An assets maintenance-workforce planning problem under uncertainty: A chance constraints assisted simulation-optimization approach

Abstract

Preventive and corrective maintenance-workforce planning for assets is a growing area of research, given the presence of uncertainty. We model a Simulation-Optimization framework to solve this challenging large-scale maintenance-workforce planning problem while considering its stochastic nature inside both the simulation and optimization models. A holistic simulation model is proposed through multiple modules to imitate different aspects of the maintenance process, i.e., spare facilities/storage and workforce. The workforce module represents technicians’ types, skills and levels, and employment life-cycle, including recruitment, promotion, and separation/retirement. The present uncertainty in the simulation model demands its corresponding optimization model to cope with the nature of the problem and guarantee a degree of confidence level of the optimal decisions. Therefore, a Chance Constraints Programming (CCP) method is integrated with an evolutionary optimization algorithm to limit the risk level of violating the random constraints while minimizing the total maintenance cost. Discrete Event Simulation (DES) and Self-Adaptive Differential Evolution assisted with Chance Constraints (SA-DE-CC) algorithm are coupled to solve the current problem. The optimization model is proposed to optimize the preventive maintenance frequency and workforce planning problem while implicitly considering corrective maintenance in the simulation model. The Taguchi method performs scenario and risk analysis for multiple CC probability distributions (i.e., Weibull and Normal), confidence levels, and critical parameter combinations. The model’s sensitivity analysis and managerial insights are also discussed.