Robust and resilient joint periodic maintenance planning and scheduling in a multi-factory network under uncertainty: A case study

The continuity of activity and competition in the global business market has urged organizations to move from centralized to decentralized structures and develop multi-factor production (MFP) networks. The design and implementation of a maintenance system are essential due to two reasons: first, increasing the life cycle of the equipment, and second, decreasing the probability of disruption in MFP networks. This study proposes a bi-objective optimization model for maintenance planning and scheduling in an MFP network. In the proposed model, how to perform, the performing agent of the maintenance operation, and the maintenance periods are determined based on the failure function in planning and scheduling phases, respectively. Besides, two strategies are proposed for MFP network resilience under disruption occurrence. The objective functions are minimizing maintenance costs and maximizing reliability. The augmented epsilon constraint method is utilized to obtain the Pareto front and trade off the objectives. Because of the parameters' inherent uncertainty, an effective robust programming approach is employed to control the uncertainty of input parameters and the conservatism level of output decisions. The CPLEX Solver can globally solve the proposed model in small and medium instances. A heuristic method based on the genetic algorithm is proposed for solving in the large-scaled sample. Finally, the proposed model is implemented for a case study of CNG stations, in which output results approve the model's applicability.

Automated summary

The study discusses the importance of organizations shifting from centralized to decentralized structures and developing multi-factor production networks in the global business market. By proposing a bi-objective optimization model and utilizing robust programming and heuristic methods for maintenance planning and scheduling, as well as resilience strategies for network disruptions, the uncertainty of input parameters is effectively addressed.