Abstract
The photovoltaic power generation industry operates in a strong competitive market where even marginal efficiency losses can translate into substantial profit margins. Sustaining optimal performance is imperative to meet expected revenue levels, requiring the implementation of monitoring methods to evaluate the efficiency of the system. In this study, a business intelligence dashboard was developed to address these challenges. The tool focuses on a detailed analysis of data, providing valuable insights into system performance, not only by adding comprehensive data analysis but also facilitating the decision-making process. In doing so, the risk of substantial expenses on equipment repairs is mitigated, ensuring efficient and cost-effective operation. The implemented tool collectively contributes to the maintenance and regulation of equipment performance, offering a holistic approach to performance monitoring in photovoltaic power generation systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
This paper presents an effective Dual-RAMP algorithm for solving the Capacitated Single Allocation p-Hub Location Problem (CSApHLP). This problem aims to determine the set of p hubs in a network that minimizes the total cost of allocating all the non-hub nodes to the p hubs. The algorithm effectively explores the primal-dual relationship, combining adaptive memory concepts and metaheuristic techniques with principles of mathematical relaxation under the Relaxation Adaptive Memory Programming (RAMP) framework, covering the dual and primal solution spaces. The proposed algorithm incorporates Lagrangean Relaxation and Subgradient Optimization in the dual side and a simple Improvement Method on the primal side. The results' quality on a standard testbed shows that the RAMP approach is a very effective approach for the CSApHLP.

Abstract
Facility Location Problems are widely studied problems in the literature with several practical applications, reaching areas such as telecommunications, design of a supply chain management, transport utilities and water distribution networks. In this paper, we address the Capacitated Facility Location Problems (CFLP), whose general goal is to determine where to locate a set of facilities to serve a particular set of customers with minimum cost. The CFLP problem has been widely studied for the past decades with the development of exact and heuristics methods. We propose a new heuristic algorithm for the Capacitated Facility Location Problem (CFLP) based on the RAMP (Relaxation Adaptive Memory Programming) framework. In the dual side of the method, the RAMP framework uses a Dual-Ascent procedure and a simple improvement method based on Tabu Search was used to explore the primal side, making this algorithm a very robust RAMP approach. The RAMP algorithm for the CFLP obtained excellent results, demonstrating its potential for new applications to other extensions and variations of Facility Location Problems. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The Hub Location Problems (HLP) have gathered great interest due to the complexity and to the many applications in industry such as aviation, public transportation, telecommunications, among others. The HLP have many variants regarding allocation (single or multiple) and capacity (uncapacitated or capacitated). This paper presents a variant of the HLP, encompassing single allocation with capacity constraints. The Capacitated Single Allocation p-Hub Location Problem (CSApHLP) objective consists on determine the set of p hubs in a network that minimizes the total cost of allocating all the non-hub nodes to the p hubs. In this work, it is proposed a sophisticated RAMP approach (PD-RAMP) to improve the results obtained previously by the simple version (Dual-RAMP). Thus, a parallel implementation is conducted to assess the effectiveness of a parallel RAMP model applied to the CSApHLP. The first algorithm, the sequential PD-RAMP, incorporates Dual-RAMP with a Scatter Search procedure to create a Primal-Dual RAMP approach. The second algorithm, the parallel PD-RAMP, also take advantage of the dual and primal, parallelizing the primal side of the problem and interconnecting both sides as it is expected in the RAMP sequential algorithm. The quality of the results carried out on a standard testbed shows that the PD-RAMP approach managed to improve the state-of-the-art algorithms for the CSApHLP.

Abstract
Facility Location Problems (FLP) are complex combinatorial optimization problems whose general goal is to locate a set of facilities that serve a particular set of customers with minimum cost. Being NP-Hard problems, using exact methods to solve large instances of these problems can be seriously compromised by the high computational times required to obtain the optimal solution. To overcome this difficulty, a significant number of heuristic algorithms of various types have been proposed with the aim of finding good quality solutions in reasonable computational times. We propose a Scatter Search approach to solve effectively the Uncapacitated Facility Location Problem (UFLP). The algorithm was tested on the standard testbed for the UFLP obtained state-of-the-art results. Comparisons with current best-performing algorithms for the UFLP show that our algorithm exhibits excellent performance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
In this paper, we consider three Relaxation Adaptive Memory Programming (RAMP) approaches for solving the Uncapacitated Facility Location Problem (UFLP), whose objective is to locate a set of facilities and allocate these facilities to all clients at minimum cost. Different levels of sophistication were implemented to measure the performance of the RAMP approach. In the simpler level, (Dual-) RAMP explores more intensively the dual side of the problem, incorporating a Lagrangean Relaxation and Subgradient Optimization with a simple Improvement Method on the primal side. In the most sophisticated level, RAMP combines a Dual-Ascent procedure on the dual side with a Scatter Search (SS) procedure on primal side, forming the Primal-Dual RAMP (PD-RAMP). The Dual-RAMP algorithm starts with (dual side) the dualization of the initial problem, and then a projection method projects the dual solutions into the primal solutions space. Next, (primal side) the projected solutions are improved through an improvement method. In the PD-RAMP algorithm, the SS procedure is incorporated in the primal side to carry out a more intensive exploration. The algorithm alternates between the dual and the primal side until a fixed number of iterations is achieved. Computational experiments on a standard testbed for the UFLP were conducted to assess the performance of all the RAMP algorithms.