Research on modeling and algorithm of resource allocation optimization based on multi-factor constraints

Gonghao Li

doi:10.54097/9bwffj64

Authors

Gonghao Li

DOI:

https://doi.org/10.54097/9bwffj64

Keywords:

Data-driven, Optimal Resource Scheduling, Linear Regression, Logistic-Growth Models, Multi-Objective Planning.

Abstract

As resource constraints and environmental pressures intensify, optimising systems under these constraints has become important in many fields, e.g. agriculture, energy scheduling and manufacturing. This requires balancing land, water, energy and so on. While existing optimisation methods address some single-constraint problems, they are poor at responding to dynamic changes in complex, multi-constraint systems due to poor adaptability and efficiency.In this paper, we propose a forecasting-scheduling modelling approach that identifies influencing factors using regression and growth models, and simulates system responses to resource constraints. It then combines mathematical scheduling methods for optimal resource allocation.Experimental findings show this framework can efficiently optimise resource allocation under these constraints, especially in constrained environments, with strong adaptability and stability.The growth model effectively reflects non-linear behaviour and the scheduling model significantly improves system efficiency, while respecting constraints.This study provides scientific decision support for scheduling resources, and has potential for industry-wide applications, and for real industrial and social systems.

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