發(fā)布時(shí)間：2018-10-15 10:23

【摘要】：當(dāng)今，信息技術(shù)、移動(dòng)互聯(lián)網(wǎng)的快速發(fā)展，電子商務(wù)作為新型的商業(yè)運(yùn)作模式完全融入到人們生活的各個(gè)方面。而配送中心是電子商務(wù)得以實(shí)現(xiàn)的核心，也是發(fā)展電子商務(wù)的一個(gè)瓶頸。在配送中心中，揀貨作業(yè)就是按照訂單上的訂單信息將貨品項(xiàng)從貨架上檢出、分類、集中、包裝、裝箱的作業(yè)過(guò)程。整個(gè)過(guò)程中，，最耗時(shí)且勞動(dòng)量最高的活動(dòng)就是揀選作業(yè)。尤其是近年來(lái)，客戶訂單逐漸向多樣化、小批量的方向發(fā)展，對(duì)揀貨作業(yè)運(yùn)作效率的研究逐漸成為物流供應(yīng)鏈研究領(lǐng)域的熱點(diǎn)，因此對(duì)揀貨作業(yè)運(yùn)作過(guò)程的優(yōu)化對(duì)提高配送效率有重要意義。 本論文研究了揀貨訂單分批模型、分批揀選并行優(yōu)化，首先，對(duì)一定量的訂單按合計(jì)量分批、時(shí)窗分批、訂單量分批、智能型分批模型建立比較，采用智能分批模型利用節(jié)約算法進(jìn)行分批中的訂單路徑計(jì)算，設(shè)計(jì)遺傳算法，在滿足約束的條件下尋找局部最優(yōu)解，使揀貨行走時(shí)間減少，提高揀貨效率。其次，對(duì)建立起的分批模型進(jìn)行進(jìn)一步的并行改進(jìn)，對(duì)細(xì)粒度模型、主從式模型和粗粒度模型三種并行模型研究，選取了主從式模型進(jìn)行并行改進(jìn)，將一定數(shù)量的訂單按種群方式進(jìn)行初始化并行分批計(jì)算，分批訂單就是各個(gè)種群，各個(gè)分批依靠遷移算子傳遞自己的所有變化信息，使各個(gè)批次達(dá)到協(xié)同最優(yōu)，通過(guò)人工選擇系數(shù)對(duì)每個(gè)種群進(jìn)行最優(yōu)個(gè)體保存，取得較好的收斂度[1]，提高了精度，得到最優(yōu)解。 根據(jù)論文中設(shè)計(jì)的揀貨分批策略和并行遺傳優(yōu)化目標(biāo)，對(duì)設(shè)計(jì)策略及并行遺傳算法進(jìn)行測(cè)試用例模擬仿真驗(yàn)證，結(jié)果表明為方法選擇與應(yīng)用提供了依據(jù)并提高了效率。
[Abstract]:Nowadays, with the rapid development of information technology and mobile Internet, E-commerce, as a new business operation mode, is fully integrated into all aspects of people's life. Distribution center is the core of e-commerce and a bottleneck of e-commerce development. In the distribution center, picking is the process of checking, sorting, centralizing, packing and packing items from the shelves according to the order information on the order. The most time-consuming and laborious activity in the whole process is picking. Especially in recent years, customer orders have been gradually diversified and developed in small quantities. The research on the operational efficiency of picking operations has gradually become a hot spot in the field of logistics supply chain research. Therefore, the optimization of picking operation process is of great significance to improve the efficiency of distribution. In this paper, the batch model of picking orders is studied, and the batch picking parallel optimization is studied. First of all, a comparison is established for a certain number of orders according to the combined measurement batching, time window batch, order quantity batching, and intelligent batch model. The intelligent batch model is used to calculate the order path in batches and the genetic algorithm is designed to find the local optimal solution under the condition of satisfying the constraints so as to reduce the walking time of picking and improve the picking efficiency. Secondly, further parallel improvement is carried out on the batch model, and three parallel models, fine-grained model, master-slave model and coarse-grained model, are studied, and the master-slave model is selected for parallel improvement. A certain number of orders are initialized in parallel batches according to the population mode. Each batch order is each population. Each batch depends on the migration operator to transfer all its own change information, so that each batch can achieve the cooperative optimum. The optimal individual preservation of each population is carried out by artificial selection coefficient, and a better convergence degree [1] is obtained, the accuracy is improved and the optimal solution is obtained. According to the batch picking strategy and the parallel genetic optimization goal designed in this paper, the test case simulation of the design strategy and the parallel genetic algorithm is carried out. The results show that the method selection and application are based on and the efficiency is improved.
【學(xué)位授予單位】：西安建筑科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP18

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本文編號(hào)：2272241