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

  更多相關文章： 生產(chǎn)調(diào)度 流水車間 多目標 免疫克隆算法

【摘要】：生產(chǎn)調(diào)度問題的本質(zhì)是一類優(yōu)化排序問題,是運籌學的一個研究方向。該類問題一般可以描述為：在給定生產(chǎn)任務的前提下,按時間的先后順序,將有限的人力、物力資源分配給不同的工作任務,以滿足某些指定的性能指標。典型的調(diào)度問題包括需要完成的產(chǎn)品集合,每個產(chǎn)品的一系列工序操作集合,各個工序的加工需要占用的設備或其它資源,并必須按照一定的加工路線來進行加工。其目標是合理地安排產(chǎn)品加工次序和各產(chǎn)品加工開始時間,使得到的排列順序滿足約束條件,同時使一些性能指標得到優(yōu)化。生產(chǎn)調(diào)度問題具有多個約束、多個目標、不確定性等特點,是典型的NP-hard問題,作為生產(chǎn)管理中的關鍵環(huán)節(jié),研究其建模和優(yōu)化,對提高生產(chǎn)效率有重要意義。 針對多目標生產(chǎn)調(diào)度問題,本文深入研究多目標優(yōu)化的相關理論,提出一種適應度共享策略,避免將多目標問題簡單擬合為單目標問題。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)度問題進行改進,并將其成功應用于flow shop調(diào)度問題中。 本文的主要貢獻如下： (1)由于多目標優(yōu)化問題并不存在一個唯一的最優(yōu)解,而是需要找到Pareto意義下的非劣解。傳統(tǒng)優(yōu)化技術一般每次只能得到Pareto解集中的一個,而用進化算法求解,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度共享策略,并成功應用于連續(xù)函數(shù)優(yōu)化中,通過仿真實驗驗證了算法的可行性。 (2)免疫克隆算法借鑒生物免疫系統(tǒng)的相關原理和機制,對于解決工程優(yōu)化問題具有良好效果。本文利用免疫克隆算法的基本原理和框架,針對多目標優(yōu)化問題的特點,將其改進后引入到多目標問題中。免疫克隆策略對于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間(makespan)和總流經(jīng)時間(total flow time)的多目標flow shop調(diào)度模型。針對多目標flow shop問題,提出一種基于免疫克隆算法的非劣解分級和擁擠距離計算策略。通過適應度共享方式,對多目標問題的解進行評估。采用改進的免疫克隆策略,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.

【學位授予單位】：華東理工大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH186

【引證文獻】

相關博士學位論文 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;2012年

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本文編號：1359135