Robust maintenance planning and scheduling for multi-factory production networks considering disruption cost: a bi-objective optimization model and a metaheuristic solution method

The intense competition in the global business market has forced organizations to move from centralized to decentralized structures and develop multi-factory production (MFP) networks. In MFP networks, a well-designed maintenance system is critical for increasing the life cycle of the machine and reducing the probability of disruption. In this regard, this study proposes a bi-objective optimization model for maintenance planning and scheduling in an MFP network. The proposed model determines backup machines for some factories, maintenance performing agents, and machine maintenance periods based on the failure function, in the planning and scheduling phases, respectively. Besides, we propose two strategies for MFP network resilience under disruption. The objective functions are minimizing the maintenance costs and maximizing reliability. To obtain the Pareto front and trade-off the objectives, we first apply a lexicographic approach to find the best payoff matrix, and then the augmented epsilon constraint method is utilized. Because of the inherent uncertainty of the parameters, an effective robust programming approach is employed to effectively control the uncertainty of the input parameters and the conservatism level of the output decisions. To solve the proposed model, the CPLEX Solver is applied for small and medium instances, while for large-scale samples, a heuristic method based on the genetic algorithm is proposed. Finally, to demonstrate the applicability of the model, it is applied to a case study of CNG stations in Iran.