本文選題：JIT + 供應(yīng)物流�。� 參考：《青島大學(xué)》2014年碩士論文

【摘要】：隨著物流管理意識(shí)的增強(qiáng)和現(xiàn)代物流業(yè)的發(fā)展,企業(yè)對(duì)于物流成本的關(guān)心日漸重視。降低物流成本是企業(yè)物流管理實(shí)踐的首要任務(wù)。 供應(yīng)物流是物流系統(tǒng)中獨(dú)立性較強(qiáng)的子系統(tǒng),和生產(chǎn)系統(tǒng)、財(cái)務(wù)系統(tǒng)等制造企業(yè)各部門以及企業(yè)外部的資源市場(chǎng)、運(yùn)輸部門有密切的聯(lián)系,對(duì)企業(yè)生產(chǎn)的正常、高效運(yùn)作發(fā)揮重要作用。企業(yè)供應(yīng)物流不僅要實(shí)現(xiàn)保證供應(yīng)的目標(biāo),而且要在低成本、少消耗、高可靠性的限制條件下來組織供應(yīng)物流活動(dòng),組織管理難度較高。因此,如何降低供應(yīng)物流的成本,提高物流服務(wù)的水平,實(shí)現(xiàn)供應(yīng)鏈整體優(yōu)化成為制造企業(yè)提升競(jìng)爭(zhēng)力的關(guān)鍵因素之一。 針對(duì)及時(shí)化(Just in time,JIT)模式的供應(yīng)物流短駁合并優(yōu)化問題,建立了供應(yīng)物流系統(tǒng)的短駁合并運(yùn)輸優(yōu)化模型,同時(shí)利用基于遞降最佳適合(Best Fit Decreasing, BFD)思想的啟發(fā)式算法和改進(jìn)的單親遺傳算法(Partheno Genetic algorithm, PGA)分別求解,并分析了此運(yùn)作模式下的供應(yīng)物流的成本和效率。單親遺傳算法沒有采用傳統(tǒng)遺傳算法的交叉算子,僅僅在一條染色體上進(jìn)行基因換位操作,即使種群中各個(gè)個(gè)體均相同,它也可以通過基因換位、基因倒位等遺傳算子來實(shí)現(xiàn)遺傳迭代,初始群體不需具有廣泛多樣性,“早熟收斂”的現(xiàn)象也就不會(huì)存在,因此該算法具備一定優(yōu)勢(shì)。以某汽車制造廠的供應(yīng)物流數(shù)據(jù)為例進(jìn)行計(jì)算機(jī)仿真實(shí)驗(yàn)。仿真結(jié)果表明,PGA算法求出的供應(yīng)成本明顯降低,比BFD啟發(fā)式算法和TGA算法更具優(yōu)勢(shì)。該算法在解決供應(yīng)物流中短駁合并運(yùn)輸問題時(shí)更加快速有效,可有效降低企業(yè)成本,提高經(jīng)濟(jì)效益。
[Abstract]:With the enhancement of logistics management consciousness and the development of modern logistics industry, enterprises pay more and more attention to logistics cost. Reducing logistics cost is the primary task of enterprise logistics management practice. Supply logistics is a subsystem with strong independence in the logistics system. It is closely related to various departments of manufacturing enterprises, such as production systems and financial systems, as well as to the external resource markets of enterprises, and transportation departments are closely related to the normal production of enterprises. Efficient operation plays an important role. Enterprise supply logistics should not only achieve the goal of ensuring supply, but also organize supply logistics activities under the condition of low cost, less consumption and high reliability, which is difficult to organize and manage. Therefore, how to reduce the cost of supply logistics, improve the level of logistics services, realize the overall optimization of supply chain has become one of the key factors to enhance the competitiveness of manufacturing enterprises. Aiming at the supply logistics short barge merger optimization problem based on the "just in time" mode, a short barge combined transportation optimization model of supply logistics system is established. At the same time, the heuristic algorithm based on the idea of descending Best Fit Decreasing, BFD) and the improved Partheno Genetic algorithm, PGA) are used to solve the problem, and the cost and efficiency of supply logistics under this operation mode are analyzed. The parthenogenic genetic algorithm does not use the crossover operator of the traditional genetic algorithm, but only performs gene transposition on one chromosome, even if each individual in the population is the same, it can be transposed through the gene. Genetic operators such as gene inversion to achieve genetic iteration, the initial population does not need to have extensive diversity, "premature convergence" phenomenon will not exist, so the algorithm has certain advantages. Taking the supply logistics data of an automobile factory as an example, the computer simulation experiment is carried out. The simulation results show that the supply cost of the algorithm is obviously reduced, which is superior to the BFD heuristic algorithm and the TGA algorithm. The algorithm is faster and more effective in solving the problem of short barge merging transportation in supply logistics, which can effectively reduce the cost of enterprises and increase economic benefits.
【學(xué)位授予單位】：青島大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F253.7;TP18;F426.471

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本文編號(hào)：1967449