本文選題：訂單揀貨　切入點(diǎn)：模塊化　出處：《華中科技大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：電子商務(wù)的迅猛發(fā)展使得電商企業(yè)之間的競(jìng)爭(zhēng)日益加劇。隨著電商企業(yè)競(jìng)爭(zhēng)的不斷升級(jí),如何提高訂單的響應(yīng)速度已經(jīng)成為各企業(yè)關(guān)注的重要問(wèn)題。此外,“雙11”“雙12”“周年慶”等促銷活動(dòng)的出現(xiàn),導(dǎo)致揀貨作業(yè)工作量穩(wěn)定性下降,這使得企業(yè)的訂單響應(yīng)速度越來(lái)越慢及響應(yīng)速度的穩(wěn)定性越來(lái)越差。因此,如何提高訂單的響應(yīng)速度以及保證響應(yīng)速度的穩(wěn)定性是各電商企業(yè)關(guān)注的重要問(wèn)題。針對(duì)以上問(wèn)題,本文提出了倉(cāng)庫(kù)模塊化揀貨策略。即通過(guò)對(duì)企業(yè)以往訂單的分析,找出不同訂單之間大量重復(fù)出現(xiàn)的產(chǎn)品組合,并且通過(guò)一定的優(yōu)化策略,找出其中有效的產(chǎn)品組合,將其定義為模塊。本文首先對(duì)倉(cāng)庫(kù)揀貨系統(tǒng)優(yōu)化、模塊化以及預(yù)測(cè)方法等概念以及相關(guān)研究現(xiàn)狀進(jìn)行了梳理,為本研究的深入奠定理論基礎(chǔ);其次,對(duì)本文研究的問(wèn)題進(jìn)行了詳細(xì)描述,對(duì)整個(gè)揀貨流程的各個(gè)環(huán)節(jié)進(jìn)行了詳細(xì)闡述,并建立了數(shù)學(xué)模型;再次,詳述了為解決本文提出的問(wèn)題而設(shè)計(jì)的相關(guān)算法;最后,本文利用現(xiàn)實(shí)數(shù)據(jù)模擬模塊化揀貨及模塊預(yù)測(cè)的效果,結(jié)果證明模塊化揀貨可以有效地提高倉(cāng)庫(kù)揀貨的效率,使用BP神經(jīng)網(wǎng)絡(luò)、Elman神經(jīng)網(wǎng)絡(luò)以及指數(shù)平滑的組合預(yù)測(cè)方法可以有效地預(yù)測(cè)模塊數(shù)量,為后續(xù)的研究提出了新的研究思路和方向。本文針對(duì)電商企業(yè)存在的現(xiàn)實(shí)問(wèn)題,提出了模塊化揀貨策略,為B2C電商倉(cāng)庫(kù)揀貨問(wèn)題的解決提供了新思路,對(duì)后續(xù)的相關(guān)研究有一定的參考價(jià)值。
[Abstract]:With the rapid development of electronic commerce, the competition between e-commerce enterprises is becoming more and more intense. How to improve the response speed of orders has become an important concern of enterprises. In addition, the appearance of sales promotion activities such as "Shuang11" and "Shuang12" and "anniversary" has resulted in a decline in the stability of the workload of picking operations. Therefore, how to improve the response speed of orders and how to ensure the stability of response speed is an important concern of e-commerce enterprises. In this paper, the modularized picking strategy of warehouse is put forward, that is, through the analysis of the past orders, we find out the product combination which appears repeatedly among different orders, and find out the effective product combination through certain optimization strategy. Firstly, this paper combs the concepts of warehouse picking system optimization, modularization and prediction methods and related research status, which lays a theoretical foundation for this research. Detailed description of the problems studied in this paper, the whole picking process of each link were described in detail, and established a mathematical model; thirdly, detailed design for solving the problems raised in this paper related algorithms; finally, This paper simulates the effect of modular picking and forecasting by using real data. The results show that modular picking can effectively improve the efficiency of picking goods in warehouse. Using BP neural network Elman neural network and exponential smoothing combined forecasting method can effectively predict the number of modules, and put forward a new research idea and direction for further research. The modular picking strategy is put forward, which provides a new way to solve the problem of picking goods in B2C e-commerce warehouse, and has certain reference value for the related research in the future.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：F724.6;F274

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