MULTI-OBJECTIVE OPTIMIZATION OF ADDITIVE AND TRADITIONAL MANUFACTURING STRATEGIES UNDER UNCERTAINTY

Abstract

This thesis addresses production scheduling challenges in additive manufacturing (AM) by using a multi objective optimization approach that simultaneously considers cost and time. While traditional models often focus on a single objective, our work integrates both, reflecting the real world tradeoffs manufacturers face. We develop a mathematical model that selects the appropriate manufacturing technologies and determines production quantities, ensuring that demand is met within specified time and budget limits. A key focus of this study is understanding how economies of scale influence the optimal strategies as demand increases. As production volumes grow, the model captures how cost efficiencies emerge and how the tradeoffs between time and cost evolve. We apply Pareto frontier analysis to identify the best compromise solutions and perform a Monte Carlo sim- ulation to assess the probability of exceeding time or cost targets, offering a deeper view of operational risks under uncertainty. The paper is organized as follows. We first discuss the challenges of production planning in AM and the motivation for a multi objective framework. We then present the model structure and solution methods, followed by the results section covering Pareto analysis, economies of scale effects, and exceedance probabilities.We found that as demand increases, traditional manufacturing technologies become more cost effective due to economies of scale, while additive manufacturing remains competitive for smaller volumes and tighter time constraints. Additionally, the Monte Carlo analysis revealed that uncertainty in costs and processing times significantly affects the selection of optimal production strategies Finally, we conclude by discussing practical implications for manufacturers and directions for future research.