Machine & Infrastructure Replacement Decisions
Stochastic and multi-stage optimization models for machinery and capital asset replacement under uncertainty.
Problem Space
Replacement decisions for capital-intensive assets involve long-term uncertainty, economic tradeoffs, and operational risk. Traditional replacement models often assume fixed planning horizons or deterministic conditions, which rarely reflect real-world environments.
The core challenge is deciding when to replace machinery while accounting for uncertainty in demand, cost, and operational conditions.
Approach
This research develops:
- Multi-stage stochastic programming models
- Replacement models under horizon uncertainty
- Integrated models that include shipping and logistical considerations
- Life-cycle cost analysis frameworks
The models explicitly incorporate uncertainty rather than treating it as an afterthought.
Impact
These frameworks improve long-term capital allocation and reduce risk exposure in industries such as construction, manufacturing, and infrastructure management.
They shift replacement analysis from static cost comparison to dynamic system-level optimization.
Selected Publications
- Seif, Shields & Yu (2019), The Engineering Economist
- Shields, Seif & Yu (2019), International Journal of Production Economics
- Seif & Rabbani (2014), Journal of Quality in Maintenance Engineering