Publications

This paper revisits the classical fuel-optimal two-impulse rendezvous problem between Keplerian orbits from a family-based perspective. The framework applies selected first-order optimality conditions and numerical continuation to connect isolated optima into continuous one-parameter families. These families are classified using Hessian-based criteria and Primer Vector Theory and projected onto porkchop plots. Case studies show how locally optimal branches emerge, merge, and disappear with orbital geometry, providing a global map of solution robustness and near-optimal alternatives.

This paper proposes an on-orbit servicing (OOS)-integrated maintenance strategy for satellite constellations that combines new satellite deployment with OOS-based recovery of failed satellites. An inventory management model with a parametric replenishment policy is developed to evaluate maintenance performance and formulate the operator-provider interaction as a bi-objective optimization problem: the operator minimizes cost while maintaining service levels, whereas the OOS provider maximizes profit through pricing and performance decisions. A real-world-scale case study shows that the Pareto frontier is driven primarily by provider pricing, while the operator’s maintenance strategy remains nearly invariant. Among OOS-related parameters, the recoverable failure fraction has the strongest structural effect.

This paper addresses satellite system architecture selection under commercial on-orbit refueling (OOR), focusing on two design parameters: design lifetime and initial propellant loading. The problem is formulated as a mathematical optimization, with design lifetime and initial propellant mass as design variables and two objectives balancing monetary return and risk. To solve it, we develop a surrogate-model-based framework grounded in satellite lifecycle simulation, incorporating uncertainty, operational flexibility, and modified sizing and cost models for OOR-enabled systems. A GEO communication satellite case study evaluates current service-performance targets and explores alternative architectures beyond traditional design trends.

This paper introduces a mixed-integer linear programming (MILP) approach to modular architecture design based on the design structure matrix (DSM). The DSM modularization problem seeks to maximize modularity, quantified by the (Q)-metric, while satisfying constraints that reflect practical considerations across the system life cycle. The proposed formulation is verified through two case studies involving automobile and electric train subsystems, demonstrating its effectiveness and practical applicability.

This paper proposes a replenishment strategy for satellite mega-constellations using primary and auxiliary supply modes. The primary mode uses an indirect channel, placing spare satellites in a parking orbit before transferring them to target orbital planes via onboard propulsion, whereas the auxiliary mode directly injects spares into their designated planes. An inventory management model with time-window-based parametric replenishment policies is developed, and optimization problems are formulated from the perspectives of the constellation operator and launch service provider. A case study shows that the effectiveness of the direct channel is context-dependent and reveals the operational tension between the small-launch provider and the mega-constellation operator.

Conceptual design of launch vehicle involves multiple disciplines, including structures, aerodynamics, propulsion, flight control, and stage sizing. This paper focuses on the integrated optimization of trajectory and stage sizing. Conventional approaches often treat these tasks sequentially, which can increase computational complexity and yield suboptimal designs. The proposed framework minimizes lift-off mass while satisfying flight requirements, including limits on maximum dynamic pressure and impact points of separated parts. A case study demonstrates the advantage of the integrated framework over the conventional sequential optimization approach.

Space situational awareness (SSA) capabilities are becoming increasingly critical as access to space expands and space activities intensify. SSA supports operations, planning, and strategic and political decision-making worldwide. This paper introduces the baseline model and implementation details of the developed software. It also presents the results and implications of numerical studies focused on low Earth orbit. These studies analyze the effects of key input parameters and investigate the evolution of the space collision environment, providing insights for parameter selection and demonstrating the applicability of the developed software.

This paper presents an actionable human resource (HR) decision-support framework for small firms and startups based on a multi-domain matrix (MDM). The framework addresses complex interdependencies among members, skills, and projects by formulating startup HR management as a multi-domain structural modeling problem. It provides qualitative guidelines and quantitative metrics for diagnosing HR states and supporting decisions on workload redistribution, hiring, and capability development. A case study of an early-stage technology startup demonstrates that the framework identifies workload imbalances, detects unsustainable member workloads, and informs hiring decisions while supporting continuous HR diagnosis after organizational changes.

This paper introduces an optimal clustering method for a design structure matrix (DSM) with constraints selection. In DSM clustering problems, constraints may conflict with one another, making it impossible to obtain a feasible solution, as illustrated in the figure. The proposed approach addresses such cases by simultaneously determining an optimal modular design that maximizes the modularity metric, (Q), while minimizing constraint violations according to their priorities and weights. A case study of an automobile suspension system demonstrates the effectiveness of the method in achieving high-quality clustering while resolving constraint conflicts.