本文選題：U形多道式 + 貨位優(yōu)化　； 參考：《昆明理工大學》2017年碩士論文

【摘要】：當今的社會是一個多元化且快速發(fā)展的社會,現(xiàn)代化工業(yè)技術(shù)應運而生。傳統(tǒng)意義上的倉儲方式已經(jīng)不能完全滿足生產(chǎn)和物資流通的需要,這集中體現(xiàn)在貨物的存儲空間和擺放不夠合理,存在浪費和耗時的情況,且在使用貨架的過程中沒有考慮對其的保護措施,加速了固定資產(chǎn)的損耗,使得整個的倉庫工作效率偏低。在競爭激烈的現(xiàn)代環(huán)境下,人們的各類需求也隨之增加,伴隨而至的則是貨物的出入庫頻率發(fā)生變化,所以貨位的分配情況也會跟著發(fā)生變化。所以說,貨位優(yōu)化已然成為提升出入庫效率,減少倉儲投資的決定性因素。現(xiàn)如今單堆垛機管理單巷道的貨位優(yōu)化研究已經(jīng)很多,優(yōu)化效果顯著。但對于出入庫作業(yè)頻率不高的部分企業(yè)來說巷道堆垛機閑置的情況尤為嚴重,且一臺巷道堆垛機造價昂貴,造成一定的資源浪費,況且部分企業(yè)在U形模式下的貨物存儲效率低下,沒能很好地利用其高效性。本文針對此現(xiàn)狀對U形多道式立體倉庫的模式下進行優(yōu)化,即一臺巷道堆垛機通過U形多道可跨越兩條巷道同時管理四排貨架貨物的出入庫作業(yè),在提升其利用率的同時減少數(shù)量,實現(xiàn)保障作業(yè)要求的同時節(jié)約成本的目的。本文以全球關于貨位優(yōu)化的研究為理論依據(jù),針對應該具體優(yōu)化的目標建立數(shù)學模型,再通過遺傳算法和粒子群算法的思想分別進行Matlab軟件的編程和實現(xiàn)以及數(shù)據(jù)仿真優(yōu)化實驗后前后的對比。經(jīng)過多次對所建模型的仿真后驗證其理論可行,得出遺傳算法在求解此類問題的效果優(yōu)于粒子群算法的結(jié)論。再將遺傳算法運用到實例當中,通過驗證分析后達到顯著的優(yōu)化效果。本文旨在為企業(yè)提供一種解決實際問題的新思路,提出一種便捷且適應性強的求解貨位優(yōu)化的方法,幫其合理降低成本,實現(xiàn)利益最大化。
[Abstract]:Today's society is a diversified and rapid development society, modern industrial technology came into being. The traditional storage methods can no longer fully meet the needs of production and material circulation, which is reflected in the unreasonable storage space and placement of goods, waste and time consuming. In the process of using the shelf, the protective measures are not considered, which accelerates the loss of fixed assets and makes the working efficiency of the whole warehouse on the low side. In the competitive modern environment, people's demand also increases, and the frequency of goods entering and storing changes, so the distribution of goods will change with it. Therefore, the optimization of cargo space has become the decisive factor to improve the efficiency and reduce the storage investment. Nowadays, there are a lot of research on the cargo location optimization of single stacker crane, and the effect of optimization is remarkable. However, for some enterprises whose operation frequency is not high, the idle situation of laneway stacker is especially serious, and the cost of a laneway stacker is expensive, which results in a certain waste of resources. Moreover, some enterprises in U-mode goods storage efficiency is low, can not make good use of its high-efficiency. In view of this situation, this paper optimizes the mode of U-shaped multi-channel warehouse, that is, a tunnel stacker can cross two roadways and manage four rows of goods from and out of storage at the same time. At the same time to improve the utilization rate and reduce the number, and achieve the purpose of ensuring the operation requirements while saving costs. On the basis of the global research on cargo location optimization, this paper establishes a mathematical model for the target that should be optimized. Then the Matlab software is programmed and implemented by the idea of genetic algorithm and particle swarm optimization algorithm, and the comparison between before and after the data simulation optimization experiment is carried out. The simulation results show that the genetic algorithm is more effective than PSO in solving this kind of problem. Then the genetic algorithm is applied to an example to achieve a significant optimization effect. The purpose of this paper is to provide a new way for enterprises to solve practical problems, and to put forward a convenient and adaptable method to solve the optimization of cargo location, which can help them to reduce the cost reasonably and realize the maximum benefit.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18;F252

【參考文獻】

相關期刊論文 前10條

1 楊瑋;張文燕;常晏彬;邱小紅;王雯;;自動化立體倉庫的貨位分配優(yōu)化[J];現(xiàn)代制造工程;2014年12期

2 王夢蘭;;智能優(yōu)化算法的比較與改進[J];中國水運;2012年12期

3 趙雪峰;，

本文編號：1878031