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Space Logistics Modeling and Optimization: Review of State of the Art

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arxiv 2306.01107 v8 pith:E43UZTFL submitted 2023-06-01 math.OC

classification math.OC
keywords in-spacelogisticsspaceastrodynamicslogistics-drivenmobilitymodelingmultiple
verification ladder T0 review T1 audit T2 compute T3 formal
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As "Space Mobility and Logistics" was listed as one of the five core competencies in the US Space Force's doctrine document, there is a growing interest in developing technologies to enable in-space refueling, servicing, assembly, and manufacturing as well as other in-space logistics operations. Modeling for space mobility and logistics requires a new approach that differs from conventional astrodynamics because it needs to consider the coordination of multiple vehicles to satisfy an overall demand; namely, the optimal trajectory of one vehicle does not necessarily lead to the optimal campaign solution that contains multiple vehicles and infrastructure elements. In addition, for in-space servicing applications, we need additional analysis capabilities to analyze and optimize the sizes of the fuel/spare depots and their inventory/sparing policies with orbital mechanics in mind. To tackle these challenges, there have been various attempts to leverage terrestrial logistics-driven techniques, coupled with astrodynamics, to enhance in-space operations. This paper aims to provide a review of the literature by categorizing the state-of-the-art studies in two ways: (1) by application questions that are addressed; and (2) by logistics-driven methods that are used in the studies. The two categorizations are expected to help both practitioners and researchers understand the state of the art and identify the under-explored and promising future research directions.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Heuristic Bounded Prime Gaps via a Chaotic Multidimensional Sieve and Random Matrix Theory

    math.NT 2025-07 reject novelty 4.0 of 10

    The paper proposes heuristic prime-gap bounds of 180 and 8 by adding chaos and random matrix perturbations to Maynard's sieve, with no proof that the perturbations help.

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