A recipe for an omnichannel warehouse storage system: Improving the storage efficiency by integrating block stacking and racking

全渠道 仓库 计算机科学 数据库 运筹学 块(置换群论) 运营管理 工程类 工业工程 可靠性工程 数学 业务 几何学 万维网 营销
作者
Thai Young Kim,Su‐Han Woo,Stein W. Wallace
出处
期刊:Computers & Industrial Engineering [Elsevier]
卷期号:182: 109320-109320
标识
DOI:10.1016/j.cie.2023.109320
摘要

Warehouses in multi-channel retailing are expected to possess storage capacity based on the purpose of operations. However, the inefficient use of storage capacity due to omnichannel retail restructuring, wherein mixed-storage items feature both online and offline, has prompted us to revisit the nature of existing storage systems (block stacking and racking). Most warehouses continue to improvise and undertake critical decision-making with regard to profiling balanced storage systems between block stacking and racking, including the efficient slotting of unit-load items across storage systems. This study aimed to establish a framework to systemically improve storage efficiency by achieving an optimum balance between block stacking and racking under inventory uncertainty – lot size and item dimensions. This study ingeniously proposes a two-stage stochastic programming model for robust optimisation during long-run profiling (redesign) and the multi-dimensional knapsack problem for achieving minimum space wastage during slotting (reshuffle). The study adopted a two-fold approach. First, the hypothetical directions for a balanced system, combining increases in buildings’ ceiling heights with its tendency to store low-volume, high-mix items, were examined. Second, the applicability of the two methods – redesign and reshuffle – was examined by considering a real-life case study and the life cycle of warehouse storage design. It was found that the reshuffling approach can resourcefully serve as a short-term solution for warehouses with imminent space issues. Redesigning also demonstrated a long-term space gain in floor space, thus minimising space wastage.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
建议保存本图,每天支付宝扫一扫(相册选取)领红包
实时播报
烟花应助aaaaaah采纳,获得30
刚刚
生与朝露同完成签到,获得积分10
刚刚
2秒前
3秒前
huan完成签到,获得积分10
3秒前
科研通AI6应助科研通管家采纳,获得10
3秒前
liuzz完成签到,获得积分20
3秒前
Alex应助凌风采纳,获得10
3秒前
李健应助科研通管家采纳,获得10
3秒前
科研通AI6应助科研通管家采纳,获得10
3秒前
浮游应助科研通管家采纳,获得10
3秒前
3秒前
Lucas应助科研通管家采纳,获得10
4秒前
搜集达人应助科研通管家采纳,获得10
4秒前
NexusExplorer应助科研通管家采纳,获得10
4秒前
xxfsx应助科研通管家采纳,获得10
4秒前
4秒前
大个应助科研通管家采纳,获得10
4秒前
研友_VZG7GZ应助科研通管家采纳,获得10
4秒前
大模型应助科研通管家采纳,获得10
4秒前
酷波er应助科研通管家采纳,获得10
4秒前
xxfsx应助科研通管家采纳,获得10
4秒前
李爱国应助科研通管家采纳,获得10
5秒前
科研通AI6应助科研通管家采纳,获得30
5秒前
zeefly7发布了新的文献求助10
5秒前
隐形曼青应助科研通管家采纳,获得10
5秒前
JamesPei应助科研通管家采纳,获得10
5秒前
5秒前
Owen应助科研通管家采纳,获得10
5秒前
FashionBoy应助科研通管家采纳,获得10
5秒前
领导范儿应助科研通管家采纳,获得10
5秒前
Akim应助科研通管家采纳,获得10
5秒前
5秒前
小二郎应助科研通管家采纳,获得10
5秒前
Akim应助emnjkl采纳,获得10
5秒前
WangJ1018发布了新的文献求助10
5秒前
情怀应助科研通管家采纳,获得10
5秒前
布布完成签到,获得积分10
5秒前
5秒前
7秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
List of 1,091 Public Pension Profiles by Region 1041
Mentoring for Wellbeing in Schools 600
Binary Alloy Phase Diagrams, 2nd Edition 600
Atlas of Liver Pathology: A Pattern-Based Approach 500
A Technologist’s Guide to Performing Sleep Studies 500
EEG in Childhood Epilepsy: Initial Presentation & Long-Term Follow-Up 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5491528
求助须知:如何正确求助?哪些是违规求助? 4589949
关于积分的说明 14428449
捐赠科研通 4522201
什么是DOI,文献DOI怎么找? 2477761
邀请新用户注册赠送积分活动 1462901
关于科研通互助平台的介绍 1435597