This paper presents a novel Approximate Dynamic Programming (ADP) ap-proach to solve large-scale nonlinear constrained Equipment Replacement (ER) problems. Since ADP requires accurate estimations of states for future periods, a heuristic estimator based on data clustering was developed for the case study of this paper with small number of sampling periods. This ADP approach uses a Rollout Algorithm to formulate the problem in a Rolling hori-zon. The model was solved using Genetic Algorithm (GA). This framework was successfully applied for the decision making process of replacement / mainte-nance of 497 transformers in a power distribution company, which led to a significant reduction in the expected costs. The proposed framework possess-es favourable features such as minimizing the effect of uncertainties in the state variables and measurement inaccuracies, which make the model robust and reliable. This work provides a novel general approach that can be em-ployed for other industrial cases and operations research problems.

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