Abstract
Resource Planning Optimization (RPO) is a common task that many companies need to face to get several benefits, like budget improvements and run-time analyses. However, even if it is often solved by using several software products and tools, the great success and validity of the Artificial Intelligence-based approaches, in many research fields, represent a huge opportunity to explore alternative solutions for solving optimization problems. To this purpose, the following paper aims to investigate the use of multiple Artificial Neural Networks (ANNs) for solving a RPO problem related to the scheduling of different Combined Heat & Power (CHP) generators. The experimental results, carried out by using data extracted by considering a real Microgrid system, have confirmed the effectiveness of the proposed approach.
Highlights
Resource Planning Optimization (RPO) is a frequent task that companies may face to get many benefits, like budget improvements, run-time analyses, and human resource organizations (Halima 2017)
An optimization problem related to the scheduling of different Combined Heat & Power (CHP) generators in a real Microgrid system is presented
It can be stated as a Mixed-Integer Linear Programming (MILP) model characterized by a minimum cost function J and subject to several constraints typologies to fulfil, such as interactive, operative, and physical
Summary
Resource Planning Optimization (RPO) is a frequent task that companies may face to get many benefits, like budget improvements, run-time analyses, and human resource organizations (Halima 2017). The related MILP formulations have been solved through different proposed approaches, like Simulated-Annealing (SA) and Iterated Greedy (IG), and compared to the classical MILP solvers with a threshold time of 1 hour In both cases, due to the complex nature of the considered problems (NP-Hard), the comparisons have shown the difficulty of classical MILP solvers in solving small data instances in a short time by becoming computationally inefficient and providing worse solutions in comparison with those derived by the proposed approaches. Artificial Neural Networks (ANNs) represent one of the most famous AI approaches that have been employed in several research areas, like robotics (Li et al 2019) and computer vision (Kanuri et al 2018), to investigate their effectiveness in solving multi-labels multi-classes problems as classification tasks For this reason, the goal of the following proposal is to investigate the use of multiple ANNs as an alternative approach for solving a RPO problem.
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