Hyunwoo Shin

The rapidly evolving landscape of lung allocation policy, including the recent adoption of the Continuous Distribution framework, demands simulation tools that are both flexible and computationally efficient. Existing lung allocation simulators are often difficult to adapt to new policy designs and can be computationally burdensome when large numbers of replications are needed for uncertainty quantification and optimization. This paper introduces the Modular Lung Allocation Policy Simulation Model (MoLAPS), a high-fidelity, discrete-event simulation framework designed to address these limitations through a modular policy architecture and parallel execution. MoLAPS integrates detailed patient and donor data, dynamic medical status updates, and a predictive organ-offer acceptance model. It is implemented with parallel computing support, substantially reducing runtimes for multi-replication and longer-horizon experiments and enabling the exploration of large-scale policy design spaces. The model features a modular architecture that isolates policy components, including allocation, acceptance, and prioritization rules, allowing policymakers to easily evaluate existing and proposed allocation policies, or hypothetical alternatives. A surrogate-based calibration procedure aligns simulation outputs with national organ transplant data, improving empirical validity. MoLAPS is rigorously validated using prediction intervals, quantile-quantile plots, and Chi-squared tests. Simulation results closely align with real-world data across key performance indicators, including transplant rates, waitlist mortality, and organ nonuse. MoLAPS provides clinicians, policymakers, and researchers with a timely, open, and extensible platform for policy evaluation. By enabling realistic, data-driven assessments of lung allocation strategies, it supports more equitable, efficient, and evidence-based transplant policy decisions.

Path planning for mobile agents is one of the areas that has drawn the attention of researchers’, as evidenced in the large number of papers related to the collision-free path planning (CFPP) algorithm. The purpose of this paper is to review the findings of those CFPP papers and the methodologies used to generate possible solutions for CFPP for mobile agents. This survey shows that the previous CFPP papers can be divided based on four characteristics. The performance of each method primarily used to solve CFPP in previous research is evaluated and compared. Several methods are implemented and tested in same computing environment to compare the performance of generating solution in specified spatial environment with different obstacles or size. The strengths and weakness of each methodology for CFPP are shown through this survey. Ideally, this paper will provide reference for new future research.

This paper suggests a novel methodology in collision-free shortest path planning (CFSPP) problems for mobile agents (MAs) using a method that combines a genetic algorithm (GA) and a direction factor toward a target point. In the CFSPP problem, MAs find the shortest path from the starting point to the target point while avoiding certain obstacles. The paper proposes an obstacle-based search methodology that identifies critical collision-free points adjacent to given obstacles. When critical obstacles are found via CFSPP, this study suggests favorable paths in 2-dimensional space found using the obstacle-based GA (OBGA). The OBGA has four advantages. First, it effectively narrows the search spaces compared to free space-based methodologies. It also determines shorter collision-free paths, and it only requires a short amount of time. Finally, convergence occurs more quickly than in previous studies. The proposed method also works properly in larger and more complex environments, indicating that it can be applied to more practical problems.