Liquid petroleum gas (LPG) is one area where catastrophic release scenarios have occurred. For this reason, preventive, and protective barriers have to be installed in order to reduce the occurrence and the severity of these scenarios. This article addresses an analysis of deluge system barrier and proposes a making decision process to ensure a high level of reliability, availability, maintainability, and safety (RAMS) using a robust Reliability Analysis with conditional probabilities. To achieve this RAMS target, a methodology for converting fault tree analysis (FTA) in continuous time using Monte Carlo (MC) simulation to Bayesian belief network (BBN) is developed. The probabilistic importance factors (PIFs) for critical components ranking and decision making are also mapped using BBN inferences in Water Deluge Systems (WDS) with an optimization aim using redundancy or maintenance tasks. This analysis illustrates the helpfulness of mapping PIFs into BBN for making a decision in any critical technological infrastructures.
The industry 4.0 concepts are moving towards flexible and energy efficient factories. Major flexible production lines use battery-based automated guided vehicles (AGVs) to optimize their handling processes. However, optimal AGV battery management can significantly shorten lead times. In this paper, we address the scheduling problem in an AGV-based job-shop manufacturing facility. The considered schedule concerns three strands: jobs affecting machines, product transport tasks’ allocations and AGV fleet battery management. The proposed model supports outcomes expected from Industry 4.0 by increasing productivity through completion time minimization and optimizing energy by managing battery replenishment. Experimental tests were conducted on extended benchmark literature instances to evaluate the efficiency of the proposed approach.
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