The increasing interest in renewable energy sources has brought attention to large voltage-gain dc–dc converters; among the different available solutions to perform a large voltage-gain conversion, this article presents an overview of non-isolated dc–dc converter topologies that utilize switched-capacitor circuits, i.e., diode-capacitors voltage multipliers. The review includes combinations of a traditional power stage with a diode-capacitor-based voltage multiplier, such as the multilevel boost converter. This article starts by reviewing switched-capacitor (SC) circuits, different topologies, and different types of charge exchange; it provides a straightforward analysis to understand the discharging losses. It then covers the multilevel boost converter and other topologies recently introduced to the state-of-the-art. Special attention is put on SC circuits with resonant charge interchange that have recently been probed to achieve very good efficiency. An additional contribution of the article is new proof of the discharging losses in resonant switched-capacitor circuits focused on the initial and final stored energy in capacitors, and this proof explains the relatively large efficiency obtained with SC resonant converters.

The construction industry performs many tasks scheduled and related to other activities. Companies must optimize their operations, increase efficiency, eliminate waste, and deliver better products to their customers. As a result, this study aims to identify the main challenges associated with the implementation of the Lean Construction model in small and medium-sized construction companies and optimize the implementation of this process using statistically-focused mathematical models. This study was conducted using the partial least squares (PLS-SEM) method and also carried out the principal component analysis to optimize Lean barriers so that they can be properly implemented in the construction industry. The most important obstacles are displayed, as well as the relationships with other factors. Significant relationships have been discovered between the barriers to Lean construction adoption, especially with regard to corporate culture, communication, training, leadership, and the influence of mentality on business and employee adaptability. Construction executives and managers can make well-informed policy and strategic decisions by having a thorough understanding of the main barriers to Lean implementation. This information enables them to focus on the implementation of Lean technologies in projects, to increase market competitiveness, reduce waste and enhance overall work efficiency.

The integration of renewable energy into the grid demands the development of increasingly more sophisticated power electronics configurations. This situation has motivated the current boom of new power converter topologies. Even though most of the recently proposed converters are scattered across the literature, some of them can be classified in a taxonomical fashion. This action permits studying their properties, generalizing principles, and generating new contributions with improved features—these are the general aims and overall research direction reported in this paper. In this work, we are devoted to generating a well-defined corpus of knowledge by characterizing an emerging family of DC-DC double dual converters. We show that the underlying principle of dualization can be fully enclosed within a systematic procedure and applied not only to classical DC-DC converters but also to any modern configuration. This contribution thus permits the diversification of new topologies that hold relevant features, such as low common-mode voltages and currents, high-voltage gains, and efficient harmonic mitigation—among other advantages that are oriented to renewable energy management. We thus demonstrate that systematic dualization leads to the development of new designs with enhanced features. Experimental results of new topologies are also presented to corroborate the proposed principles and advantages.

Supplier selection and order quantity allocation have a strong influence on a company’s profitability and the total cost of finished products. From an optimization perspective, the processes of selecting the right suppliers and allocating orders are modeled through a cost function that considers different elements, such as the price of raw materials, ordering costs, and holding costs. Obtaining the optimal solution for these models represents a complex problem due to their discontinuity, non-linearity, and high multi-modality. Under such conditions, it is not possible to use classical optimization methods. On the other hand, metaheuristic schemes have been extensively employed as alternative optimization techniques to solve difficult problems. Among the metaheuristic computation algorithms, the Grey Wolf Optimization (GWO) algorithm corresponds to a relatively new technique based on the hunting behavior of wolves. Even though GWO allows obtaining satisfying results, its limited exploration reduces its performance significantly when it faces high multi-modal and discontinuous cost functions. In this paper, a modified version of the GWO scheme is introduced to solve the complex optimization problems of supplier selection and order quantity allocation. The improved GWO method called iGWO includes weighted factors and a displacement vector to promote the exploration of the search strategy, avoiding the use of unfeasible solutions. In order to evaluate its performance, the proposed algorithm has been tested on a number of instances of a difficult problem found in the literature. The results show that the proposed algorithm not only obtains the optimal cost solutions, but also maintains a better search strategy, finding feasible solutions in all instances.

This article studies a recently proposed dc-dc converter and its optimization in terms of capacitors selection through the Particle Swarm Optimization (PSO) algorithm. The converter under study is the so-called Low Energy Storage Quadratic Boost Converter (LES-QBC), a quadratic type of converter that offers a smaller Output Voltage Ripple (OVR) compared to the traditional quadratic boost topology with capacitors of the same characteristics. This study presents a way to select the capacitors for minimizing the OVR while achieving a constraint of a maximum stored energy in capacitors. The capacitor’s stored energy is given as a design specification. The results are compared against the traditional quadratic boost converter and the LES-QBC without optimization (equal capacitance in capacitors). The optimization algorithm used was the so-called Particle Swarm Optimization (PSO). The experimental results demonstrate the effectiveness of the proposition. For the design exercise used for the results, the capacitor’s stored energy was kept almost the same, and a reduction in the OVR was achieved versus the non-optimized LES-QBC.

Economic Order Quantity (EOQ) is an important optimization problem for inventory management with an impact on various industries; however, their mathematical models may be complex with non-convex, non-linear, and non-differentiable objective functions. Metaheuristic algorithms have emerged as powerful tools for solving complex optimization problems (including EOQ). They are iterative search techniques that can efficiently explore large solution spaces and obtain near-optimal solutions. Simulated Annealing (SA) is a widely used metaheuristic method able to avoid local suboptimal solutions. The traditional SA algorithm is based on a single agent, which may result in a low convergence rate for complex problems. This article proposes a modified multiple-agent (population-based) adaptive SA algorithm; the adaptive algorithm imposes a slight attraction of all agents to the current best solution. As a proof of concept, the proposed algorithm was tested on a particular EOQ problem (recently studied in the literature and interesting by itself) in which the objective function is non-linear, non-convex, and non-differentiable. With these new mechanisms, the algorithm allows for the exploration of different regions of the solution space and determines the global optimum in a faster manner. The analysis showed that the proposed algorithm performed well in finding good solutions in a reasonably short amount of time.