Group Preventive Maintenance Model for Multi-unit Series System: A TLBO Algorithm-Based Approach

TLDR: A recently developed meta-heuristic named teaching–learning-based optimization (TLBO) algorithm is applied to optimize the objective function to obtain an optimum group of components and PM intervals which minimizes the expected total maintenance cost of the system per unit time.

Abstract: Group maintenance deals with performing preventive maintenance (PM) on other components when the system stops due to induced failure or scheduled PM interval of a particular component. Group maintenance actions save downtime costs and other production losses. However, simultaneous maintenance of all units is not always economically beneficial. As a consequence, the decision to which group of components is maintained is very selective due to economic and stochastic interdependencies. In the present paper, we propose a novel and efficient group maintenance model in the multi-unit series system for grouping under PM intervals. The objective is to obtain an optimum group of components and PM intervals which minimizes the expected total maintenance cost of the system per unit time. A recently developed meta-heuristic named teaching–learning-based optimization (TLBO) algorithm is applied to optimize the objective function. The peculiarity of TLBO is that unlike other evolutionary-based heuristics it is a parameterless algorithm which makes it computationally easy to understand and implement. Computational results yield the effectiveness of the proposed approach when compared with traditional maintenance practices.