本文關(guān)鍵詞：基于免疫克隆算法的多目標(biāo)flow shop生產(chǎn)調(diào)度的研究　出處：《華東理工大學(xué)》2011年碩士論文　論文類型：學(xué)位論文

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【摘要】：生產(chǎn)調(diào)度問(wèn)題的本質(zhì)是一類優(yōu)化排序問(wèn)題,是運(yùn)籌學(xué)的一個(gè)研究方向。該類問(wèn)題一般可以描述為：在給定生產(chǎn)任務(wù)的前提下,按時(shí)間的先后順序,將有限的人力、物力資源分配給不同的工作任務(wù),以滿足某些指定的性能指標(biāo)。典型的調(diào)度問(wèn)題包括需要完成的產(chǎn)品集合,每個(gè)產(chǎn)品的一系列工序操作集合,各個(gè)工序的加工需要占用的設(shè)備或其它資源,并必須按照一定的加工路線來(lái)進(jìn)行加工。其目標(biāo)是合理地安排產(chǎn)品加工次序和各產(chǎn)品加工開始時(shí)間,使得到的排列順序滿足約束條件,同時(shí)使一些性能指標(biāo)得到優(yōu)化。生產(chǎn)調(diào)度問(wèn)題具有多個(gè)約束、多個(gè)目標(biāo)、不確定性等特點(diǎn),是典型的NP-hard問(wèn)題,作為生產(chǎn)管理中的關(guān)鍵環(huán)節(jié),研究其建模和優(yōu)化,對(duì)提高生產(chǎn)效率有重要意義。 針對(duì)多目標(biāo)生產(chǎn)調(diào)度問(wèn)題,本文深入研究多目標(biāo)優(yōu)化的相關(guān)理論,提出一種適應(yīng)度共享策略,避免將多目標(biāo)問(wèn)題簡(jiǎn)單擬合為單目標(biāo)問(wèn)題。借鑒遺傳算法和免疫克隆算法的基本原理和框架,結(jié)合生產(chǎn)調(diào)度問(wèn)題進(jìn)行改進(jìn),并將其成功應(yīng)用于flow shop調(diào)度問(wèn)題中。 本文的主要貢獻(xiàn)如下： (1)由于多目標(biāo)優(yōu)化問(wèn)題并不存在一個(gè)唯一的最優(yōu)解,而是需要找到Pareto意義下的非劣解。傳統(tǒng)優(yōu)化技術(shù)一般每次只能得到Pareto解集中的一個(gè),而用進(jìn)化算法求解,可以得到更多的Pareto非劣解。本文提出一種基于遺傳算法的適應(yīng)度共享策略,并成功應(yīng)用于連續(xù)函數(shù)優(yōu)化中,通過(guò)仿真實(shí)驗(yàn)驗(yàn)證了算法的可行性。 (2)免疫克隆算法借鑒生物免疫系統(tǒng)的相關(guān)原理和機(jī)制,對(duì)于解決工程優(yōu)化問(wèn)題具有良好效果。本文利用免疫克隆算法的基本原理和框架,針對(duì)多目標(biāo)優(yōu)化問(wèn)題的特點(diǎn),將其改進(jìn)后引入到多目標(biāo)問(wèn)題中。免疫克隆策略對(duì)于Pareto非劣解的精英保留具有良好的作用。 (3)建立了基于最小化完成時(shí)間(makespan)和總流經(jīng)時(shí)間(total flow time)的多目標(biāo)flow shop調(diào)度模型。針對(duì)多目標(biāo)flow shop問(wèn)題,提出一種基于免疫克隆算法的非劣解分級(jí)和擁擠距離計(jì)算策略。通過(guò)適應(yīng)度共享方式,對(duì)多目標(biāo)問(wèn)題的解進(jìn)行評(píng)估。采用改進(jìn)的免疫克隆策略,有效保留和利用了搜索到的非劣解信息；通過(guò)基因變異模式增加群體多樣性,提高算法收斂性。大量仿真驗(yàn)證了調(diào)度模型的正確性和算法的優(yōu)越性。
[Abstract]:The essence of the production scheduling problem is a kind of optimization scheduling problem, is a research direction of operational research. This kind of problem can be described as: given the production task, according to the time order, the limited manpower, material resources allocated to different tasks, to meet the specified performance index. Scheduling problem typically includes the need to complete the set of products, a set of operating procedures for each product, processing the various processes require equipment or other resources, and must be in accordance with certain processing route for processing. Its goal is to arrange the processing order and processing products of each product starting time, the order to meet the constraints, and make some performance indexes. The optimization production scheduling problem with multiple constraints, multiple objectives, characteristics of uncertainty, is a typical NP-hard problem, As the key link in production management, it is of great significance to study its modeling and optimization to improve production efficiency.
Aiming at the multi-objective scheduling problem, this paper studies the related theory of multi-objective optimization, proposes a fitness sharing strategy, avoid the multi-objective problem into single objective problem. A simple fitting from the basic principle and framework of the genetic algorithm and immune clone algorithm, combined with the production scheduling problem is improved, and applied it successfully flow shop scheduling problem.
The main contributions of this article are as follows:
(1) for multi-objective optimization problems is not only one optimal solution, but need to find non inferior solutions under Pareto. Traditional optimization techniques in general can only get a Pareto solution set, and evolutionary algorithm for solving non dominated solutions, can get more Pareto. This paper proposes a genetic algorithm the fitness sharing strategy based on, and successfully applied to continuous function optimization, simulation results verify the feasibility of the algorithm.
(2) the immune clonal algorithm reference principle and mechanism of the biological immune system, to solve engineering optimization problems with good results. This paper uses the basic principle and framework of the immune clonal algorithm for multi-objective optimization problems, the improvement is introduced to the multi-objective problem. Immune clone strategy has a good effect for Pareto Pareto elitist.
(3) is established to minimize the completion time based on (makespan) and total flow time (total flow time) multi-objective flow shop scheduling model for multi objective flow shop problem, this paper proposes a method to calculate the immune clonal algorithm Pareto classification and crowding distance. Through strategy based on fitness sharing method, solution evaluation for the multi-objective problem. By using the improved immune clonal strategy, effective retention and use of non inferior solutions to information search; through gene mutation patterns increase population diversity, improve the convergence of the algorithm. The simulation proved the superiority of the algorithm is correct and the scheduling model.

【學(xué)位授予單位】：華東理工大學(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)博士學(xué)位論文 前1條

1 蒲洪彬;基于人工免疫系統(tǒng)的質(zhì)量功能配置研究[D];華南理工大學(xué);2012年

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本文編號(hào)：1359135