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In this paper we apply fuzzy set theory to the portfolio optimization of power generation assets, using a semi-mean absolute deviation (SMAD) model as a benchmark and a fuzzy semi-mean absolute deviation (FSMAD) model for comparison. The two models are applied to five onshore wind power plants in Germany considered for the portfolio analysis. The results show that the combinations of favorable assets for efficient portfolios are very similar, although the portfolio shares are markedly different. Also, the return and risk span of the SMAD model are much broader than those of the FSMAD model. The highest returns are generated by portfolios based on the latter model. Offering less portfolio choices, the FSMAD model thus facilitates decision-making. This is in compliance with the notion that portfolio optimization by fuzzy set theory is able to better account for the decisionmaker's preferences under real-world conditions. [ABSTRACT FROM AUTHOR]

The study here summarizes the work growth in "Particle Swarm Optimization (PSO)" since (1995 to 2021). Thirty-five papers have been analysed. Those research articles have been divided into various aspects in which PSO technique has been modified a lot during these years due to its simplicity. The hybridization of PSO with many evolutionary algorithms has been done during these years which helped us a lot to solve many optimizations and real-life problems. This paper discusses the progress, modification, improvement and application of PSO during these years. [ABSTRACT FROM AUTHOR]

The paper explores and reviews the mathematical optimization models that have been developed seeking to incorporate Light Electric Vehicles (LEV) for urban load distribution in logistics distribution models. This exploration is done through a Systematic Literature Review (SLR) with the purpose of making a deep understanding of the optimization functions, the decision variables, and the parameters that have been used in the reviewed research. In addition, this study investigates the attention that this issue has received in recent years in different optimization approaches such as distribution time, greenhouse gas emissions, or operating costs. Furthermore, this paper highlights the relevance of in-depth research that allows the development of new mathematical models that make evident the viability of the implementation of eco-friendly cargo vehicles in Latin American cities’ distribution systems in terms of costs, and the need for a replicable model-construction methodology in locations with similar characteristics. The novelty of this paper, therefore, relies on the specific exploration of mathematical model-based publications, and deep analysis and comparison of their variables, parameters, and restrictions. This paper identifies the five different optimization approaches that have been used globally for the incorporation of LEV in urban logistics and compares the 25 main parameters and 7 main variables used among these models. [ABSTRACT FROM AUTHOR]

Heart disease is a major killer in the world. Nature-inspired algorithms have been used as optimization techniques to predict the disease. The prediction of the disease can be further enhanced by using classification models of machine learning algorithms. The accuracy and other aspects of prediction varies with classification models. This paper presents the research conducted by combining nature-inspired techniques with machine learning algorithms to make intelligent decisions for prediction of heart diseases. As part of the research conducted, the prediction of the heart disease has been assessed by the combination of nature-inspired techniques and machine learning algorithms. This paper presents the outcome of the research conducted with four nature-inspired algorithms, namely Ant, Bat, Bee and Genetic algorithms and two machine learning classification models, Random Forest and SVM to predict the heart disease. Feature variables are extracted from heart data obtained from the UCI Machine Learning Repository. The output of the optimization techniques is used to train models based on the two classification algorithms. The measures of prediction were compared. [ABSTRACT FROM AUTHOR]

