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A Hybrid Photovoltaic Thermal (PVT) system is one of the most emerging and energy-efficient technologies in the area of solar energy engineering. This review paper provides a comprehensive review of hybrid PVT systems in the context of the history of PVT, general classification, and parameter analysis. Several cell technologies with spectrum analysis are discussed to understand the application's ability and energy efficiency. Hybrid PVT concept, characteristics, and structure analysis is also discussed in this study. An extensive analysis on the classifications of hybrid PVT systems from the recent literature is also presented here. These literatures are identified based on several criteria. In order to provide a complete and energy-efficient technology, an innovative classification of the hybrid PVT system is proposed in this paper. This proposed classification is a combination and upgrade of various existing classifications mentioned in recent research studies. Parameters have a significant and unavoidable impact on the performance and efficiency of the hybrid PVT system. A brief analysis of different parameters and the optimization of the system is conducted after reviewing recent research articles. This analysis provides insights into the impact of parameter variations on the system. A novel parameter model comprising parametric and optimistic analyses is also presented in this paper. It provides a detailed parametric description that significantly affects the performance and efficiency of the hybrid PVT system. Finally, the assessment focuses on a critical analysis of the main challenges in adopting PVT technology and suggests ways to overcome these barriers. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) techniques have emerged as powerful tools for optimizing energy systems, offering the potential to enhance efficiency, reliability, and sustainability. This review paper provides a comprehensive examination of the applications of RL in the field of energy system optimization, spanning various domains such as energy management, grid control, and renewable energy integration. Beginning with an overview of RL fundamentals, the paper explores recent advancements in RL algorithms and their adaptation to address the unique challenges of energy system optimization. Case studies and real-world applications demonstrate the efficacy of RL-based approaches in improving energy efficiency, reducing costs, and mitigating environmental impacts. Furthermore, the paper discusses future directions and challenges, including scalability, interpretability, and integration with domain knowledge. By synthesizing the latest research findings and identifying key areas for further investigation, this paper aims to inform and inspire future research endeavors in the intersection of reinforcement learning and energy system optimization. [ABSTRACT FROM AUTHOR]

Owing to increasing competition, the marketing of commercial software products is a challenging task. Diverse marketing strategies are adopted to tap the huge market share. Among these, pricing and warranty are the most significant ones. The warranty policies organizations adopt usually have two dimensions: duration and effort spent during the warranty phase. In this paper, two variables are broadly taken: testing, which is an essential phase during software development, and warranty, which is a crucial component of after‐sales service. Here, we will formulate an optimization problem where returns and expenses will be influenced by price, testing resources spent, the duration for which testing is done, warranty duration and effort spent during warranty for the software product. The product sales are assumed to follow a Stochastic Bass model based on Non‐Homogeneous Poisson Process (NHPP). A free on‐site support warranty with an upper limit on duration and effort covers the 2D failure process. The numerical illustration is presented wherein profit is maximized by conjointly optimizing five variables, that is price, testing duration, testing effort, warranty duration and warranty effort. This study emphasizes on application of a popular soft computing, metaheuristic technique named genetic algorithm (GA) to solve an optimization problem for a software system. The solution to the Optimization problem is obtained using the GA tool in MATLAB. The results obtained have relevance in the industry for finding optimal values of significant variables before the release of newly developed software products to the ultimate client. The concept discussed in this paper has diverse applications in computer engineering, software and hardware industry. [ABSTRACT FROM AUTHOR]

