Dynamic Maintenance Planning Approach Considering Machine Dependencies and Human Error

In this thesis a novel approach is presented for maintenance planning by considering grouping strategy and human factors. Various objective functions are considered which are: 1) minimizing maintenance planning costs, 2) minimizing failure risks due to the maintenance grouping and 3) minimizing human errors. In order to identify and reduce human error in maintenance, a methodical approach based on cost-benefit analysis is presented, namely human factor effects and analysis (HFEA). HFEA presents a roadmap for selecting key human factors affecting maintenance management as well as the most effective solutions to reduce undesirable safety and operational consequences. In order to model maintenance activities by considering human factors as well as grouping strategy, a dynamic hierarchical model is proposed that minimizes total cost including maintenance costs, set-up cost and grouping and human error penalties. Exact, heuristic and meta-heuristic algorithms are presented to solve the proposed models. In order to model a more realistic situation, different set-up costs are considered for various maintenance activities and work complexity is also incorporated to the model. In order to show the applicability of the proposed approach, three different case studies are applied including a production system, a power plant and a petrochemical unit. It is shown fatigue and time pressure have impact on the preferred maintenance plan. The impact of grouping and human factors on the preferred maintenance plan is shown in different case studies and in various operational contexts. The developed models possess the capability of providing the maintenance decision makers the right information at the right time to make the right maintenance decision.status: publishe