In the online minimum cost matching problem, there are n servers and, at each of n time steps, a request arrives and must be irrevocably matched to a server that has not yet been matched, with the goal of minimizing the sum of the distances between the matched pairs. Online minimum cost matching is a central problem in applications such as ride-hailing platforms and food delivery services. Despite achieving a worst-case competitive ratio that is exponential in n even on the line, the simple greedy algorithm, which matches each request to its nearest available server, performs well in practice and has a number of attractive features such as strategyproofness. A major question is thus to explain greedy's strong empirical performance. In this paper, we aim to understand the performance of greedy on the line over instances that are at least partially random. When both the requests and the servers are drawn uniformly and independently from [0, 1], we obtain a constant competitive ratio for greedy, which improves over the previously best-known bound of [EQUATION] for greedy in this setting. We also show that this constant competitive ratio also holds in the excess supply setting where there is a linear excess of servers, which improves over the previously best-known bound of O(log3n) for greedy in this setting. We moreover show that in the semi-random model where the requests are still drawn uniformly and independently but where the servers are chosen adversarially, greedy achieves an O(log n) competitive ratio. Even though this one-sided randomness allows a large improvement in greedy's competitive ratio compared to the model where the requests are fully adversarial or arrive in a random order, we show that it is not sufficient to obtain a constant competitive ratio by giving a tight Ω(log n) lower bound. These results invite further investigation about how much randomness is necessary and sufficient to obtain strong theoretical guarantees for the greedy algorithm for online minimum cost matching, on the line and beyond. A full version of this paper can be found at https://arxiv.org/abs/2210.03166. [ABSTRACT FROM AUTHOR]

Poor power quality is a major concern in power system, which can lead to equipment damage, power interruptions, and safety hazards. In modern days, due to non-linear loads like UPS, inverters, converters, etc., the supply current takes on a non-sinusoidal shape. Multilevel inverters are widely used in power systems to improve power quality in higher voltage levels with fewer switches and by reducing harmonic distortion which improves the overall efficiency of the system. Several optimization techniques can be used to design and control multilevel inverters for improved power quality performance. The important object of this review paper is to identify various optimization techniques while taking various power quality-related challenges into account. Thus, the current study analyses different power quality issues and their improvement with multilevel inverters. The paper also discusses overviews of various technical and common issues presented in the literature, all carried out in a comprehensive manner. Finally, this paper offers a scenario of the current state of power quality improvement along with different optimization techniques which discovers research gaps along with suggestions for future research directions. [ABSTRACT FROM AUTHOR]

The application of the Automated Storage and Retrieval System (AS/RS) in the warehouse has been improved significantly over the last few decades. In the recent years, a new type of AS/RS, Shuttle Based Storage and Retrieval System (SBS/RS) has taken many industries interest by storm due to its flexibility and throughput performances. This paper studies the mathematical optimization of item location assignment and task scheduling in SBS/RS mainly with tier-to-tier system which allows the shuttle to transport between tiers using a lift system. Since this system is mostly applied in giant e-commerce industries, considering order data set and multiple stock keeping unit (SKU) in the mathematical model formulation will give a significant benefit in the real-life scenario. This paper also proposed a modified genetic algorithm to solved both item location assignment and task scheduling with a good computational time result for large size instances within a reasonable amount of time. [ABSTRACT FROM AUTHOR]

Investment basically means to indulge the money into an investment alternative for the purpose of profit and income stability, such as gold, fixed deposits, real estate and cryptocurrency, people who invest known are as investors. Maintaining risk in individual positions and throughout the portfolio as a whole is critical to being a successful investor and trader. However, with so many different approaches to risk management at these two levels, it can be tough to figure out which one is ideal for your own style and risk tolerance. For making it easier and efficient there are some techniques and strategies which are Market Cap Weighted Portfolio, Sharpe Ratio, Global Efficient Frontier AKA MSR, Mean-Variance (Markowitz), Minimum Variance, Hierarchical Risk Parity (HRP), Cvar and Var, equal weighted portfolio and minimum variance. This research paper is concrete scope review of all these techniques and strategies. [ABSTRACT FROM AUTHOR]

This paper presents the comparison ofselective harmonic elimination in a cascaded h-bridge multilevel (CHBMLI) inverter using PSO and TLBO optimization algorithms. The inverters are employed in a variety of applications, including high-voltage dc transmission, variable drive systems, and electrical vehicles. The inverter's output includes harmonics. PSO and TLBO algorithm is used in this paper To find out the best switching angles for a CHBMLI selective harmonic elimination. In order to get the appropriate fundamental component and get rid of low-order harmonics, this project solves nonlinear equations. These algorithms can produce the most effective triggering pulses to minimize Overall Harmonic Distortion(THD) and Selected Harmonics. If the harmonics are removed, the inverter's output will be improved. MATLAB is used to implement and compared both optimization techniques usingthe seven-level inverter. [ABSTRACT FROM AUTHOR]