The growth in linked and autonomous vehicles has led to the emergence of vehicular ad hoc networks (VANETs) as a means to enhance road safety, traffic efficiency, and passenger comfort. However, VANETs face challenges in facilitating trustworthiness and high-quality services due to communication delays caused by traffic, dynamic topology changes, variable speeds, and other influencing factors. Hence, there is a need for a reliable data dissemination scheme capable of reducing communication delays among hops by identifying effective forwarder nodes. In this paper, we propose a novel, weighted, estimated, spider monkey-based, nature-inspired optimization (w-SMNO) method to generate a set of efficient relays. Additionally, we introduce a dynamic weight assignment and configuration model to enhance system accuracy using a neural network based on backpropagation with gradient descent optimization techniques to minimize errors in the machine learning model. The w-SMNO also incorporates a distinct algorithm for effective relay selection among multiple monkey spider groups. The simulation results demonstrate substantial improvements in w-SMNO, with a 35.7% increase in coverage, a 41.2% reduction in the end-to-end delay, a 36.4% improvement in the message delivery rate, and a 38.4% decrease in the collision rate compared to the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Many different aspects of software system development and verification rely on precise function identification in binary code. Recognition of the source Assembly functions in embedded systems is one of the fundamental challenges in binary program analysis. While numerous approaches assume that the functions are given a priori, correct identification of the functions in binaries remains a great issue. This contribution addresses the problem of uncertainty in binary code in identification of functions, which were optimized during compilation. This paper investigates the difference between debug and optimized functions via modeling of these functions. To do so, we introduce an extensible model-centred hands-on approach for examining similarities between binary functions. The main idea is to model each function using a set of predetermined, experimentally discovered features, and then find a suitable weight vector that could give impact factor to each such a feature. After finding the weight vector, the introduced models of such desired functions can be identified in binary software packages. It means that we reduce the similarity identification problem of the models to a classical version of optimization problems with one optimization criterion. Using our implementation, we found that the proposed approach works smoothly for functions, which contain at least ten Assembly instructions. Our tool guarantees success at a very high level. [ABSTRACT FROM AUTHOR]

Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables. [ABSTRACT FROM AUTHOR]

Simulation models are a valuable tool for exoskeleton development, especially for system optimization and evaluation. It allows an assessment of the performance and effectiveness of exoskeletons even at an early stage of their development without physical realization. Due to the closed physical interaction between the exoskeleton and the user, accurate modeling of the human–exoskeleton interaction in defined scenarios is essential for exoskeleton simulations. This paper presents a novel approach to simulate exoskeleton motion in response to human motion and the interaction forces at the physical interfaces between the human and the exoskeleton. Our approach uses a multibody model of a shoulder exoskeleton in MATLAB R2021b and imports human motion via virtual markers from a digital human model to simulate human–exoskeleton interaction. To validate the human-motion-based approach, simulated exoskeleton motion and interaction forces are compared with experimental data from a previous lab study. The results demonstrate the feasibility of our approach to simulate human–exoskeleton interaction based on human motion. In addition, the approach is used to optimize the support profile of an exoskeleton, indicating its potential to assist exoskeleton development prior to physical prototyping. [ABSTRACT FROM AUTHOR]

In the last decade, micro-architected structures have gained significant attention in academia and industry for their lightweight, strong, and thermally efficient properties. Inspired by biomimicry design, this paper presents a novel ribbed family of additively manufactured Micro-Architected Domes (MAD). The design incorporates tetrapod pyramid unit cells, golden ratio-based fractal patterns, Schoen's Minimal Gyroid, and spherical geometry. The study focuses on dome radius, height, and azimuth/elevation partitioning as input variables, with the main output being ribbed micro-cell diameter. The relationships between unit-cells' diameter and input variables were established through problem-solving and numerical computations: linear dependency with the dome radius and hyperbolic dependency with the azimuth and elevation partitioning. The proposed design successfully adhered to the Surface-to-Volume ratio of Schoen's Minimal Gyroid, achieving an average volume relative density of 2.5%, confirming its lightweight nature. The feasibility of the design was further supported by fabricating three specimens using Filament Fused Fabrication. This research showcases the potential of biomimicry-inspired micro-architected structures, paving the way for innovative applications in various fields. [ABSTRACT FROM AUTHOR]

The paper considers a modelling framework for a set of households in residential areas using electricity as a form of energy for domestic consumption. Considering the demand and availability of units for electricity consumption, optimal decisions for electricity load allocation are paramount to sustain energy management. We formulate this problem as a stochastic decision-making process model where electricity demand is characterized by Markovian demand. The demand and supply phenomena govern the loading and operational framework, where shortage costs are realized when demand exceeds supply. Empirical data for electricity consumption was collected from fifty households in two residential areas within the suburbs of Kampala in Uganda. Data collection was made at hourly intervals over a period of four months. The major problem focussed on determining an optimal electricity loading decision to minimize consumption costs as demand changes from one state to another. Considering a multi-period planning horizon, an optimal decision was determined for loading or not loading additional electricity units using the Markov decision process approach. The model was tested, and the results demonstrated the existence of optimal state-dependent decision and consumption costs considering the case study used in this study. The proposed model can be cost-effective for managers in the electricity industry. Improved efficiency and utilization of resources for electricity distribution systems to residential areas were realized, with subsequently enhanced service reliability to essential energy market customers. [ABSTRACT FROM AUTHOR]

