A Hybrid Simulation-Optimization Framework for Pandemic and Crisis Response: A Reconfigurable Systems Approach

A Simulation-Optimization (SimOpt) framework reconfigures healthcare resources during pandemic crises via an analogy with Reconfigurable Manufacturing Systems (RMS). The study formally establishes equivalence between Reconfigurable Manufacturing Systems RMS reconfigurability and healthcare adaptability, then develops a Mixed-Integer Linear Programming MILP model optimized through Discrete Event Simulation. The methodology includes: (1) a conceptual mapping of six RMS characteristics to healthcare, (2) a validated DES model of an emergency department under crisis, and (3) a MILP model with 267 variables and 431 constraints, coupled via queueing-theory bounds. The framework achieves a 35.0% reduction in overall patient waiting times and a 44.3% improvement for critical patients, moving only 11 resource units across 6 transfers. All six RMS characteristics map directly onto quantifiable healthcare attributes, confirming operational validity. This is among the first studies to formally apply the RMS framework to healthcare operations using a validated SimOpt architecture. The work integrates industrial engineering, operations research, and healthcare management science, offering a practical tool for hospital crisis resource planning.