This paper proposes a novel data-driven optimization framework that integrates building design decisions with simulation results to optimize sustainability across environmental, social, and economic dimensions. The building industry faces increasing pressure to reduce emissions and environmental impacts, but existing design tools do not fully account for how buildings will be operated by occupants. The proposed optimization framework utilizes statistical learning via metamodels and decision optimization to explore the complex tradeoffs involved in building decisions. The focus of this paper is on developing an optimization approach performed on common machine learning models such as classification and regression trees (CART), gradient boosted trees and random forest to investigate the causal relationship between building decisions and performance metrics. We integrate both descriptive and prescriptive analytics to inform an optimization model that provides decisions for both the design and operations of a given building. A case study is presented demonstrating the potential benefits of the optimization model in the design phase of a building. The integrative, data-driven optimization approach incorporates both categorical and continuous variables to create higher-performing, more sustainable building designs that better balance Planet, People, and Prosperity goals. [ABSTRACT FROM AUTHOR]

Generation of very fast transient over voltages (VFTOs) is very common during switching operations in gas insulated substations (GIS) and it is extremely harmful for substation equipment like transformers as it becomes a reason of insulation damage due to its less arising time. Sometimes, the VFTOs magnitude may be very near to the basic insulation level (BIL). Improvement and investigation of such transients in power transformer is of crucial importance in GIS as transformers are vital components of power system network. Generation of transient issues considers the generation and propagation of VFTOs while switching of circuit breakers or disconnector switches or earth faults. In this research, the investigation is on VFTO by putting intelligible models for a GIS of a capacity 400 KV. This paper presents a modeling guideline for VFTO analysis in GIS and, further mitigation techniques such as Nano-crystalline Rings, RC Filter, Ferrite Rings, Damping Resistance etc. have been studied using MATLAB simulation. Using an intelligent optimization technique like PSO, VFTO waveforms can be scaled down further to controllable limits so that substation components can be saved from impending failures. Further VFTO effect on GIS such as stress on substation Transformer leading to winding deformation has been discussed in this paper and localization of deformation and type of deformation in transformer winding is also carried out using intelligent classifier. [ABSTRACT FROM AUTHOR]

The ATLAS Trigger and Data Acquisition (TDAQ) High Level Trigger (HLT) computing farm contains 120 000 CPU cores. These resources are critical for the online selection and collection of collision data in the ATLAS experiment during LHC operation. Since 2013, during a longer period of LHC inactivity, these resources are being used for offline event simulation via the "Simulation at Point One" project (Sim@P1). With the recent start of LHC Run 3 and the flat computing budget expected in the near future, finding ways to maximize resource utilization efficiency is of paramount importance. Recent improvements in the ATLAS software stack can potentially allow the utilization of the Sim@P1 even during LHC operation for the duration of the LHC inter-fill gaps. While previous papers on the Sim@P1 project emphasized the technical implementation details, the current contribution is presenting results of a variety of tests that led to the optimal configuration of the job submission infrastructure which would allow the use of Sim@P1 during LHC Run 3. [ABSTRACT FROM AUTHOR]