Recommender Systems have been widely employed in information systems over the past few decades, making it easier for each user to choose their own products based on their past behaviour. Data mining tasks and visualization tools regularly use clustering techniques in the scientific and commercial arenas. It has been shown that clustering-based methods are effective and scalable to big data sets. The accuracy and coverage of clustering-based recommender systems are, however, somewhat low. In this paper, we suggest an improved multi-view clustering method for the recommendation of items in social networks to overcome these problems. To create better partitions, the artificial Bees colony optimization algorithm (ABC) is first used to improve the initial medoids' selection. After that, users are clustered iteratively using views of both rating patterns as well as social information using multiview clustering (MVC) (i.e. trust and friendships). Ultimately, a framework is suggested for evaluating the various options. This research study suggests a novel MVC clustering approach using the ABC optimization technique. The proposed ABC-MVC algorithm's usefulness in terms of enhancing accuracy is demonstrated by experimental findings performed on a real-world dataset and it is observed that it performs better than the pre-existing techniques and baselines. [ABSTRACT FROM AUTHOR]

Determining the mining sequence is one of the main objectives in mine planning. However, depending on the size of the analyzed instances, such activity might become an extremely difficult task, despite current computational capacity. In addition, determining a feasible and operational mining sequence is also challenging, so practitioners usually employ strategies to segment and simplify the main problem, such as splitting it into distinct time horizons and aggregating blocks into clusters. This paper aims to perform a critical review about the different clustering methodologies and algorithms used for mining-block aggregation, with the purpose of understanding the proposed solutions and identifying the gaps found in the current literature. The reviewed aggregation strategies encompass the modelling of tabular deposits as sets of layers and grouping of blocks in benches, bench-phases, and mining cuts. Among the optimization techniques evaluated, one may find heuristics, artificial intelligence, and exact approaches, relying on deterministic or uncertainty-based methodologies, considering approximately six decades of studies and covering fifty-eight works published in journals and proceedings from 1967 to 2022. In addition to what is seen within the literature analyzed, we also propose future research directions, such as approaches and algorithms not yet implemented to solve the block aggregation problem, thus presenting opportunities for further research in this field. [ABSTRACT FROM AUTHOR]

Computational modeling plays an increasingly important role in neuroscience, highlighting the philosophical question of how computational models explain. In the particular case of neural network models, concerns have been raised about their intelligibility, and how these models relate (if at all) to what is found in the brain. We claim that what makes a system intelligible is an understanding of the dependencies between its behavior and the factors that are responsible for that behavior. In biology, many of these dependencies are naturally "top-down", as ethological imperatives interact with evolutionary and developmental constraints under natural selection to produce systems with capabilities and behaviors appropriate to their evolutionary needs. We describe how the optimization techniques used to construct neural network models capture some key aspects of these dependencies, and thus help explain why brain systems are as they are — because when a challenging ecologically-relevant goal is shared by a neural network and the brain, it places constraints on the possible mechanisms exhibited in both kinds of systems. The presence and strength of these constraints explain why some outcomes are more likely than others. By combining two familiar modes of explanation — one based on bottom-up mechanistic description (whose relation to neural network models we address in a companion paper) and the other based on top-down constraints, these models have the potential to illuminate brain function. [ABSTRACT FROM AUTHOR]

Water distribution systems (WDSs) are essential elements for the prosperity and development of societies around the globe. However, over time, the pipeline network starts to age and deteriorate, which results in an increasing rate of breaks and water loss due to leakages. Many countries have started government-funded plans to upgrade and rehabilitate their WDS network components to overcome these challenges. This study proposes an optimization framework that addresses these issues and offers potential benefits. It aims to achieve the optimal upgrading strategies considering network operation (hydraulic) performance and upgrading cost, including investment and non-revenue water costs. The upgrade of the WDS network in the model consists of replacing pipes and controlling the pressure-reducing valve (PRV) settings to reduce leakages. The proposed framework is demonstrated using a small-sized benchmark WDS. The study's outcomes provide the utilities' operators and municipalities' decision-makers with a guiding tool to choose the optimal upgrading strategy for their WDS networks at the lowest cost and optimum operation performance. The methodology involves simulating various leakage scenarios and applying optimization techniques to find the best combination of pipe replacements and PRV settings. This approach ensures a balance between minimizing leakage rates and controlling upgrading costs. The framework achieved a reduction of leakage up to 20% from the original leakage with a 70% probability for the tested benchmark network. The optimization framework can also offer a range of upgrading strategies, with a trade-off between the WDS network leakage reduction and the required cost of the upgrading strategy. [ABSTRACT FROM AUTHOR]

