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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

Publications