AI-driven warehouse automation: A comprehensive review of systems

Enoch Oluwademilade Sodiya,; Uchenna Joseph Umoga,; Olukunle Oladipupo Amoo,; Akoh Atadoga,

doi:10.30574/gscarr.2024.18.2.0063

Cited by 4 publications

References 18 publications

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Efficient Material Flow and Storage Space Determination in Automated Distribution Centers

Alnahhal,

Gjeldum,

Ruzayqat

2024

Journal of Engineering

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There can be two automated storage and retrieval systems (AS/RS) in the same distribution center: one for pallets and the other for cases. Full pallets or half‐full pallets are depalletized and moved to the case storage system. The number of cases depalletized daily for an item needs to be determined based on the average daily demand for that item. Too many depalletized cases ahead of time will inflate the required storage capacity of the case storage system. On the other hand, depalletizing only the necessary cases day by day might lead to too frequent material flow, and therefore more stacker cranes are necessary. Therefore, the number of depalletized pallets affects the number and utilization of stacker cranes in the pallet storage system. It also affects the needed space in the case storage system. This study determines the most efficient number of depalletized cases for three classes of items (A, B, and C), which are determined based on their daily demand. This is done using three scenarios. The total cost is minimized, considering the utilization of the two storage systems. More investigation was done for B items, which have daily demand levels of one layer of cases or more, but less than pallet size. First, analytical equations were formulated to model the system. The model was solved using a genetic algorithm (GA) with special chromosome representations. Results showed that B items can be grouped into two subgroups that are replenished based on two different material flow scenarios. Items with relatively large demand levels have scenario 3 as the optimal one. Results also showed that the model reduces both total costs and stacker crane utilization while improving system flexibility.

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Efficient Material Flow and Storage Space Determination in Automated Distribution Centers

Alnahhal,

Gjeldum,

Ruzayqat

2024

Journal of Engineering

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The Economic and Financial Health of Lithuanian Logistics Companies

Bužinskienė,

Gelashvili

2024

Risks

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In recent decades, the importance of transport and logistics companies has increased considerably, especially for Lithuania, where this sector is on the rise and creating benefits for various users. Therefore, this study aims to analyse the economic–financial situation of transport and logistics companies operating in Lithuania, focusing mainly on their financial risk, probability of bankruptcy, and level of solvency. To achieve these results, 416 companies were analysed based on their data from 2022. The employed methodology included descriptive analysis, quartile ratio analysis, the use of Altman’s Z-score model to predict bankruptcy, and, finally, logistic regression analysis to answer the hypotheses. The results show that the companies analysed in this study were highly profitable, with a high level of solvency and liquidity that did not compromise their continuity in the market. These results were confirmed by the Z-score analysis. In addition, it was observed that the age and size of the companies did not affect their survival on the market. This study presents results that are of great interest for the academic literature, as well as for the management of logistics companies. The originality of the study lies in its relevance and timeliness, presenting robust results for different stakeholders, such as policymakers or new entrepreneurs, among others.

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The Gig Economy

Talukder,

Chowdhury,

Khan

2024

Advances in Human Resources Management and Organizational Development

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Technological developments and economic breakthroughs are propelling the gig economy, revolutionizing human resource management. Significant economic shifts include decentralized workplaces, flexible work arrangements, freelancing, and dynamic pricing patterns. The administration of gig workers is made more accessible by technological improvements, including digital HR tools, AI-driven recruitment, and virtual collaboration platforms. Considering these shifts, strategic workforce planning, regulatory compliance, and initiatives to engage and retain gig workers are imperative. HR departments must adjust to a diverse workforce by promoting inclusivity striking a balance between stability and flexibility. Technology-enabled integration of gig workers improves productivity and job satisfaction. To manage a diversified and regionally distributed workforce, businesses must adopt these advances to enhance their HR procedures. This flexibility is essential to keeping a competitive advantage in the changing labour market, where the gig economy is becoming increasingly important.

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AI-driven warehouse automation: A comprehensive review of systems

Cited by 4 publications

References 18 publications

Efficient Material Flow and Storage Space Determination in Automated Distribution Centers

Efficient Material Flow and Storage Space Determination in Automated Distribution Centers

The Economic and Financial Health of Lithuanian Logistics Companies

The Gig Economy

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