本文選題：供應(yīng)鏈網(wǎng)絡(luò) + 服務(wù)備件��； 參考：《哈爾濱理工大學(xué)》2014年碩士論文

【摘要】：在當(dāng)前市場(chǎng)經(jīng)濟(jì)全球化的形勢(shì)下，供應(yīng)鏈管理作為先進(jìn)高效的管理模式，是企業(yè)提升競(jìng)爭(zhēng)力和經(jīng)濟(jì)收益的有效途徑之一。目前，隨著市場(chǎng)的大環(huán)境的轉(zhuǎn)變，逐步從相對(duì)成熟的產(chǎn)品供應(yīng)鏈體系向服務(wù)備件供應(yīng)鏈體系發(fā)展。服務(wù)備件作為產(chǎn)品售后領(lǐng)域的主體，越來(lái)越得到制造企業(yè)的重視，特別是現(xiàn)階段產(chǎn)品利潤(rùn)被不斷壓縮，產(chǎn)品售后服務(wù)已經(jīng)成為制造企業(yè)營(yíng)銷和收益的關(guān)鍵。供應(yīng)鏈管理的環(huán)節(jié)之一是網(wǎng)絡(luò)優(yōu)化選址，它通過考慮配送成本、建設(shè)成本和運(yùn)營(yíng)成本等因素，建立合理的配送中心對(duì)供應(yīng)鏈網(wǎng)絡(luò)各環(huán)節(jié)進(jìn)行的協(xié)調(diào)，達(dá)到企業(yè)總經(jīng)營(yíng)成本最小并使企業(yè)利潤(rùn)最大化的目標(biāo)。 本文主要針對(duì)服務(wù)備件供應(yīng)鏈體系深入探究，考慮服務(wù)備件的分銷特性，組建多產(chǎn)品分銷選址模型。同時(shí)，為保證售后業(yè)務(wù)中“服務(wù)至上”的營(yíng)銷目的，引入基于時(shí)間窗的客戶滿意度量化思想，應(yīng)用客戶滿意度函數(shù)與成本函數(shù)集成的評(píng)價(jià)方法對(duì)服務(wù)備件配送選址問題進(jìn)行深入研究。通過建立該服務(wù)備件供應(yīng)鏈選址模型，在保證服務(wù)質(zhì)量的前提下實(shí)現(xiàn)物流網(wǎng)絡(luò)中各配送中心的總配送成本最低的目標(biāo)，進(jìn)而整體上實(shí)現(xiàn)效益最優(yōu)化。 在模型求解方面，考慮遺傳算法對(duì)種群規(guī)模要求過大，必然導(dǎo)致進(jìn)化速度過慢的局限性，本文提出改進(jìn)的克隆免疫遺傳算法，，即對(duì)不同適應(yīng)度的個(gè)體擇優(yōu)選擇，并且從中選取優(yōu)良個(gè)體進(jìn)行自適應(yīng)的克隆操作，這樣能夠進(jìn)一步提升種群的運(yùn)算效率，減少迭代次數(shù)，盡快得到最優(yōu)解，使得算法求解性能得到提高。 通過應(yīng)用汽車服務(wù)備件配送實(shí)例對(duì)該選址模型進(jìn)行驗(yàn)證，使用遺傳算法和克隆免疫算法分別對(duì)此模型求解。將計(jì)算得出的結(jié)果進(jìn)行比對(duì)，驗(yàn)證克隆免疫算法用于求解該模型的正確性和合理性。
[Abstract]:With the globalization of market economy, supply chain management, as an advanced and efficient management mode, is one of the effective ways for enterprises to enhance their competitiveness and economic benefits. At present, with the change of market environment, the relatively mature product supply chain system is gradually developed into the service spare parts supply chain system. Service spare parts, as the main body in the field of product after-sale, have been paid more and more attention by manufacturing enterprises, especially at this stage, the profit of products has been continuously compressed, and product after-sales service has become the key to marketing and income of manufacturing enterprises. One of the links of supply chain management is network optimization location. By considering the factors of distribution cost, construction cost and operation cost, it establishes a reasonable distribution center to coordinate each link of supply chain network. Achieve the goal of minimizing the total operating cost and maximizing the profits of the enterprise. This paper mainly discusses the supply chain system of service spare parts, considering the distribution characteristics of service spare parts, sets up a multi-product distribution location model. At the same time, in order to ensure the marketing purpose of "service first" in after-sales business, the time window based quantification of customer satisfaction is introduced. Based on the evaluation method of customer satisfaction function and cost function, the problem of service spare parts distribution location is studied. By establishing the service spare parts supply chain location model, the goal of the lowest total distribution cost of each distribution center in the logistics network can be achieved under the premise of guaranteeing the service quality, and then the benefit optimization can be realized as a whole. In the aspect of model solving, considering the limitation that the genetic algorithm requires too much population size, it will inevitably lead to the slow evolution speed. In this paper, an improved clonal immune genetic algorithm is proposed, that is, to select individuals with different fitness. And select good individuals to carry out adaptive cloning operation, which can further improve the efficiency of the population, reduce the number of iterations, as soon as possible to obtain the optimal solution, and improve the performance of the algorithm. The model is verified by vehicle service spare parts distribution example and solved by genetic algorithm and clone immune algorithm respectively. The calculated results are compared to verify the correctness and rationality of the clonal immune algorithm for solving the model.
【學(xué)位授予單位】：哈爾濱理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP18;F252

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