Multi-objective Optimization Strategy Model Based on Simulated Annealing

This paper proposes a multi-objective optimization model based on simulated annealing for sensitivity analysis of decision variables and time-series forecasting. The model is constructed with dynamic optimization conditions, using constraint solving and progressive algorithms to determine the optimal paths for variables. Integrating risk assessment and cross-elasticity simulation, the study presents a multi-objective approach to address complex market environments. Experimental results indicate that the model effectively balances demand and supply fluctuations across various simulation scenarios. To further refine the strategy, this paper analyzes the algorithm’s adaptability under multiple constraints and provides robust improvement recommendations suitable for complex scenarios.