A Unified System Framework for Hybrid Allocation Optimization and Monte Carlo Reliability Simulation in the Logistics

Abstract

This paper constructs a unified optimization modeling framework for Earth-Moon cargo transportation and proposes a method that integrates physical mechanism modeling with multi-source transportation coordination and allocation. By establishing a hybrid transportation model that combines elevators and rockets, and introducing an allocation algorithm based on the superposition of throughput capacities, the framework achieves the coordinated optimization of transportation time and cost. Building on this foundation, reliability modeling is incorporated, transforming equipment availability, operational efficiency, and failure probability into a utilization function, while a multi-level stochastic perturbation structure is used to characterize system fluctuations. To analyze the impact of uncertainty, discrete-time Monte Carlo simulation is employed to dynamically evaluate the transport process, enabling the quantitative characterization of delay risks. Concurrently, a capacity margin constraint model is constructed, and key influencing factors are identified using surrogate functions. At the multi-objective level, a coupled model of transportation allocation and emissions is introduced, and environmental impact assessments are conducted through stochastic simulation. Finally, a unified allocation strategy that integrates time, reliability, and environmental constraints is proposed. This strategy offers excellent scalability and general applicability, providing methodological support for resource allocation and risk control in complex transportation systems.