The objective of this study is to assess the impact of incorporating rubber tire waste and brick fillers as a substitute for sand in cement mortar samples. The evaluation includes measurements of the bending strength, dynamic modulus of elasticity, and displacement. The replacement of sand by rubber waste (RW) and brick waste (BW) at 0%, 10%, 20% and 30% for rubber waste and 0%, 2.5%, 5.0%, 7.5%, and 10% for brick waste by volume was experimentally conducted. According to the findings, the introduction of rubber waste leads to a significant reduction in the flexural strength, dynamic modulus of elasticity, and displacement. The brick waste can be considered a suitable filler, which minimizes the negative effect of rubber tires and tends to result in suitable mixtures. The optimal values (10.10% for the RW and 10% for the BW) showed good agreement with the experimental results, with differences of 1.85%, -4.73%, and -4.48% for the displacement, flexural strength, and dynamic elastic modulus, respectively. [ABSTRACT FROM AUTHOR]

In order to improve the hydraulic efficiency and internal flow pattern of a multi-stage pump under multiple flow conditions, an intelligent optimization design was proposed for its hydraulic components. Sensitivity analysis was used to select the key parameters influencing the hydraulic efficiency of a multi-stage pump. The optimal Latin hypercube sampling and non-dominated sorting genetic algorithm Ⅱ were employed to build a multi-objective optimization system. Moreover, a radial basis function neural network was adopted as the surrogate model of hydraulic efficiency. The research results showed that the impeller outlet width, impeller blade wrap angle, impeller outlet blade angle, and diffuser inlet width were the key factors affecting the hydraulic efficiency. The efficiency of the optimized model increased by 4.35% under the design condition and the matching of the internal flow between the optimized impeller and diffuser was significantly enhanced under the nominal condition. The improved flow pattern could be clearly observed in the flow passage of both the pump impeller and the diffuser. After optimization, the wear performance of the model was also improved compared to the original design. The wear area decreased in size and was distributed more evenly, resulting in a noticeable decrease in the maximum amount of wear. [ABSTRACT FROM AUTHOR]

A model of a system of wireless power transfer (WPT) pads is developed, where each WPT pad is modeled as a node and the coupling between pads is modeled as graph edges. This modeling approach is generalized to admit primary, secondary, and booster coils, where power can flow among the pads and a pad can fill multiple roles. An excitation in one pad induces voltage and current in all neighboring pads, causing each pad to act as both a booster coil and either a transmitter or a receiver. Power flow through the entire system can be modeled with the graph structure; the power flow can then be optimized by alternating the phases of the WPT excitations to maximize power transfer. An example is shown where exploiting the graph-based WPT system modeling increases total energy transfer by 25% compared to another method. This increase occurs without altering the geometry of the pads or the magnitude of the pad excitations. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources, such as wind and solar, into co-located hybrid power plants (HPPs) has gained significant attention as an innovative solution to address the intermittency and variability inherent in renewable systems among plant developers because of advancements in technology, economies of scale, and government policies. However, it is essential to examine different challenges and aspects during the development of a major work on large-scale hybrid plants. This includes the need for optimization, sizing, energy management, and a control strategy. Hence, this research offers a thorough examination of the present state of co-located utility-scale wind–solar-based HPPs, with a specific emphasis on the problems related to their sizing, optimization, and energy management and control strategies. The authors developed a review approach that includes compiling a database of articles, formulating inclusion and exclusion criteria, and conducting comprehensive analyses. This review highlights the limited number of peer-reviewed studies on utility-scale HPPs, indicating the need for further research, particularly in comparative studies. The integration of machine learning, artificial intelligence, and advanced optimization algorithms for real-time decision-making is highlighted as a potential avenue for addressing complex energy management challenges. The insights provided in this manuscript will be valuable for researchers aiming to further explore HPPs, contributing to the development of a cleaner, economically viable, efficient, and reliable power system. [ABSTRACT FROM AUTHOR]