This paper presents the rapid progress, vision and outlook across multiple state of the art development lines within the Global Network Advancement Group (GNA-G) and its Data Intensive Sciences and SENSE/AutoGOLE working groups, which are designed to meet the present and future needs and address the challenges of the Large Hadron Collider and other science programs with global reach. Since it was founded in the Fall of 2019 and the working groups were formed in 2020, in partnership with ESnet, Internet2, CENIC, GEANT, ANA, RNP, StarLight, NRP, N-DISE, AmLight, and many other leading research and education networks and network R&D projects, as well as Caltech, UCSD/SDSC, Fermilab, CERN, LBL, and many other leading universities and laboratories, the GNA-G working groups have deployed two virtual circuit and programmable testbeds spanning six continents which supports continuous developments aimed at the next generation of programmable networks interworking with the science programs' computing and data management systems. The talk covers examples of recent progress in developing and deploying new methods and approaches in multidomain virtual circuits, flow steering, path selection, load balancing and congestion avoidance, segment routing and machine learning based traffic prediction and optimization. [ABSTRACT FROM AUTHOR]

This paper presents three types of optimisation techniques namely particle swarm optimisation, artificial bee colony optimisation, and grey wolf optimisation. These optimisation approaches are employed to determine the optimal voltage collapse point (VCP) of the power system weakest bus without having to perform several power flow calculations. Moreover, this study is employed in the simplified Department of Electrical Services (DES) network in Brunei Darussalam. The optimality of the results is aided with reactive power-voltage (QV) modal analysis. Furthermore, the results obtained from the employed methods were compared with the results obtained from power system simulator for engineering (PSSE) 34 software. In the end, the results of each analysis were discussed and compared to show its effectiveness in determining the VCP of the weakest bus. [ABSTRACT FROM AUTHOR]

Nature inspired techniques are most popular techniques used for optimization. Clustering is a data mining functionality which is also used in optimization. In recent past, researchers have implemented several nature inspired techniques using clustering. In this paper, scientometric analysis is conducted to analyze the research trends of these techniques using data from Web of Science and Scopus databases where analysis of ACO, PSO, Bat, Lion, water droplets, and such optimization techniques inspired by nature are studies which use k-means, DB Scan, k-neighbor and other such clustering algorithms. Data is analyzed globally, cluster analysis of related keywords in analyzed along with link strength. Various other experiments are also conducted which help analyze research trends in clustering and nature inspired techniques. [ABSTRACT FROM AUTHOR]

A shaft is used to transmit power from a device that produces power to a gadget that absorbs energy. The strength of the hollow shaft is greater than the equal weight stable shaft. Hollow shafts have a greater polar second of inertia, which permits them to transmit greater torque than stable shafts. Non-Traditional Optimization strategies are efficaciously used to resolve many engineering problems. This paper uses ten non-traditional optimization techniques to discover the weight minimization of a hollow shaft. The comparative outcomes display that the Particle Swarm Optimization outperforms different techniques. [ABSTRACT FROM AUTHOR]

This dissertation concentrates on the application of non-traditional optimization techniques. To solve the optimization problem, the various non-traditional optimization methods are reviewed critically. The methods that are going to review in this survey are Particle Swarm optimization (PSO), Crow Search Algorithm (CSA), Charged system search algorithm (CSSA) and Big-bang big crunch optimization (B-BBCO). These methods are applied to investigate the optimum values. The both linear and non-linear optimization problems are solved to get global solutions. [ABSTRACT FROM AUTHOR]

Cement-based composites have a very broad field of applications but their composition has to be designed with care to meet the desired functional properties. High-temperature resistant mixtures can be stated as a characteristic example in that respect. The standard calcium silicate binder should be replaced usually by high alumina cements which can be supplemented by waste materials or byproducts within the global environmental protection efforts. To reach the optimal properties and composition, advanced optimization techniques are supposed to be used rather than empirical approaches. This paper presents an application of downhill simplex method to optimize the composition of a binder based on calcium alumina cement and supplemented by ceramic powder, super reactive alumina and boehmite powder. The optimization procedure results in a 7% improvement of compressive strength, which reaches ∼117 MPa. Due to the high temperature resistant nature of all substances used (chamotte and corundum as fillers), the proposed mixture is supposed to resist increased temperatures up to 1400 °C. [ABSTRACT FROM AUTHOR]

