Multi-agent simulation of autonomous industrial vehicle fleets: Towards dynamic task allocation in V2X cooperation mode

In order to facilitate community utilization, we are releasing the code of our project, on the Github page: https://gitlab.inria.fr/jgrosset/AIV_Simulator.The documentation of the simulator is available at : https://aiv-simulator.readthedocs.io/en/latest/readme.htmlInternational audienceThe smart factory leads to a strong digitalization of industrial processes and continuous communication between the systems integrated into the production, storage, and supply chains. One of the research areas in Industry 4.0 is the possibility of using autonomous and/or intelligent industrial vehicles. The optimization of the management of the tasks allocated to these vehicles with adaptive behaviours, as well as the increase in vehicle-to-everything communications (V2X) make it possible to develop collective and adaptive intelligence for these vehicles, often grouped in fleets. Task allocation and scheduling are often managed centrally. The requirements for flexibility, robustness, and scalability lead to the consideration of decentralized mechanisms to react to unexpected situations. However, before being definitively adopted, decentralization must first be modelled and then simulated. Thus, we use a multi-agent simulation to test the proposed dynamic task (re)allocation process. A set of problematic situations for the circulation of autonomous industrial vehicles in areas such as smart warehouses (obstacles, breakdowns, etc.) has been identified. These problematic situations could disrupt or harm the successful completion of the process of dynamic (re)allocation of tasks. We have therefore defined scenarios involving them in order to demonstrate through simulation that the process remains reliable. The simulation of new problematic situations also allows us to extend the potential of this process, which we discuss at the end of the article

Asymptotics of joint maxima for discontinuous random variables

This paper explores the joint extreme-value behavior of discontinuous random variables. It is shown that as in the continuous case, the latter is characterized by the weak limit of the normalized componentwise maxima and the convergence of any compatible copula. Illustrations are provided and an extension to the case of triangular arrays is considered which sheds new light on recent work of Coles and Pauli (Stat Probab Lett 54:373-379, 2001) and Mitov and Nadarajah (Extremes 8:357-370, 2005). This leads to considerations on the meaning of the bivariate upper tail dependence coefficient of Joe (Comput Stat Data Anal 16:279-297, 1993) in the discontinuous case

A survey on smart automated computer-aided process planning (ACAPP) techniques

© 2018, The Author(s). The concept of smart manufacturing has become an important issue in the manufacturing industry since the start of the twenty-first century in terms of time and production cost. In addition to high production quality, a quick response could determine the success or failure of many companies and factories. One the most effective concepts for achieving a smart manufacturing industry is the use of computer-aided process planning (CAPP) techniques. Computer-aided process planning refers to key technology that connects the computer-aided design (CAD) and the computer-aided manufacturing (CAM) processes. Researchers have used many approaches as an interface between CAD and CAPP systems. In this field of research, a lot of effort has been spent to take CAPP systems to the next level in the form of automatic computer-aided process planning (ACAPP). This is to provide complete information about the product, in a way that is automated, fast, and accurate. Moreover, automatic feature recognition (AFR) techniques are considered one of the most important tasks to create an ACAPP system. This article presents a comprehensive survey about two main aspects: the degree of automation in each required input and expected output of computer-aided process planning systems as well as the benefits and the limitations of the different automatic feature recognition techniques. The aim is to demonstrate the missing aspects in smart ACAPP generation, the limitations of current systems in recognising new features, and justifying the process of selection.The authors of the paper would like to sincerely thank the Republic of Iraq Ministry of Higher Education & Scientific Research and the University of Technology, Baghdad for funding the project