A Submodular Framework for Multi-Dimensional Resource Optimization: Applications in Chemical Production and Traditional Chinese Medicine

Abstract

This paper synthesizes recent advances in resource optimization, system resilience, and intelligent response mechanisms across chemical engineering and healthcare domains. Building upon heterogeneous resource slot optimization frameworks and machine learning-based logistics networks, this study proposes an integrated approach to multi-dimensional resource allocation with cross-space spillover effects. The methodology incorporates predictive multi-dimensional safeguards for system stability, knowledge graph-based intelligent response systems, and multi-objective optimization algorithms for process parameter regulation. Empirical validation draws from case studies in transnational electrolyte production facilities and standardized traditional Chinese medicine external therapy protocols. Results demonstrate significant improvements in resource utilization efficiency, system resilience under concurrent operations, and process parameter coupling regulation. The findings contribute to both theoretical frameworks in submodular constrained optimization and practical applications in industrial and healthcare settings.