An effective dynamic service composition reconfiguration approach when service exceptions occur in real-life cloud manufacturing

Abstract

• A dynamic service composition reconfiguration model when service exceptions occur under practical constraints(DSCRWECPC) in real-life CMfg is established. • DSCRWECPC considers service exceptions, the strict completion time constraint of original CMSC, service occupancy time constraint, and the impact on service quality due to the reconfiguration. • An improved HHO(SCRIHHO) is developed through well-designed strategies aiming at the nature of the DSCRWECPC. • Results of numerical experiments and practical applications verify SCRIHHO is superior to PSO and GWO in tackling the real-world DSCRWECPC. Cloud Manufacturing Service Composition (CMSC), as one of the key issues of Cloud Manufacturing (CMfg), has already attracted much attention. Existing researches on CMSC mainly focus on the optimization efficiency in ideal conditions, while scarcely focus on how to efficiently reconfigure CMSC when service exceptions occur. Uncertain service exceptions often occur during CMSC's execution in real-life CMfg. Thus, it is an urgent issue to perform an adjustment for CMSC to continue to complete the processing task. Besides, some practical constraints are non-negligible in real-world CMfg. Thus, it is necessary to consider them when reconfiguring CMSC. To bridge these gaps, this paper proposes a dynamic service composition reconfiguration model when service exceptions occur under practical constraints (DSCRWECPC). This model redefines optimization objectives, including machining quality, service quality, and cost. Besides, DSCRWECPC considers service exceptions, the cloud manufacturing service occupancy time constraint, the strict time constraint of original CMSC, and dynamic service quality change as its practical constraints. To solve this model, this paper proposes a service composition reconfiguration algorithm (SCRIHHO) based on the strengthened Harris Hawks Optimizer (HHO). Finally, to certify SCRIHHO's performance, this paper conducts numerical experiments and the case application to perform comparisons between SCRIHHO and other algorithms (Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO)). Results showed SCRIHHO in this paper is superior to PSO, GWO when tackling the practical DSCRWECPC in CMfg.