A reliability-based maintenance technicians’ workloads optimisation model with stochastic consideration
الموضوعات :
D. E. Ighravwe
1
(Department of Mechanical Engineering, Faculty of Engineering, University of Lagos, Room 10, Mezzanine Complex, Akoka-Yaba, Lagos, Nigeria|Department of Mechanical Engineering, Faculty of Engineering, Ladoke Akintola University of Technology, Ogbomosho, Nigeria)
S. A. Oke
2
(Department of Mechanical Engineering, Faculty of Engineering, University of Lagos, Room 10, Mezzanine Complex, Akoka-Yaba, Lagos, Nigeria)
K. A. Adebiyi
3
(Department of Mechanical Engineering, Faculty of Engineering, Ladoke Akintola University of Technology, Ogbomosho, Nigeria)
الکلمات المفتاحية: Experiential knowledge . Stochastic workloads . Goal programming . Technician’s reliability . Technician’s fatigue,
ملخص المقالة :
The growing interest in technicians’ workloads research is probably associated with the recent surge in competition. This was prompted by unprecedented technological development that triggers changes in customer tastes and preferences for industrial goods. In a quest for business improvement, this worldwide intense competition in industries has stimulated theories and practical frameworks that seek to optimise performance in workplaces. In line with this drive, the present paper proposes an optimisation model which considers technicians’ reliability that complements factory information obtained. The information used emerged from technicians’ productivity and earned-values using the concept of multi-objective modelling approach. Since technicians are expected to carry out routine and stochastic maintenance work, we consider these workloads as constraints. The influence of training, fatigue and experiential knowledge of technicians on workload management was considered. These workloads were combined with maintenance policy in optimising reliability, productivity and earned-values using the goal programming approach. Practical datasets were utilised in studying the applicability of the proposed model in practice. It was observed that our model was able to generate information that practicing maintenance engineers can apply in making more informed decisions on technicians’ management.
