Multi-objective optimization modeling and risk-resilient robustness assessment for large-scale extraterrestrial material transfer logistics systems

Abstract

Addressing the massive interplanetary logistics challenge of transporting 100 million tons of materials for lunar colonization, this study proposes a computational framework integrating deterministic optimization strategies with stochastic risk assessment. The research first focuses on the deep trade-off between transportation efficiency and economic costs. Using a logistic regression model to predict launch frequencies at global bases by 2050, combined with operational cost quantification based on fuel compensation patterns derived from geographical latitude variations, a multi-objective pure integer programming model is constructed. Through algorithmic optimization, the study confirms that under various weighting configurations, the hybrid transport scheme combining space elevators and ground launch bases consistently achieves optimal overall performance. Subsequently, to address potential systemic disturbances in real space engineering, this paper introduces random variables to model extreme scenarios such as space elevator failures, rocket launch failures, and cable oscillations. Using Monte Carlo simulation, the study quantified the disturbance patterns of risk events on total project duration and cost based on tens of thousands of random trials, providing a system performance prediction range at a 95% confidence level. The results reveal the core role of space elevators in cost reduction while quantifying performance degradation caused by accident risks. This research establishes a scientific decision-making paradigm for large-scale planetary logistics missions, significantly enhancing the reliability of future space transportation chains.