本文選題：遺傳算法　切入點(diǎn)：多目標(biāo)優(yōu)化　出處：《華南理工大學(xué)》2015年碩士論文

【摘要】：倉儲(chǔ)是商品流通的重要環(huán)節(jié)之一,也是物流活動(dòng)的重要支柱。為滿足一定時(shí)間內(nèi)社會(huì)生產(chǎn)和消費(fèi)的需要,必須儲(chǔ)存一定量的物資,保證社會(huì)再生產(chǎn)過程的順利進(jìn)行。我國當(dāng)前的倉儲(chǔ)業(yè)正處在從傳統(tǒng)倉儲(chǔ)業(yè)向現(xiàn)代倉儲(chǔ)業(yè)的過度階段,隨著土地的增值、人工成本的大幅上升和現(xiàn)代物流對(duì)倉儲(chǔ)作業(yè)效率的需求,倉儲(chǔ)作業(yè)必然要求提高空間的利用率、降低人工成本、提升響應(yīng)速度和作業(yè)精準(zhǔn)。本文以W公司LCM模組成品倉當(dāng)前的倉儲(chǔ)管理及貨位分配為背景,探討如何利用人工智能算法—遺傳算法進(jìn)行立體倉庫和平面?zhèn)}庫混合存儲(chǔ)的研究。針對(duì)倉儲(chǔ)管理中存在的問題,在遺傳算法理論研究和倉庫作業(yè)流程詳盡分析的基礎(chǔ)上,重點(diǎn)研究了平面?zhèn)}庫與立體倉庫混合存儲(chǔ)的貨位分配和優(yōu)化問題。采用隨機(jī)存儲(chǔ)的動(dòng)態(tài)貨位分配策略,以考慮周轉(zhuǎn)率的出入庫效率、貨架的穩(wěn)定性、同類相鄰存儲(chǔ)以及產(chǎn)品的先進(jìn)先出為優(yōu)化目標(biāo),建立了平面?zhèn)}庫與立體倉庫混合存儲(chǔ)的多目標(biāo)貨位分配優(yōu)化模型。為了簡(jiǎn)化計(jì)算和提高遺傳算法的效率,采用了改進(jìn)的遺傳算子,運(yùn)用權(quán)重系數(shù)變化法將多目標(biāo)優(yōu)化問題進(jìn)行轉(zhuǎn)換。對(duì)于散貨的出庫,運(yùn)用運(yùn)籌學(xué)中的整數(shù)規(guī)劃思想來減少叉車往返的次數(shù)。最后,通過獲取倉庫產(chǎn)品的相關(guān)數(shù)據(jù)信息,設(shè)置貨位優(yōu)化模型的基本參數(shù),利用Matlab仿真軟件進(jìn)行求解,從輸出的貨位坐標(biāo)和立體仿真圖形可以看出,本文所采用的自適應(yīng)遺傳算法能夠使多目標(biāo)貨位分配數(shù)學(xué)模型有效地收斂到最優(yōu)解,立體仿真圖形清晰直觀地展示了出入庫貨位分配與優(yōu)化效果。在文章最后進(jìn)行了課題研究工作總結(jié),并指出后期的研究?jī)?nèi)容。
[Abstract]:Warehousing is one of the important links in the circulation of goods and also an important pillar of logistics activities. In order to meet the needs of social production and consumption within a certain period of time, a certain amount of materials must be stored. The current warehousing industry in our country is in the transitional stage from traditional warehousing industry to modern warehousing industry. With the increase of land value, the substantial increase of labor cost and the demand of modern logistics for the efficiency of warehousing operations, China's current warehousing industry is in a transitional stage of transition from the traditional warehousing industry to the modern warehousing industry. Warehouse operation must improve space utilization, reduce labor cost, improve response speed and precision. This paper takes the current warehouse management and location allocation of LCM module finished product warehouse of W Company as the background. This paper discusses how to use artificial intelligence algorithm-genetic algorithm to study the hybrid storage of stereoscopic warehouse and plane warehouse, aiming at the problems existing in warehouse management, based on the research of genetic algorithm theory and the detailed analysis of warehouse operation flow. This paper focuses on the allocation and optimization of cargo space in the mixed storage of plane warehouse and stereoscopic warehouse. The dynamic location allocation strategy of random storage is adopted to consider the efficiency of the turnover rate and the shelf stability. In order to simplify the calculation and improve the efficiency of genetic algorithm, a multi-objective cargo allocation optimization model for the mixed storage of plane warehouse and stereoscopic warehouse is established for the same adjacent storage and product first-in-first-out (FIFO). An improved genetic operator is used to transform the multi-objective optimization problem by using the weight coefficient variation method. For bulk goods, the integer programming idea in operations research is used to reduce the number of forklift commutations. By obtaining the relevant data information of warehouse products, setting up the basic parameters of the cargo location optimization model, and using the Matlab simulation software to solve the problem, we can see from the output coordinates and three-dimensional simulation graphics. The adaptive genetic algorithm used in this paper can effectively converge to the optimal solution of the multi-objective location assignment mathematical model. The three-dimensional simulation graphics show clearly and intuitively the effect of allocation and optimization of incoming and outgoing storage spaces. At the end of this paper, the research work is summarized and the later research contents are pointed out.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：F274;TP18

【引證文獻(xiàn)】

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本文編號(hào)：1678966