Optimizing the Production Cost of Minting with Mixed Integer Programming

TL;DR

This paper presents a mixed-integer programming model to optimize coin minting costs for central banks, reducing extra-shift costs by 24% over 21 quarters compared to current methods.

Contribution

It introduces a novel mixed-integer programming approach to minimize minting costs while satisfying demand and operational constraints.

Findings

24% reduction in extra-shift costs over 21 quarters

Model outperforms current spreadsheet-based approach

Effective in managing demand and inventory constraints

Abstract

For central banks, managing the minting is one of the most important task since a shortage yields negative economic and social impacts, and the budget committed for minting is one of the largest within the central banks. Hence, the central bank requires to find the mixture of coins to be produced that satisfies the demand, inventory and production constraints while minimizing the cost. We propose a mixed-integer programming model that minimize the cost of minting by reducing the number of extra-shifts required while fulfilling the constraints. We also perform a simulation with data of a central bank which shows that the model reduces in 24\% the cost of extra-shifts used during 21 quarters, compared with the spreadsheet based approach used currently at the operation.