Abstract

This paper presents the development of a decision support system for run-time safety management in Smart Work Environments (SWEs). Our approach consists of four main phases: (i) definition of the basic steps of a methodology for run-time safety management; (ii) development of an ontological knowledge-base of safety in work environments; (iii) definition of constraints on the ontology based on organizations’ safety protocols; (iv) communication of relevant information to each actor in the safety management team. We propose a generic ontological model of safety expertise, based on Occupational Safety and Health Regulations (OSHA), that is employed as Knowledge required in our safety management methodology based on the MAPE-K (Monitor–Analyze–Plan–Execute and Knowledge) loop. We present the RAMIRES (Risk-Adaptive Management in Resilient Environments with Security) tool, implementing this methodology. RAMIRES is developed as a dashboard, supporting the safety management team in understanding the risk and its consequences, and to support decision making in risk treatment. RAMIRES interacts with the SWE and the safety management team (actors) in order to: (i) communicate the risks and preventive strategies to actors; (ii) obtain more data about the observed areas to understand the risk and its consequences; and (iii) execute the automatic preventive strategies and support actors in the execution of human-operated preventive strategies. In this paper, we show the details on concepts designed in the safety ontology and illustrate how these concepts can be extended to provide an abstract model of a specific use case. Furthermore, we propose the definition of constraints on the ontology using logic-based rules. Finally, we discuss the advantages and limitations of the proposed methodology regarding the resilience of the environment.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.