本文關鍵詞：軍工企業(yè)生產與物流系統(tǒng)優(yōu)化關鍵問題研究　出處：《沈陽理工大學》2016年碩士論文　論文類型：學位論文

  更多相關文章： 軍工企業(yè) 裝配序列優(yōu)化 生產物流調度 動態(tài)規(guī)劃 粒子群算法

【摘要】：以智能制造、智慧物流為特征的新型工業(yè)化改革已是大勢所趨,我國在該領域的研究也正在全面展開。在制造型企業(yè)中,軍工制造企業(yè)由于其特殊的作用,在我國的經濟發(fā)展中扮演著重要角色。限于我國特殊國情,該型企業(yè)在智能化、信息化方面的建設普遍處于落后狀態(tài)。企業(yè)從接到客戶訂單直至訂單運輸至客戶的全過程,多采用以人工經驗為基礎的粗放型運作管理方式,導致其生產環(huán)節(jié),特別是最后的產品裝配過程效率低下;其物流環(huán)節(jié),特別是從企業(yè)到客戶的運輸成本過高,客戶滿意度較低。提高軍工企業(yè)的智能化、信息化水平有助于提高我國軍工制造行業(yè)整體競爭力,并實現順利轉型。本文以我國某引信制造企業(yè)為研究平臺,針對企業(yè)中存在的問題,結合當前的研究熱點,從引信的生產和物流過程中提煉出兩個需要優(yōu)化的問題,建立優(yōu)化模型,設計求解算法。最終給出可以指導企業(yè)生產運作和物流管理的決策依據,以提高企業(yè)的智能化,信息化管理水平。主要研究兩部分問題:引信裝配序列優(yōu)化問題研究;具有隨機特征的引信訂單生產和運輸協(xié)調調度問題。對兩部分具體內容做如下概述:(1)針對引信的機械結構,分析零件之間的幾何約束關系,以最大化滿足幾何約束次數,以及最小化裝配方向改變次數為目標建立優(yōu)化模型�；陔x散粒子群算法,提出產生初始解的啟發(fā)式算法,以及以深度臨域搜索為特征的改進策略,通過實例驗證此算法在求解速度和穩(wěn)定性方面優(yōu)于其他算法。結合軍工企業(yè)對產品質量的要求,從裝配質量角度,建立裝配序列評價指標體系,最終從多個解中得到有助于提高裝配質量的裝配序列。(2)針對引信訂單生產和運輸兩個環(huán)節(jié),考慮到訂單生產過程中可能出現不可預測情況,將訂單加工時間考慮為隨機變量,訂單的加工環(huán)境考慮為單機環(huán)境。訂單運輸過程采用批運輸的形式,客戶數量考慮單客戶和多客戶兩種情況,分別建立了以訂單期望完工時間和與總運輸費用之和最小化為目標的優(yōu)化模型。對于兩種情況下,分別證明了問題是多項式時間可解和NP-hard的,并分別設計了帶有隨機變量的動態(tài)規(guī)劃算法和粒子群優(yōu)化算法予以解決。通過實際算例,驗證了算法的有效性和穩(wěn)定性。
[Abstract]:The new industrialization reform, which is characterized by intelligent manufacturing and intelligent logistics, has been the trend of the times, and the research in this field is also being carried out in an all-round way. In the manufacturing enterprises, the military manufacturing enterprises play an important role in the economic development of our country because of their special functions. Limited to the special national conditions of our country, the construction of this type of enterprise is generally in the backward state in the construction of intelligence and information. The whole process of receiving customer orders until they are transported to the customer from the enterprise, the extensive operation management mode based on human experience, the production process, especially the low efficiency of the final product assembly process; the logistics links, especially from the enterprise to the customer the high transport costs, low customer satisfaction. Improving the intelligence and information level of military industrial enterprises will help to improve the overall competitiveness of our military manufacturing industry and achieve a smooth transition. In this paper, a fuze manufacturing enterprise in China is taken as the research platform, aiming at the existing problems in the enterprise, combined with the current research hotspots, we extract two optimization problems from the fuze production and logistics process, establish the optimization model and design the solving algorithm. Finally, the decision basis which can guide the operation of enterprise production and logistics management is given in order to improve the intelligence of the enterprise and the level of information management. This paper mainly studies the two part of the problem: the optimization of fuse assembly sequence; the problem of order production and transportation coordination scheduling with random characteristics. The following two parts are summarized as follows: (1) aiming at the mechanical structure of fuzes, the geometric constraint relationship between parts is analyzed, and the optimization model is established to maximize the number of geometric constraints and minimize the number of changes in assembly direction. Based on discrete particle swarm optimization (PSO), a heuristic algorithm to generate initial solution and an improved strategy based on deep in search are proposed. The algorithm is proved to be superior to other algorithms in solving speed and stability. Considering the requirements of military enterprises for product quality, the assembly sequence evaluation index system is established from the perspective of assembly quality, and finally, assembly sequences that are helpful to improve assembly quality are obtained from multiple solutions. (2) in view of the two links of fuze order production and transportation, considering the unpredictable situation of the order production process, the order processing time is considered as a random variable, and the processing environment of the order is considered as a single machine environment. The order transportation process takes the form of batch transportation. Considering the two situations of the number of customers, single customer and multiple customers, the optimization model is established to minimize the sum of the expected time of completion of the order and the total transportation cost. For the two cases, it is proved that the problem is polynomial time solvable and NP-hard, respectively, and dynamic programming algorithm with random variables and particle swarm optimization algorithm are designed respectively to solve them. The effectiveness and stability of the algorithm are verified by a practical example.
【學位授予單位】：沈陽理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：F426.48;F273;TP18

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