Shayan Farhang Pazhooh

arXiv2026

From Operations to Elderly Care Outcomes: A Thematic Review of Industrial Engineering and Decision-Support Approaches

The rapid growth of the global aging population presents severe challenges to healthcare systems, necessitating efficient, equitable, and patient-centered care models. While Industrial Engineering and Operations Research (OR) provide robust optimization and decision-support tools to address these multidimensional complexities, current applications often remain fragmented. This paper presents a thematic review of 30 seminal studies at the intersection of OR and elderly care, categorizing the literature into home healthcare operations, polypharmacy management, and clinical chronotherapy. Our analysis highlights a significant methodological evolution from static, deterministic models toward dynamic and stochastic frameworks integrated with artificial intelligence (AI). Despite these advancements, a critical translational gap persists: the current OR literature is heavily dominated by process-level optimizations, such as staff routing, and struggles to translate these operational efficiencies into measurable clinical outcomes. Furthermore, holistic models bridging the transition between hospital and community care remain critically underexplored. To develop resilient and smart healthcare systems, this study proposes a conceptual framework that shifts the research focus from isolated operational tasks to integrated, multi-level decision-making. We emphasize the critical need for robust systems analysis, human-inclusive design, and the smartification of care through emerging digital technologies - including digital twins and large language models - to successfully bridge the gap between theoretical operational metrics and tangible patient-level health outcomes.

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arXiv2025

An Efficient Continuous-Time MILP for Integrated Aircraft Hangar Scheduling and Layout

Efficient management of aircraft MRO hangars requires the integration of spatial layout with time-continuous scheduling to minimize operational costs. We propose a continuous-time mixed-integer linear program that jointly optimizes aircraft placement and timing, overcoming the scalability limits of prior formulations. A comprehensive study benchmarks the model against a constructive heuristic, probes large-scale performance, and quantifies its sensitivity to temporal congestion. The model achieves orders-of-magnitude speedups on benchmarks from the literature, solving a long-standing congested instance in 0.11 seconds, and finds proven optimal solutions for instances with up to 40 aircraft. Within a one-hour limit for large-scale problems, the model finds solutions with small optimality gaps for instances up to 80 aircraft and provides strong bounds for problems with up to 160 aircraft. Optimized plans consistently increase hangar throughput (e.g., +33% serviced aircraft vs. a heuristic on instance RND-N030-I03), leading to lower delay penalties and higher asset utilization. These findings establish that exact optimization has become computationally viable for large-scale hangar planning, providing a validated tool that balances solution quality and computation time for strategic and operational decisions.

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