Blockchain mining pools assist in reducing computational load on individual miner nodes via distributing mining tasks. This distribution must be done in a non-redundant manner, so that each miner is able to calculate block hashes with optimum efficiency. To perform this task, a wide variety of mining optimization methods are proposed by researchers, and most of them distribute mining tasks via statistical request processing models. These models segregate mining requests into non-redundant sets, each of which will be processed by individual miners. But this division of requests follows a static procedure, and does not consider miner specific parameters for set creation, due to which overall efficiency of the underlying model is limited, which reduces its mining performance under real-time scenarios. To overcome this issue, an Incremental & Continuous Q-Learning Framework for generation of miner-specific task groups is proposed in this text. The model initially uses a Genetic Algorithm (GA) method to improve individual miner performance, and then applies Q-Learning to individual mining requests. The Reason for selecting GA model is that it assists in maintaining better speed-to-power (S2P) ratio by optimization of miner resources that are utilized during computations. While, the reason for selecting Q-Learning Model is that it is able to continuously identify miners performance, and create performance-based mining pools at a per-miner level. Due to application of Q-Learning, the model is able to assign capability specific mining tasks to individual miner nodes. Because of this capability-driven approach, the model is able to maximize efficiency of mining, while maintaining its QoS performance. The model was tested on different consensus methods including Practical Byzantine Fault Tolerance Algorithm (PBFT), Proof-of-Work (PoW), Proof-of-Stake (PoS), and Delegated PoS (DPoS), and its performance was evaluated in terms of mining delay, miner efficiency, number of redundant calculations per miner, and energy efficiency for mining nodes. It was observed that the proposed GA based Q-Learning Model was able to reduce mining delay by 4.9%, improve miners efficiency by 7.4%, reduce number of redundant computations by 3.5%, and reduce energy required for mining by 7.1% when compared with various state-of-the-art mining optimization techniques. Similar performance improvement was observed when the model was applied on different blockchain deployments, thus indicating better scalability and deployment capability for multiple application scenarios. [ABSTRACT FROM AUTHOR]

[Display omitted] • The most profitable system is on-grid PV-Wind Turbine system with an NPC of $52.8 M. • For 0% and 25% sale constrained systems low penetration systems are optimal. • For 50% sale constrained and unconstrained systems high penetration systems are preferred. • Fire hawk Optimizer, Grey Wolf Optimizer and Particle Swarm Optimizer are used to size the system by power flow analysis. Fire Hawk Optimizer is found to be usable in optimal power flow analysis and system sizing. • Increase in penetrations leads to enlargement of the system in the region with high load demand because of optimizations. Today, with a human population of 7.5 billion, increasing power consumption, and widespread use of high-technology equipment, the need for energy in the world is increasing day by day. The damage to nature caused by non-renewable energy resources and the economic and political disadvantages they cause in countries that must import these resources have led the world to search for alternative energy sources. One of the most important benefits of renewable energy sources is that they can create hybrid energy systems with other energy sources. Hybrid energy systems are structures in that more than one energy generation unit works together to feed the electrical load. In this paper, a hybrid system will be designed by using Homer PRO, to supply the energy consumed in the residential areas of Karaburun and Urla districts of Izmir province in Turkey with hybrid renewable energy systems. Systems of different sizes are designed for 50%, 75%, and 100% penetration rates, and sales to the grid are constrained to 0%, 25%, 50%, and no constraint on sales. Sensitivity analyses have been performed for 10% and 20% more power required by the load. Techno-economic analyses were done for the systems and the optimal system has been identified. The power flow of the optimal system is inspected by defining the network in Python and Fire Hawk Optimizer (FHO), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO) are used for optimal sizing of the system. The optimal system has a Net Present Cost (NPC) of $52.3 M, 34 wind turbines, and 954 kW PV with conditions of 100% penetration and no restrictions on sales to the grid. FHO proposed 26 wind turbines and 780 kW PVs for Urla and 8 wind turbines, and 174 kW PVs for Karaburun. GWO proposed 25 wind turbines and 747 kW PVs for Urla, 9 wind turbines, and 165 PVs for Karaburun. PSO offered 24 wind turbines and 708 kW PVs for Urla, 10 wind turbines, and 246 kW PVs for Karaburun. When there is no sale to and 25% sales to the grid, it is seen that the optimal scenario is the system with a 50% penetration rate. In fully sale-constrained systems, NPCs are higher than the base system and they can never pay for themselves compared to the base system. It is concluded that smaller-sized systems are more suitable for systems with high constraints. The proposed FHO algorithm and GWO algorithm react to increasing penetration ratios by enlarging the system size in the regions with high load demand. PSO sizes the system like predictions by load demand. FHO reacts quicker to increasing penetrations than PSO and GWO. FHO algorithm is found to be usable to solve optimal power flow problems and optimal system sizing. [ABSTRACT FROM AUTHOR]

