Stochastic robot failure management in an assembly line under industry 4.0 environment

Abstract

Robot failures at stations pose a major challenge to the smooth functioning of fully automated assembly lines in an industry 4.0 environment. A probable solution to this problem is a redundant configuration wherein downstream stations automatically take over upstream operations in the event of a failure. This paper proposes an improved integrated model of operation reallocation and robot allocation for stochastic failures of a robotic assembly line. A particle swarm optimization (PSO) algorithm is developed to solve the proposed integrated model. The novelty of the proposed algorithm is that it optimizes the production rate and power consumption simultaneously at the targeted production rate. The paper demonstrates the superiority of the proposed model over the genetic algorithm and differential evolution models. The robustness of the proposed model is evaluated at different production rates. The proposed model is capable of fulfilling organizational needs of production rate at the minimum energy consumption.