In the design of positive displacement compressors, the mathematical relationship between design parameters and design objectives is usually impractical and costly to be extracted, making the optimization process becomes a 'black- box' problem. In the available literature, the Bayesian optimization method, compared to other optimization techniques, has been proven as an accurate and efficient method to solve the 'black-box' problem. However, the application of such a method in the design of the rotary compressor has not been discussed in published papers. Hence, the current study is intended to employ Bayesian optimization to geometric design a class of positive displacement compressors, which is known as the limaçon compressor. In this paper, the geometric characteristics of the limaçon compressor are presented, and a function, which incorporates volumetric and geometric aspects, is employed to evaluate the optimization process and to reveal the optimum design scenario as per design requirements. A case study is offered to prove the validity of the presented approach. [ABSTRACT FROM AUTHOR]

The inflation phenomenon is a common factor for today's inventory system as it is increasing with time in the real world. Every industrial corporation is performing the optimization of their current inventory techniques to achieve the highest profit at minimum cost. In this scenario, the major problem, they are facing, is the problem of deterioration which contributes to the increment of the total cost. To reduce this loss of deteriorating items; organizations are investing in preservation technology. In this present paper, an economic order quantity model is considered, in which it is assumed that the demand rate is depending on stock and selling price factors under the effect of inflation over time. The variable holding cost is assumed as a linear function of time. The main intention of this initiated paper is to find the optimal investment in preservation technology in the context of inflation so that total cost will be minimized. The case of without preservation technology and many more cases are also discussed. A numerical example with these special cases has been also discussed. Finally, the sensitivity analysis is also studied to provide several observations. [ABSTRACT FROM AUTHOR]

The paper proposes a new approach to solving the optimization problem for a system of processing technogenic waste of the oil and gas industry. The optimization problem is formulated as an integer mathematical programming problem for the maximum of the objective function considering the constraints. The objective function in the optimization problem is determined using a set of efficiency assessments of environmental safety, resource, and energy consumption in the waste processing system. These assessments are obtained via multi-factorial analysis based on the Data Envelopment Analysis (DEA) method. As the constraints to the problem solution, the limitations on the maximum operating time of the technological plant for the recycling of the waste placed in the storage facility are considered. Special software is developed for the numerical solution of the optimization problem. [ABSTRACT FROM AUTHOR]

Geopolymer-based alternatives to concrete have been mentioned very frequently in the last decade as they conform the topical environmental saving efforts. Utilization of byproducts or waste material make them even more attractive from this point of view. However, the process of a design is not the same like in case of well known concretes which means. it might be difficult to reach the properties desired. This paper therefore aims at the optimization of a geopolymer composition to reach sufficient 7-day compressive strength. Being composed of metashale, water glass, siliceous sand and water, the mixture proposed is supposed to be used in floorings applications. As the optimization technique, the modified downhill simplex method was applied, being tailored to specifics of the mixture design. Proving its effectiveness, the demanded 7-day compressive strength (25.0 MPa) was achieved after the first optimization step (27.8 MPa, due to an combination with an empirical design that exploited authors' experience to set the initial simplex closer to the optimum zone. In the end of the research presented, analytical formulas of the composition were proposed that are supposed to guarantee that the required 7-day compressive strength will be achieved. [ABSTRACT FROM AUTHOR]

Water is essential for all human activities, and that rainfall is one of the critical sources of this precious commodity. Prediction of how much rainfall and when it is most likely to occur will assist concerned officials in planning for its storage and subsequent distribution. Meteorological agencies predict rainfall using statistical or dynamic models. Because of the complexity involved in rainfall prediction and limitations of existing techniques, prediction skill improvement is necessary. Recently, researchers in prediction are using intelligent systems such as Artificial Neural Networks, Fuzzy Inference Systems, Support Vector Machines, and Genetic Algorithms. Many network parameters are required to be selected for the use of these systems, and the choice of the parameters affects the accuracy of the model. Experimental discovery of the parameters is one way, and the other way is to use optimization algorithms. In this paper, various optimization techniques used in computationally intelligent systems are surveyed for rainfall prediction. The optimization techniques mainly used for this purpose are Particle Swarm Optimization, Genetic Algorithm, and Ant Colony Optimization. In all the research articles case study of a certain geographical area for rainfall prediction with a different set of inputs and different forecasting lead times is presented, and hence comparison between the models is difficult. For rainfall prediction, model input selection is equally important to the selection of model parameters as a set of predictors change with increasing or decreasing geographical area and forecast lead time. This paper attempts to identify optimization techniques suitable for medium-range rainfall forecasting over a small geographical area. [ABSTRACT FROM AUTHOR]

