Dynamic Order-Based Scheduling Algorithms for Automated Retrieval System in Smart Warehouses

Lei, LiuJia; Liew, Soung-Yue; Ooi, Boon-Yaik; Qin, Donghong

doi:10.1109/access.2021.3129585

Cited by 8 publications

(1 citation statement)

References 34 publications

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“…Therefore, it needs to be adjusted to be used in a broad range of industries. Jialei et al [21] proposed dynamic order-based scheduling algorithms. The algorithms make picking plans of parts by Automated Guided Vehicle (AGV).…”

Section: Related Workmentioning

confidence: 99%

Parts Supply Support Method for Leveling Workload in In-Process Logistics

Yuasa

Yamawaki²,

Shima³

et al. 2023

IEICE Trans. Inf. & Syst.

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At manufacturing sites, mass customization is expanding along with the increasing variety of customer needs. This situation leads to complications in production planning for the factory manager, and production plans are likely to change suddenly at the manufacturing site. Because such sudden fluctuations in production often occur, it is particularly difficult to optimize the parts supply operations in these production processes. As a solution to such problems, Industry 4.0 has expanded to promote the use of digital technologies at manufacturing sites; however, these solutions can be expensive and time-consuming to introduce. Therefore, not all factory managers are favorable toward introducing digital technology. In this study, we propose a method to support parts supply operations that decreases work stagnation and fluctuation without relying on the experience of workers who supply parts in the various production processes. Furthermore, we constructed a system that is inexpensive and easy to introduce using both LPWA and BLE communications. The purpose of the system is to level out work in in-process logistics. In an experiment, the proposed method was introduced to a manufacturing site, and we compared how the workload of the site's workers changed. The experimental results show that the proposed method is effective for workload leveling in parts supply operations.

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Section: Related Workmentioning

confidence: 99%