Abstract
This study aims to develop a mathematical decision-support framework for optimizing workforce allocation and operational planning in medical device preventive maintenance (PM). An investigation a case study at a public hospital revealed that the PM plan for medical devices at the operational level often lacks decision-support tools for effective work planning. Due to the large number and diverse range of medical devices in hospitals, the head of the medical device department often encounters difficulties in allocating technicians efficiently across different device categories according to their skill levels and time constraints. This limitation frequently results in disproportionate workloads and an inequitable distribution of tasks among personnel. To address these inefficiencies, this study employs a sequential optimization framework consisting of a Binary Integer Linear Programming (BILP) model followed by an Integer Linear Programming (ILP) model. First, the BILP model assigns maintenance tasks based on technician skills and time constraints with the simultaneous optimization of workload balance and equitable distribution of maintenance tasks among technicians within a single model. The optimized BILP output is then used as input for the ILP model to determine optimal daily task allocation. This sequential approach effectively reduces computational complexity. The proposed models are implemented within Microsoft Excel and integrated with OpenSolver, a free and open-source optimization solver, to provide a flexible and cost-effective alternative to manual planning. The results indicate that the proposed BILP and ILP models reduced labor costs by 12.64% and 12.60%, respectively, compared with the current maintenance planning. The proposed model effectively balances workloads among technicians within the same skill group, resulting in workload deviations of 0.70% and 0.84% for the skilled and general groups, respectively. Furthermore, equitable task distribution is achieved, as the difference in the number of assigned devices of each type among technicians within the same skill group does not exceed one device.