The application of complex nonlinear material models of concrete within the numerical simulations is currently the most used way for the purposes of modeling the nonlinear computer response of concrete structures. However, the modeling of nonlinear concrete behavior through the nonlinear material models very often brings with it the major problem. This problem can be specified as a user nescience of a large number of material model parameters which is very common. Nowadays, the solution of this problem is possible with the so called inverse analysis or identification which can be performed via the different methods and approaches. One of the most effective approach for the purposes of the inverse analysis is currently using the combination of sensitivity analysis with optimization algorithms. This mentioned approach is demonstrated within this paper in which the parameters of the material model called the Continuous Surface Cap Model are identified. Material parameter identification is performed on the basis of interaction between nonlinear numerical simulations, statistical and optimization methods and experimental data which are represented by a load-extension curve obtained from the unconfined uniaxial tensile (UUT) test. The final comparison of the experimental data with the numerically-simulated data for optimal parameter values is, of course, also part of this paper. [ABSTRACT FROM AUTHOR]

Teaching-Learning Based Optimization (TLBO) approach is widely accepted in numerous engineering applications. Despite the similarity of this algorithm to other Meta-heuristic techniques in terms of employing a set of solution and stochastic nature, the inspiration of algorithm is unique. This paper aims at discussing the position of TLBO within the do-main of Nature-Inspired Algorithms (NIA). The effectiveness of this simple and robust paradigm in the fields of engineering and sciences is gaining popularity rapidly due to its less complex approach in tuning with the algorithm and other common parameters like population size, number of iterations and stopping criteria, etc. The paper also highlights the development, mechanism, mathematical formulation and algorithm of TLBO. Furthermore, it is concluded that TLBO is a promising variant in the class of stochastic methods that can be extended to find solutions of challenging real-world problems. The review is expected to be useful for the freshers in research area of Optimization techniques. [ABSTRACT FROM AUTHOR]

Optimization of well placement plays a considerable role in the production and maximizing the net present value of the investment of oil field developments. However, the application of the optimization techniques in well placement developments is so complicated because many decision variables, objective functions, and constraints are involved in the well placement optimization case. Furthermore, many computational techniques; conventional and non-conventional, have been utilized to optimize well placement operations. This study displays the advancement of the optimization methods applied in the well placement. Subsequently, the study assorted the applied optimization methods, and it demonstrates the restriction and the range of implementation of each method to gain an appropriate degree of precision and simulation run time. Finally, the paper provides an inclusive review of the well placement optimization methods utilized in the petroleum engineering domain from conventional methods to modern artificial intelligence methods. [ABSTRACT FROM AUTHOR]

Logistics hub location problem is a trending topic both academically and among competing countries. Many authors were interested in selecting this kind of problems and especially in the last 20 years. However, most of the published works were focused on improving and developing solution techniques rather than implementing those techniques into real-life cases. Thus, only limited number of literatures had considered real-life aspects in locating logistics hubs within specific regions. 15 papers were found to be relevant with 3 main families of implemented tools and techniques in several different regions. Tools like spatial modeling and optimization modeling were usually linked with macro level allocation phases, while ordering and ranking criteria are usually applied on micro level phases to select set of alternatives or arrange/sort them according to some determined factors. This paper aims to analyze and benchmark different practical cases, and illustrate the use of different allocation models. [ABSTRACT FROM AUTHOR]

