Stochastic & Multi-Objective Optimization Models
Multi-stage and multi-objective optimization models for complex resource allocation and scheduling under uncertainty.
Applications include healthcare scheduling, project team formation, and resource allocation in dynamic environments.
Approach
This work develops:
- Multi-stage stochastic programming formulations
- Multi-objective optimization models
- Simulation–optimization frameworks
- Workforce and team formation decision models
The emphasis is on modeling complexity faithfully while maintaining computational tractability.
Impact
These models demonstrate how structured optimization can support decision-making in complex service and project-based systems.
They extend optimization theory into practical, multi-objective environments with real uncertainty.
Selected Publications
- Rahmanniyay, Yu & Seif (2019), Computers & Industrial Engineering
- Dehghanimohammadabadi et al. (2022), SIMULATION