Starting Age Zero-Based Excel Automation of Optimal Policy Prescriptions and Returns for Machine Replacement Problems with Stationary Data and Age Transition Perspectives

Abstract

Aim: This investigation aimed at automating the computations of optimal replacement policies and rewards for a class of equipment replacement problems with zero starting age, based on age perspectives and stationary pertinent data. Methodology: The aim was achieved by the exploitation of the structure of the states given as functions of the decision periods, in age-transition dynamic programming recursions. Results: Optimal Excel interface and solution implementation templates were designed and automated for the determination of the optimal replacement policies in machine replacement problems, with pertinent data given only as a function of machine age. Conclusion: The automation of these templates obviates the need for manual inputs of the states and stage numbering, as well as the inherent tedious and prohibitive manual computations associated with dynamic programming formulations and may be optimally exploited for sensitivity analyses on such models, paving the way for the solutions of practical and large-scale problems.