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Chapitre D'ouvrage Année : 2022

IoT Safe Area: An innovative approach for a smart decentralized and hybrid decision support process for industrial warehousing

Résumé

This chapter shed lights on a decentralized approach which aims at monitoring resources securely and dealing with products in IoT-based warehouses, and this is part of hybrid logistic decision-making aided processes. These latter are composed of two phases. The first phase consists in logistic centralized decision-making in relation to terminal product allocations, utilizing advanced product classifications, floating locations and the method of static potential fields. The second phase consists in decentralized decision making, hence assuring at the shop floor level the reactive product-trajectory control through the use of the method of dynamic potential fields, while taking into account product-resource interactions. The proposal can achieve that safer warehouse management which is based on the reactivity of smart shelves and products against any critical surrounding risk. Some scenarios for negotiated interaction mechanisms that concern the safe storage and picking operations are proposed. Using intelligence in Warehouse Management System (WMS) results in proposing an “IoT-controlled Safe Area” (IoT-SA) as a new concept. Our contribution is to make decisional, communicational and informational abilities near the warehouse because of those communicating objects. Thus, we can achieve safety assurance while decreasing certain delays in decision-making and increasing the effectiveness of solving dynamic and local disruptions. This leads to avoid the inherent WMS-centralization short comings. Therefore, we present an implementation case for IoT-SA concept, which utilizes Labview software and a platform of a ZigBee wireless sensor network.
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Dates et versions

hal-03711135 , version 1 (01-07-2022)

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Citer

Sourour Trab, Ahmed Zouinkhi, Eddy Bajic. IoT Safe Area: An innovative approach for a smart decentralized and hybrid decision support process for industrial warehousing. Artificial Intelligence of Things for Smart Green Energy Management, 446, Springer International Publishing, pp.173-189, 2022, Studies in Systems, Decision and Control, 978-3-031-04850-0. ⟨10.1007/978-3-031-04851-7_11⟩. ⟨hal-03711135⟩
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