【摘要】：隨著全球性經濟的飛速發(fā)展,制造產業(yè)面臨著新的挑戰(zhàn),企業(yè)要想在激烈的競爭中立于不敗之地,必須以最低的成本、最好的質量、最快的速度和最優(yōu)的服務來響應市場。通過改善生產調度方案,可以有效地提高企業(yè)的生產效率,增強企業(yè)的市場競爭力,由此調度問題應運而生。車間調度問題就是要解決如何利用有限的資源在滿足各種生產約束的前提下,確定工件和設備的加工順序和時間,使性能指標最優(yōu)。然而企業(yè)的實際生產調度過程中,一般不會單純的只考慮一個目標,往往同時考慮多個目標,多目標優(yōu)化問題就會普遍存在,因此多目標混合流水車間調度問題(Hybrid Flow-Shop Scheduling Problem, HFSP)的研究有重大意義。 本文通過對遺傳算法(Genetic Algorithm, GA)和粒子群算法(Particle Swarm Optimization, PSO)進行融合,提出了一種針對HFSP的多目標遺傳粒子群混合算法。遺傳算法具有較強的魯棒性和群體尋優(yōu)能力,但其存在過早收斂和后期搜索效率低的問題,粒子群具有計算簡單和效率高的特點,但存在易早熟和陷入局部最優(yōu)的缺點。在分別分析了遺傳算法和粒子群算法優(yōu)劣勢的基礎上,取長補短,利用遺傳算法優(yōu)秀的群體尋優(yōu)能力,總體上把握進化的方向,根據(jù)粒子群算法計算簡單、效率高的特點,首先進行多個粒子群的獨立進化,快速地全面搜索出較優(yōu)良的個體,各粒子群之間亦實行個體遷移,以擴大搜索領域,然后采集各粒子群的最優(yōu)個體組成遺傳算法的初始種群,進行遺傳操作,隨后用得到的優(yōu)良個體代替種群中的較劣個體,如此循環(huán),高效率地找到目標最優(yōu)解。本文在詳細分析了HFSP基礎上,建立了一套完整的多目標遺傳粒子群混合算法求解方案。 本文實現(xiàn)了利用多目標遺傳粒子群混合算法解決HFSP,首先根據(jù)企業(yè)生產中常見的優(yōu)化目標建立了HFSP模型,在此基礎上,利用HFSP中的經典實例進行測試,分析評估了算法的效率,并將該算法的結論與其他算法進行比較,結果表明,該算法有明顯的優(yōu)越性,可以有效地解決HFSP,具有良好的應用前景。
[Abstract]:With the rapid development of the global economy, the manufacturing industry is facing new challenges. In order to be invincible in the fierce competition, enterprises must respond to the market with the lowest cost, the best quality, the fastest speed and the best service. By improving the production scheduling scheme, the production efficiency of the enterprise can be effectively improved and the market competitiveness of the enterprise can be enhanced, thus the scheduling problem emerges as the times require. The problem of job-shop scheduling is to solve the problem of how to make use of limited resources to determine the processing order and time of workpieces and equipment under the premise of satisfying various production constraints, so as to optimize the performance index. However, in the actual production scheduling process of an enterprise, the multi-objective optimization problem will generally exist because it does not only consider only one goal, but also considers more than one goal at the same time. Therefore, the study of multi-objective hybrid flow shop scheduling problem (Hybrid Flow-Shop Scheduling Problem, HFSP) is of great significance. Based on the fusion of genetic algorithm (Genetic Algorithm, GA) and particle swarm optimization (Particle Swarm Optimization, PSO), a hybrid multi-objective genetic particle swarm optimization algorithm for HFSP is proposed in this paper. Genetic algorithm has strong robustness and population optimization ability, but it has the problems of premature convergence and low search efficiency in late stage. Particle swarm optimization has the characteristics of simple calculation and high efficiency, but it is easy to precocity and fall into local optimization. Based on the analysis of the advantages and disadvantages of genetic algorithm and particle swarm optimization algorithm, the advantages and disadvantages of genetic algorithm and particle swarm optimization algorithm are analyzed, and the excellent population optimization ability of genetic algorithm is used to grasp the direction of evolution in general. According to the characteristics of simple calculation and high efficiency of particle swarm optimization algorithm, First, the independent evolution of multiple particle swarm groups is carried out, and the better individuals are searched out quickly and comprehensively. The individual migration is also carried out among the particle swarm to expand the search field, and then the optimal individuals of each particle swarm are collected to make up the initial population of genetic algorithm. Genetic manipulation is carried out, and then the superior individuals are used to replace the inferior individuals in the population, so that the target optimal solution can be found efficiently in this cycle. In this paper, based on the detailed analysis of HFSP, a complete set of multi-objective genetic particle swarm hybrid algorithm is proposed. In this paper, a hybrid multi-objective genetic particle swarm algorithm is used to solve HFSP,. Firstly, the HFSP model is established according to the common optimization objectives in enterprise production. On this basis, the classical examples in HFSP are used to test, and the efficiency of the algorithm is analyzed and evaluated. The conclusion of the algorithm is compared with other algorithms, and the results show that the algorithm has obvious advantages and can effectively solve HFSP, has a good application prospect.
【學位授予單位】：大連交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP18;TB497

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