This paper presents a spreadsheet-based mixed-integer nonlinear programming (MINLP) model for cost-optimal construction project scheduling. The model was developed in an MS Excel spreadsheet containing all the input parameters necessary for optimization. The OpenSolver add-in is used to solve the scheduling problem. OpenSolver can solve the optimization task by directly using the computing power of the end user's computer or by converting it into an algebraic modeling language formulation, which is thereupon deployed on an online-based optimization service - NEOS (Network-Enabled Optimization System). With the NEOS server, the end-user can solve more complex problems in less time as they are processed by a supercomputer. The model capabilities are presented on a literature example of a construction project. The model can process project networks with generalized precedence relationships with corresponding lead/lag times. The results obtained by the solver automatically generate a Gantt chart for a cost-optimal construction schedule. [ABSTRACT FROM AUTHOR]

The traditional fixed topology of transmission networks is prone to power congestions, which can cause load curtailment and reduce the integrations of wind power, leading to more dispatching of conventional generators and higher overall dispatch cost. This can be overcome by improving the operation flexibility of transmission networks to enhance wind integration level. This is achieved in this paper by employing the network topology optimization technique to optimize network topology by flexibly altering the connections of lines and busbars. The proposed evaluation framework is generic and applicable on any network. Case studies performed on the IEEE 24-bus test system show that the proposed technique is effective in reducing the three costs considered here, namely the load curtailment cost, generator dispatch cost and wind curtailment cost. [ABSTRACT FROM AUTHOR]

In this modern world, human life has become increasingly reliant on electricity, which serves to be one of the basic needs of human to lead a normal life. As the usage of electricity has increased over the years, the consumers are very much concerned about its consumption rate and the electricity bill generated out of it. Hence monitoring the consumption rate stands first which is very trivial and challenging by designing an efficient energy management system. This paper outlines the survey on energy management systems implemented with artificial intelligence techniques towards finding a viable solution to the aforementioned issue. This study elucidates how artificial intelligent systems are incorporated in different energy management systems to match demand and supply. A comparative analysis of different intelligent techniques with optimization goals, issues and solutions, applied to the energy management systems for effective functioning is also presented. Finally, Home Energy Management System (HEMS) using Binary Particle Swarm Optimization Algorithm (BPSO) is presented. 26% reduction in the daily bill with optimization of HVAC and non-interruptible appliances was attained. Due to the interrupted supply of energy sources, effective storage model is determined to be an alternate viable option owing to technological advancement and capacity of ensuring excellent grid services. Future directions in terms of developing hybrid systems using hybrid energy sources and intelligent systems are also suggested. [ABSTRACT FROM AUTHOR]

End-of-life vehicles (ELV) has became a threat to the environment and could lead to severe environmental pollution. Implementing an efficient closed-loop supply chain system may assist in reducing the hazard caused by ELVs, as end-of-life strategies such as recycling can be conducted to process the used vehicle into raw material. However, the investigation of closed-loop supply chain systems for ELV recycling is still minimal. On that account, this paper investigates the problems in closed-loop supply chain optimization studies for end-of-life vehicle recycling by conducting a content analysis of the published articles in this area of research. The articles were reviewed, then, the main issue in each article was summarized and categorized into different classes, based on common issues found in the closed-loop supply chain optimization literature. In this case, there are five main issues in closed-loop supply chain optimization, which include network design, inventory management, supply chain coordination, production planning, and partner selection. Next, the publishing platform is listed in this paper to determine the main publication outlet in this area of research. Finally, this review provides a summary of the supply chain optimization problems in ELV recycling that will be a beneficial reference for researchers of similar interests. [ABSTRACT FROM AUTHOR]

