulation results show that the SCO algorithm is insensitive to the initial point, and the optimization efficiency is improved significantly. (2) aiming at the complex production scheduling problem in the process industry, the simulation results show that the algorithm is not sensitive to the initial point, and the optimization efficiency is improved significantly. A discrete time based MILP (Mixed Integer Linear Programming) model for process industry was established and applied to the seven days production scheduling of saccharified brewing workshop in beer enterprises. SCO algorithm is used to decompose the model into seven sub-disciplines of single-day production scheduling in saccharification workshop and one sub-discipline of production scheduling in brewing workshop. At the same time, genetic algorithm is used to solve the SCO algorithm at the subject and system levels. Through the simulation and analysis of this case, the rationality of the model and the feasibility and efficiency of SCO algorithm used to solve the production scheduling problem in the process industry are verified.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TB497

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