Optimizing Medical Kits for Spaceflight

Keenan, A. B,; Foy, Millennia; Myers, G.

Home/Archive/NASA NTRS

NASA NTRS · 20140008547

Optimizing Medical Kits for Spaceflight

Keenan, A. B,·Foy, Millennia·Myers, G.

View original source ↗Export metadata ↓

Abstract

The Integrated Medical Model (IMM) is a probabilistic model that estimates medical event occurrences and mission outcomes for different mission profiles. IMM simulation outcomes describing the impact of medical events on the mission may be used to optimize the allocation of resources in medical kits. Efficient allocation of medical resources, subject to certain mass and volume constraints, is crucial to ensuring the best outcomes of in-flight medical events. We implement a new approach to this medical kit optimization problem. METHODS We frame medical kit optimization as a modified knapsack problem and implement an algorithm utilizing a dynamic programming technique. Using this algorithm, optimized medical kits were generated for 3 different mission scenarios with the goal of minimizing the probability of evacuation and maximizing the Crew Health Index (CHI) for each mission subject to mass and volume constraints. Simulation outcomes using these kits were also compared to outcomes using kits optimized..RESULTS The optimized medical kits generated by the algorithm described here resulted in predicted mission outcomes more closely approached the unlimited-resource scenario for Crew Health Index (CHI) than the implementation in under all optimization priorities. Furthermore, the approach described here improves upon in reducing evacuation when the optimization priority is minimizing the probability of evacuation. CONCLUSIONS This algorithm provides an efficient, effective means to objectively allocate medical resources for spaceflight missions using the Integrated Medical Model.

Explore related subjects

Optimization Resource Utilization Monte Carlo Method

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Keenan, A. B,, Foy, Millennia, Myers, G.. 2014-02-12. Optimizing Medical Kits for Spaceflight. https://ntrs.nasa.gov/citations/20140008547

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

A Linear Programming Approach to Backtracking for Single-Axis Trackers on Rolling Terrain

In this article, we present a computationally efficient method for determining optimal backtracking rotations for single-axis solar trackers on nonuniform terrain. The method allows for ganged tracking, mechanical rotation constraints, uneven row spacing, and arbitrary maximum allowable shaded fractions (to enable “fractional backtracking”). As with previous 2-D approaches, the method is suitable for terrain that varies in the transverse direction with respect to the rotation axis of the trackers. The novelty of the method lies in formulating the problem of shade avoidance as a linear problem, which is achieved by using the row interception width as the optimization variable instead of rotation angles. Formulating backtracking as a linear problem enables the use of extremely efficient linear programming algorithms, making the method highly scalable, requiring less than 1 min to compute optimal rotation schedules for hundreds of trackers. It also produces more effective backtracking rotations, reducing the frequency of shading by 4× and improving system energy output by 1%–2%.

Optimization↗

NASA NTRS2024

Implementation of a First-Order Quadratic Program Solver in C

This paper details a translation of a first order quadratic program (QP) solver from MATLAB to C. NASA could use this QP solver to generate online flight path trajectories for powered descent vehicles during landing. Over 12 weeks, the team designed, implemented, and tested two iterations of the QP solver for accuracy and runtime on 104 benchmark QP tests. The final iteration was 541.07% faster than the first, handling most tests in under one second. Additionally, it solved four more QP tests for N≥1383, and all outputs for cost and D_x matched the MATLAB reference values.

Optimization↗

NASA NTRS2024

A Convexification-Based Outer-Approximation Method for Convex and Nonconvex MINLP

The advancement of domain reduction techniques has significantly enhanced the performance of solvers in mathematical programming. This paper delves into the impact of integrating convexification and domain reduction techniques within the Outer-Approximation method. We propose a refined convexification-based Outer-Approximation method alongside a Branch-and-Bound method for both convex and nonconvex Mixed-Integer Nonlinear Programming problems. These methods have been developed and incorporated into the open-source Mixed-Integer Nonlinear Decomposition Toolbox for Pyomo-MindtPy. Comprehensive benchmark tests were conducted, validating the effectiveness and reliability of our proposed algorithms. These tests highlight the improvements achieved by incorporating convexification and domain reduction techniques into the Outer-Approximation and Branch-and-Bound methods.

Optimization↗