Resource Optimisation Study Based on Multi-objective Particle Swarm Optimisation and Monte Carlo Simulation

This study focuses on the application of multi-objective optimisation algorithms in complex environments, aiming at algorithmically solving resource utilisation and revenue optimisation problems. For resource allocation under specific constraints, we adopt a linear programming framework to model the problem, focusing on exploring the dynamic relationship between output, cost and market price. To this end, this paper proposes a solution based on a multi-objective particle swarm optimisation (PSO) algorithm and uses the Matlab platform to solve the linear programming model and generate the optimal decision-making strategy for the period 2024-2030. To cope with parameter uncertainty, we further combine conditional value-at-risk (CVaR) and robust optimisation methods to ensure that the optimisation model is adaptive and robust under uncertainty. The performance of the algorithm in dealing with uncertain scenarios is enhanced by Monte Carlo simulation, which improves the model's responsiveness to market fluctuations. In addition, the paper analyses the interactions between different parameters (e.g., production, pricing, cost structure) through Pearson's correlation coefficient, which provides a scientific basis for the constraints in the model. This algorithm-driven optimisation-based approach demonstrates the potential for a wide range of applications in the fields of resource allocation, supply chain management, etc., and exhibits significant advantages in terms of computational efficiency and resilience.