Study on a storage location strategy based on clustering and association algorithms

Abstract

In this paper, we study the improvement of a storage location strategy through the use of big data technology, including data collection, cluster analysis and association analysis, to improve order picking efficiency. A clustering algorithm is used to categorize the types of goods in orders. Classification is performed based on the turnover of goods, value, sales volume, favorable commodity ratings, whether free shipping is provided and whether cash on delivery is supported. An association algorithm is used to determine the relationships among goods by studying the habits of consumers who buy them. A method for improving the class-based storage strategy is proposed. The picking distance of the improved storage strategy is compared with that of the traditional strategy via simulation experiments. The picking efficiency is shown to be enhanced by the improved strategy.

Industry 4.0 technologies and sustainable warehousing: a systematic literature review and future research agenda

“…For instance, Zhao et al (2017) note that equipment visibility is significantly improved based on real-time data collection and communication through the cloud service, thus enhancing warehouse picking efficiency and effectiveness, as well as reducing inventory level and waste of resources on paper-based tasks. Zhou et al (2020) demonstrate the powerful capability of big data analytics in improving picking efficiency and resource allocation strategy compared to traditional strategies, thereby boosting the order fulfilment rate and eventually enhancing customer satisfaction. The study of Ar et al (2020) and Ruile (2021) propose the potential of IoT supported blockchain in revamping the visibility and traceability of inbound processes, which, in turn, provides better control to warehouse inventory status and inventory forecast leading to improved economic conditions.…”

“…These technologies can directly contribute to the reduction of operational (2015) argued that the construction of IoT infrastructure is essential to increase warehouse transparency and help warehouse management improve their reactivity and competitiveness in a dynamic and complex environment. This could, in turn, boost the order fulfilment rate (Lam et al, 2015;Zhou et al, 2020) and reduce sub-standardised products (Lao et al, 2011), leading to the firm's improved reputation and public image (Mostafa et al, 2019). In their study on the economic sustainability of warehousing via Industry 4.0 technologies, Lorenc and Lerher (2020) propose an artificial reality (AR) software called "Pickup Simulo" that can provide valuable information to warehouse managers on the effectiveness and efficiency of their warehouse practices, thus mitigating the risk of resources' loss.…”

“…Indeed, a high volume of studies demonstrated that Industry 4.0 technologies can provide substantial economic advantages to warehouses. These advantages include enhancing warehouse efficiency (Ballest ın et al, 2013); mitigating operational costs and wastage of resources (energy, material, water) (Fosso Wamba and Takeoka Chatfield, 2011; Lim et al, 2013); and raising customer satisfaction level and competitive advantage (Lam et al, 2015;Mostafa et al, 2019;Zhou et al, 2020;Nantee and Sureeyatanapas, 2021).…”

A Random Classified-Storage Picking Path Model for V-Type Storage Layout

“…Jiang et al [18] developed a crowd perception incentive model based on the voting mechanism, enabling each participant to perform multiple tasks, which greatly improved the participants' execution ability. Scholars used class-based storage strategy, gray clustering, fuzzy c-means clustering, and other methods to classify the types of goods in orders [19][20][21][22]. On this basis, this paper replans the ABC classification of goods.…”

Dwell Time Estimation of Import Containers as an Ordinal Regression Problem

“…Optimization problems arise during the operations of stacking containers in the yard of a terminal [1]. One of those is the container Storage Space Allocation Problem (SSAP), a particular case of the storage location assignment problem [2][3][4][5]. SSAP consists of finding the best allocation for each container in a yard minimizing a criterion such as the number of container reshuffles or the crane traveling distance [6].…”