Optimization Strategies in Planned Economy Models: Enhancing Global Profit through Quantitative Forecasting

In this article, we introduce a decentralization algorithm that advances in stages, with the central authority setting quantitative production targets for decentralized units. The decentralized units aim to maximize profits by aligning their production with these targets, incentivized by a bonus system where rewards increase as deviations from the central objectives are minimized. The algorithm's goals include enhancing system efficiency, promoting collaboration through rewards, and enabling adaptive goal setting. The iterative nature of the process allows the central authority to refine its understanding of each unit's technical capabilities, setting more precise production targets over time. The algorithm ensures convergence towards an optimal solution that balances the objectives of both the central authority and the decentralized units. Key results show that the algorithm is monotonic, meaning each iteration progressively moves closer to the optimal solution without regression. This improves decision-making by the central authority as it gains insights into each unit's capabilities. The study also highlights the practicality of the algorithm, showing that decentralized units are motivated to adhere to the rules set by the center, leading to a well-coordinated and optimized production system.