This study, centered around the engineering context of the Wuxue Yangtze River Bridge, addresses the challenge of significant temperature-induced secondary internal forces in the short lower tower column. A novel open lower corbel tower scheme is proposed as a solution. Firstly, comprehensive finite element models are established for both the open lower corbel pylon scheme and the traditional lower continuous beam pylon scheme. These models are employed for finite element analysis to derive bending moments and displacements of the bridge pylon under various loads, including permanent, vehicle, temperature, and wind loads. Subsequently, considering internal force distribution and stiffness, a comparative assessment is made between the open lower corbel cable pylon scheme and the traditional lower continuous beam cable pylon scheme. The findings reveal that the open corbel structure bridge pylon exhibits lower transverse bending moment values under the influence of permanent load, vehicle load, temperature load, and wind load. This reduction is advantageous for mitigating the issue of significant temperature-induced secondary internal forces in the bridge pylon. Additionally, the transverse bridge stiffness of the open lower corbel cable pylon scheme is found to be on par with that of the lower continuous beam cable pylon scheme. Moreover, topology optimization of the original corbel design is accomplished using the relative density method. The computational results demonstrate that the corbel’s stress and deformation under vertical loads meet code requirements. These research findings offer valuable insights for the design and construction of similar projects. [ABSTRACT FROM AUTHOR]

Genetic Algorithm is an optimization technique inspired by nature. The technique has been used by scientists and engineers for real-life search and optimization problems. This work makes use of the genetic algorithm for the solution of the traveling salesman problem. This work focuses on real-time problems; the algorithm is used to find the optimum path for sales travelers inside the Khyber Pakhtunkhwa Province of Pakistan. The solution provides the shortest distance between the cities to be traveled and gives the optimal route. The coding is done in Python-3 and software is developed for the traveling salesmen, where the salesmen can select the cities, they want to travel, and the software will provide the optimal path and the distance. [ABSTRACT FROM AUTHOR]

Logistics and the supply chain are areas of great importance within organizations. Due to planning gaps, an increase in extra and unnecessary transport costs is usually observed in several companies due to their commercial commitments and need to comply with the delivery time and the batch quantity of products, leading to a negative economic impact. Thus, the objective of this work was to adjust an optimization model to maximize the shipments usually carried out by the companies. To validate the model, an automotive components manufacturer was selected, allowing us to apply the model to a real case study and evaluate the advantages and drawbacks of this tool. It was found that the company to validate the model exports most of its products, and most pallets sent are not fully optimized, generating excessive expense for the company in terms of urgent transport. To solve this problem, two mathematical optimization models were used for the company's current reality, optimizing the placement of boxes per pallet and customer. With the use of the new tool, it was possible to determine that five pallets should be sent to the customer weekly, which correspond to their needs, and that have the appropriate configurations so that the pallet is sent completely. [ABSTRACT FROM AUTHOR]

Sewer systems are a component of city infrastructure that requires large investment in construction and operation. Metaheuristic optimization methods have been used to solve sewer optimization problems. The aim of this study is to investigate the effects of network size on metaheuristic optimization algorithms. Cuckoo Search (CS) and four versions of Grey Wolf Optimization (GWO) were utilized for the hydraulic optimization of sewer networks. The purpose of using different algorithms is to investigate whether the results obtained differ depending on the algorithm. In addition, to eliminate the parameter effect, the relevant algorithms were run with different parameters, such as population size. These algorithms were performed on three different-sized networks, namely small-sized, medium-sized, and large-sized networks. Friedman and Wilcoxon tests were utilized to statistically analyze the results. The results were also evaluated in terms of the optimality gap criterion. According to the results based on the optimality gap, the performance of each algorithm decreases as the network size increases. [ABSTRACT FROM AUTHOR]

