A communicating object’s approach for smart logistics and safety issues in warehouses

A communicating object, or connected object, is a key element of the Internet of Things to shift a perceptible real world into a wide digital virtual world known as the cyber-physical system. Knowing that sustainability, safety, and logistic issues are among the significant goals and challenges of modern industrial enterprises, the communicating object can be a relevant concept to guarantee safety performance in logistics and warehouse management. This article presents the impacts and advantages of the communicating object in smart logistics and the design of a communicating object model inspired from Internet of Things European research projects, which controls and monitors safety risks in a hazardous and chemical industrial context. Generic safety-based scenarios are presented, which rely on a set of negotiated interaction mechanisms for storage and picking. The relevant deployment of intelligence in a warehouse management system leads to propose a new concept called “IoT-controlled Safe Area.” Our contribution is to bring informational, communicational, and decisional capabilities close to the warehousing physical world thanks to the communicating object. This enables achieving safety assurance with a decrease in the decision-making delay and an increase in the solving efficiency of local and dynamic disruptions, while avoiding inherent shortcomings of the warehouse management system centralization. For this, an industrial implementation is presented.

[1]  Michalis D Christou Analysis and control of major accidents from the intermediate temporary storage of dangerous substances in marshalling yards and port areas , 1999 .

[2]  V. Agarwal,et al.  The intelligent product driven supply chain , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[3]  Marek B. Zaremba,et al.  Manufacturing Enterprise Control and Management System Engineering: paradigms and open issues , 2003, Annu. Rev. Control..

[4]  André Thomas,et al.  Opportunities to reconsider decision making processes due to Auto-ID , 2007 .

[5]  Erik Jan Hultink,et al.  How Today’s Consumers Perceive Tomorrow’s Smart Products , 2007 .

[6]  Kees Jan Roodbergen,et al.  Design and control of warehouse order picking: A literature review , 2006, Eur. J. Oper. Res..

[7]  André Thomas,et al.  Contribution to reusability and modularity of manufacturing systems simulation models: Application to distributed control simulation within DFT context , 2008 .

[8]  H. El Haouzi,et al.  Design and validation of a product-driven control system based on a six sigma methodology and discrete event simulation , 2009 .

[9]  Florian Michahelles,et al.  Technology, Standards, and Real-World Deployments of the EPC Network , 2009, IEEE Internet Computing.

[10]  André Thomas,et al.  STIGMERGY: A DESIGN PATTERN FOR PRODUCT-DRIVEN SYSTEMS , 2009 .

[11]  Gunther Reinhart,et al.  A holistic approach for the cognitive control of production systems , 2010, Adv. Eng. Informatics.

[12]  吉村 允孝 System design optimization for product manufacturing , 2010 .

[13]  Mu-Chen Chen,et al.  The adaptive approach for storage assignment by mining data of warehouse management system for distribution centres , 2011, Enterp. Inf. Syst..

[14]  Dimitris Kiritsis,et al.  Closed-loop PLM for intelligent products in the era of the Internet of things , 2011, Comput. Aided Des..

[15]  A. Pahlavani,et al.  A hybrid algorithm of simulated annealing and tabu search for graph colouring problem , 2011 .

[16]  R. Koster,et al.  Accidents happen: The influence of safety-specific transformational leadership, safety consciousness, and hazard reducing systems on warehouse accidents , 2011 .

[17]  Ahmed Zouinkhi,et al.  Ambient Intelligence: Awareness Context Application in Industrial Storage , 2011, Wirel. Sens. Netw..

[18]  Julia Kantorovitch,et al.  Towards the future smart products systems design , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[19]  Duncan C. McFarlane,et al.  Product Intelligence in Warehouse Management: A Case Study , 2013, HoloMAS.

[20]  Yves Dallery,et al.  Applying the guaranteed-service model to a decentralized supply chain: Impact on the Cycle-Service-Level , 2013, Proceedings of 2013 International Conference on Industrial Engineering and Systems Management (IESM).

[21]  Xin Guo Ming,et al.  Collaborative product innovation network: Status review, framework, and technology solutions , 2013, Concurr. Eng. Res. Appl..

[22]  Juan Garbajosa,et al.  Providing a Consensus Definition for the Term "Smart Product" , 2013, 2013 20th IEEE International Conference and Workshops on Engineering of Computer Based Systems (ECBS).

[23]  Alessandro Bassi,et al.  Enabling Things to Talk , 2013, Springer Berlin Heidelberg.

[24]  Roberto Musmanno,et al.  A mathematical model for the Multi-Levels Product Allocation Problem in a warehouse with compatibility constraints , 2013 .

[25]  Eleonora Borgia,et al.  The Internet of Things vision: Key features, applications and open issues , 2014, Comput. Commun..

[26]  Lvqing Yang,et al.  The Design and Development of Intelligent Warehouse Management System based on .NET and Internet of Things , 2014 .

[27]  M. Schiraldi,et al.  Multiproduct slot allocation heuristic to minimize storage space , 2014 .

[28]  Ahmed Zouinkhi,et al.  Product Allocation Planning with Safety Compatibility Constraints in IoT-based Warehouse☆ , 2015 .

[29]  Ahmed Zouinkhi,et al.  Application and network layers design for wireless sensor network to supervise chemical active product warehouse , 2014, ArXiv.

[30]  Angappa Gunasekaran,et al.  Bottom-Up Approach based on Internet of things for Order Fulfillment in a Collaborative Warehousing Environment , 2015 .

[31]  Paulo Leitão,et al.  Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges , 2016, Comput. Ind..