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Hybrid simulation of circular supply chains in healthcare
US & West ·
Researchers applied a hybrid discrete-event and agent-based simulation to analyze the transition to circular economy in healthcare supply chains. Using laparoscopic scissors as a case study, they assessed the impact of introducing reusable products on individual supply chain members and the entire system. The work accounts for uncertainties in flows and actor behavior, which was not done before.
Why it matters
Shows how simulation helps assess the consequences of business model changes in complex supply chains, relevant for resource management in large-scale systems.
A nonlinear adaptive predictive control method (NPCAC) is proposed that identifies a pseudo-linear model online from input-output data without prior training. It combines recursive least squares with information forgetting and iterative MPC, tested with polynomial, Fourier, and spline basis functions. The approach handles systems with high uncertainty.
A GPU-based solver for trajectory planning problems using branch model predictive control is presented. The solver is based on iterative LQR, multiple-shooting formulation, and augmented Lagrangian method for constraint handling. Numerical experiments show superiority over a CPU-based solver on large-scale problems.
The integration of shared autonomous vehicles (SAVs) into microtransit systems, which address the last-mile problem, is investigated. The Atlanta case study shows that SAVs can improve sustainability, convenience, and reliability compared to conventional fixed-route transit.
