Multi-Objective Optimal Acquisition and Production Planning of 3D Printing Technologies under Uncertainty

The rapid evolution of Additive Manufacturing (AM) technologies offers significant opportunities for process improvement, yet selecting the most suitable 3D printers remains challenging due to the high specialization and cost of equipment tailored to specific applications such as prototyping. This complexity is further compounded by uncertainty in future demand and production capacity. To support informed decision-making, this study introduces a stochastic multi-objective optimization framework for the acquisition and utilization of AM technologies over a multi-period planning horizon. The proposed model simultaneously: (i) minimizes the total discounted acquisition cost, (ii) maximizes the probability of satisfying fluctuating demand, and (iii) maximizes the probability of maintaining feasibility with respect to both machine capacity and human supervision availability. Uncertainty is addressed through a Monte Carlo simulation approach and, given the high-dimensional and nonlinear nature of the problem, the solution strategy relies on an evolutionary algorithm to efficiently explore trade-offs among competing goals. The framework is examined using industry-inspired performance data, demonstrating its ability to identify Pareto-optimal solutions and highlighting its potential as a basis for future decision-support applications in AM system planning.