A literature review of smart warehouse operations management

Zhen, Lu; Li, Haolin

doi:10.1007/s42524-021-0178-9

Cited by 50 publications

(14 citation statements)

References 163 publications

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“…The simultaneous appearance of various keywords in a work of literature is known as keyword co-occurrence [ 104 ]. Keywords are a very condensed version of the study content.…”

Section: Research Hotspots and Evolutionary Trendsmentioning

confidence: 99%

The evolution of data pricing: From economics to computational intelligence

Hao,

Deng,

2023

Heliyon

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“…The simultaneous appearance of various keywords in a work of literature is known as keyword co-occurrence [ 104 ]. Keywords are a very condensed version of the study content.…”

Section: Research Hotspots and Evolutionary Trendsmentioning

confidence: 99%

The evolution of data pricing: From economics to computational intelligence

Hao,

Deng,

2023

Heliyon

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“…This method can be further improved by incorporating warehouse robots to reduce errors and increase efficiency [4]. Smart warehouses are becoming increasingly trendy [5]. This means choosing how to make any warehouse smart is also an extremely important decision.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Approach to Yard Management Using QR Code-Based Technology

Pranith Saravanan,

Anurag De,

Dhyan Prasad

2024

Int Res J Adv Engg Hub

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Traditional yard and warehouse management systems are often prone to human errors which result in misplaced materials and products, operational disruptions and delays. To address these challenges, we propose a novel QR-based Yard Management System mobile application that leverages the ubiquity of QR codes to digitalize the process of material identification and placement verification. Upon receiving a material, dedicated QR codes placed on the material and the bins are scanned to automatically keep track of which bin the material is being placed in along with a validation process that ensures that the material is being placed in the right bin. This reduces the likelihood of misplacement of products, minimizes inventory discrepancies and any associated disruptions. Additionally, the system also provides real-time visibility into the product and bin statuses, enabling the yard managers to effectively track material locations and ensure their timely availability. By streamlining the material handling process, the Yard Management System enhances overall yard operation efficiency, thereby improving productivity and cost savings.

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“…Smart warehouses-which are equipped with advanced technologies such as robotics, artificial intelligence, and the Internet of Things-have emerged as a solution to address the limitations of traditional warehouses [6]. These state-ofthe-art facilities are designed to streamline processes, optimize inventory management, and expedite order fulfillment [7]. A critical aspect of warehouse automation is the efficient assignment of tasks within the warehouse [8].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Task Assignment and Shelf Reallocation in Smart Warehouses

Wu,

Chiu,

2024

IEEE Access

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With the rapid development of online shopping and the prosperity of the e-commerce industry in recent years, traditional warehouses are struggling to cope with increasing order volumes. Accordingly, smart warehouses have gained considerable attention for their relatively high efficiency and productivity. In such warehouses, robots transport shelves to picking stations on the basis of tasks assigned to them and then return to the inventory area. An accurate task assignment method must be developed to achieve high efficiency in smart warehouses; however, existing task assignment methods use limited information, resulting in a lack of insight regarding future tasks in warehouses. This paper proposes a method based on the deep Q-network (DQN) that considers inventory for task assignments. The developed DQN-based model determines shelf return locations on the basis of current states to improve warehouse performance. The proposed method was compared with a traditional task assignment method, namely regret and marginal-cost based task assignment algorithm (RMCA); the results indicated that compared with the RMCA method, the proposed approach is more efficient and faster and can accommodate more robots.

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A literature review of smart warehouse operations management

Cited by 50 publications

References 163 publications

The evolution of data pricing: From economics to computational intelligence

The evolution of data pricing: From economics to computational intelligence

An Innovative Approach to Yard Management Using QR Code-Based Technology

Deep Reinforcement Learning for Task Assignment and Shelf Reallocation in Smart Warehouses

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