372. Multi-Objective Optimization Model for Strategic Acquisition of Additive Manufacturing Technologies
Invited abstract in session WA-38: Industrial Optimization, stream Data Science meets Optimization.
Wednesday, 8:30-10:00Room: Michael Sadler LG19
Authors (first author is the speaker)
| 1. | Federica Tomelleri
|
| 2. | Matteo Brunelli
|
| University of Trento |
Abstract
The rapid evolution of Additive Manufacturing (AM) technologies presents new opportunities to optimize production processes. However, selecting and acquiring suitable AM machines is a complex challenge due to the wide variety of specialized, high-cost equipment available, each designed for different applications such as prototypes, functional components, prosthetics, and tools. Efficiently planning these investments to support large-scale production requires robust decision-making under uncertainty. This paper proposes a stochastic optimization model to guide the acquisition and utilization of AM technologies across multiple planning periods. The model employs a multi-objective approach that balances two key goals: maximizing the probability of meeting uncertain demand forecasts and minimizing acquisition costs. To address computational challenges, chance constraints are reformulated into deterministic equivalents, enabling effective integration of stochastic data with machine capacity and production planning decisions. The solution framework leverages mixed-integer nonlinear programming techniques combined with heuristic optimization, specifically genetic algorithms, to identify Pareto-optimal solutions. By reflecting real-world complexities, this research demonstrates the importance of combinatorial optimization in achieving both cost-efficiency and service-level reliability for strategic investments in AM facilities.
Keywords
- Multi-Objective Decision Making
- Stochastic Optimization
- Combinatorial Optimization
Status: accepted
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