Most biofuels today come from sources that have some relationship with products of the human diet, which has generated controversy regarding their feasibility for industrial-scale production. This work presents a mathematical optimization model for installing bioethanol and biodiesel processing plants involving unconventional bioresources. The considered bioresources are not part of the human diet. The proposed mathematical model considers the maximization of the annual profit considering the installation of biofuel plants and the distribution of these biofuels in areas that present a greater demand for gasoline or diesel to replace fossil fuels. A case study from Mexico was considered since its wide biodiversity. The first scenario presents the optimal solution where there is high feasibility of bioethanol and biodiesel production, obtaining a production of 8.14 × 107 L/year and 2.18 × 108 L/year, respectively. In addition, the feasibility is shown considering the variation in the temporality of the availability of the bioresources for the generation of biofuels. The proposed model is flexible, and it can be used to analyze other case studies. [ABSTRACT FROM AUTHOR]

An interdisciplinary approach to management allows for the integration of knowledge and tools of different fields of science into a unified methodology in order to improve the efficiency of resource management of different kinds of systems. In the conditions of global transformations, it is economic systems that have been significantly affected by external destabilizing factors. This determines the focus of attention on the need to develop tools for the modeling and optimization of economic systems, both in terms of organizational structure and in the context of resource management. The purpose of this review study is to identify the current gaps (shortcomings) in the scientific literature devoted to the issues of the modeling and optimization of economic systems using the tools of mass service theory. This article presents a critical analysis of approaches for the formulation of provisions on mass service systems in the context of resource management. On the one hand, modern works are characterized by the inclusion of an extensive number of random factors that determine the performance and efficiency of economic systems: the probability of delays and interruptions in mobile networks; the integration of order, inventory, and production management processes; the cost estimation of multi-server system operation; and randomness factors, customer activity, and resource constraints, among others. On the other hand, controversial points are identified. The analytical study carried out allows us to state that the prevailing majority of mass service models applied in relation to economic systems and resource supply optimization are devoted to Markov chain modeling. In terms of the chronology of the problems studied, there is a marked transition from modeling simple systems to complex mass service networks. In addition, we conclude that the complex architecture of modern economic systems opens up a wide research field for finding a methodology for assessing the dependence of the enterprise performance on the effect of optimization provided by using the provisions of mass service theory. This statement can be the basis for future research. [ABSTRACT FROM AUTHOR]

Syngas fermentation can play an important role in implementing the concept of biorefinery as it can serve as a platform to convert high-lignin biomass to biofuels. For the utilization of this process in commercial scale, the generation of an experimental database supported by a deterministic mathematical model and optimization is necessary. In this study, a locally isolated clostridial consortium, UACJUChE1, was used to convert pyro-syngas to ethanol and acetic acid. Mathematical models were developed and validated for a 3 L stirred and gas-sparged bioreactor operated in both semi-batch and continuous modes. The volumetric productivity of ethanol was correlated with the dilution rate and the gas residence time. The performance of the bioreactor, run in both semi-batch and continuous modes, was optimized using response surface methodology. For the semi-batch operation, a maximum ethanol concentration of 13.122 g/L after 30 h operation was achieved at optimum values of pyrolysis temperature, ratio of gas to liquid volume (VG/VL), and volumetric gas flow rate of 648 °C, 0.46, and 6.7 L/h respectively. For continuous operation, a maximum ethanol concentration of 29.450 g/L after 300 h is obtained at optimum values of VG/VL and ratio of gas to liquid volumetric flow rate of 0.28 and 335.148, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a hybridization method based on Arithmetic optimization algorithm (AOA) and Aquila optimizer (AO) solver namely, the AO-AOA is applied to solve the Optimal Power Flow (OPF) problem to independently optimize generation fuel cost, power loss, emission, voltage deviation, and L index. The proposed AO-AOA algorithm follows two strategies to find a better optimal solution. The first strategy is to introduce an energy parameter (E) to balance the transition between the individuals' procedure of exploration and exploitation in AO-AOA swarms. Next, a piecewise linear map is employed to reduce the energy parameter's (E) randomness. To evaluate the performance of the proposed AO-AOA algorithm, it is tested on two well-known power systems i.e., IEEE 30-bus test network, and IEEE 118-bus test system. Moreover, to validate the effectiveness of the proposed (AO-AOA), it is compared with a famous optimization technique as a competitor i.e., Teaching-learning-based optimization (TLBO), and recently published works on solving OPF problems. Furthermore, a robustness analysis was executed to determine the reliability of the AO-AOA solver. The obtained result confirms that not only the AO-AOA is efficient in optimization with significant convergence speed, but also denotes the dominance and potential of the AO-AOA in comparison with other works. [ABSTRACT FROM AUTHOR]

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