Osmotic computing is an emerging research field that studies the migration, deployment and optimization of microservices over both edge and cloud infrastructures. In this paper, a systematic method was used to develop an osmotic computing map and to classify the existing papers. Each of 21 relevant studies are classified into categories in three different dimensions (publication type; applied methods and techniques; and paper maturity). The frequencies of each category show what kind of research was prevalent in the past to identify the gaps and new research possibilities. Osmotic computing is a new paradigm with many future research opportunities, such as configuration, networking, security, monitoring, and orchestration of microservices deployed on this complex environment. [ABSTRACT FROM AUTHOR]

This paper presents the mathematical model which considered the facility location problem for local drug stores for psychiatric disorders in Thailand. Due to the increasing number of patients in the hospital, Thailand has initiated several strategies and projects in order to reduce congestion in the hospital. A medicine distribution system is one of the projects to cope with hospital overcrowding. The initiative project especially focuses on the psychiatric patients who live in rural areas which is hard to access psychiatric services. The idea of this project is to select local drug store representatives as medicine distributors, so that psychiatric patients can directly go to pick up their medicine from those local drug stores according to hospital's prescriptions. Therefore, this paper studies on the design of a drug delivery system between hospital and local drug stores, and 7 provinces in Northern Thailand are chosen as a case study. The goal of a mathematical model is to determine the location of local drug store representatives that will be medicine distribution hubs for psychiatric patients. The objective of the model is to minimum number of local drug store representatives that can serve all patients in the system. The model is solved using LINGO 14.0 and the experimental results showed that the optimal solutions can be obtained for all provinces. [ABSTRACT FROM AUTHOR]

The paper discusses a practical School Bus Routing Problem (SBRP) considering interscholastic transport in university contexts. This study focuses on the incorporation of a mixed load composed of multiple schools, to emphasize the need to provide collaborative transportation SBRP solutions in university contexts. A Systematic Literature Review was developed with the purpose of exploring the mathematical optimization models that have been elaborated in the last decade, showing the research opportunities on mixed load situations. Henceforth, the paper presents a real case scenario in a Mexican city to highlight the need for a mathematical model that optimizes the transportation offer of two universities on a common urban zone, that can be replicable in similar conditions. This study exhibits the development of two of the SBRP subproblems: Bus Stop Selection (BSS) and Bus Route Generation (BRG), through the Operations Research Methodology, to construct a solution that maximizes coverage by sharing resources within schools. A sensitivity analysis that focuses on the demand, capacity and time windows variations is conducted to prove the robustness of the approach and to propose an optimal solution for the schools of the specific case. [ABSTRACT FROM AUTHOR]

The development of industrial/agro based composites is a big challenge for engineers and scientists. Much research work is underway to develop metal matrix composites (MMC) and fibre reinforced polymer composites (FRP) for advanced engineering applications. The industry is seeking improvements in these assets. This paper focuses on the review of various composites produced using industrial-agricultural waste as catalysts and the consequences of this strengthening physical and biological environments. Research in this area has shown that the use of this industrial-agricultural waste as a reinforcing material has facilitated the complete operation of the improved composites and helped to reduce the cost of recycling. FRP Composites using agro based materials are developed using hand layup process and tested for Tensile, Flexural and Impact strengths with appropriate ASTM standards. Characterization using Taguchi optimization technique has suggested the use of jute fibre with fibre weight fraction of 10% and poly lactic acid (PLA) as matrix material to be the best combination among the test specimens. The concluding section of the paper discusses the potential uses of this waste as potential engineering materials to strengthen future research work. [ABSTRACT FROM AUTHOR]

Budget-conscious tourists are travellers electing to plan their vacations by taking advantage of pricing variability to minimize the total cost of their desired multi-city vacation. This paper presents the formulation and analysis of a timediscretized linear integer programming (LIP) model to optimize total travel costs for travellers vacationing between major European cities by considering dynamic flight costs and accommodation expenses. The proposed timediscretized network flow model presented is an extension of classical Shortest-Route-Problems (SRP) and is solved using Excel’s SIMPLEX LP algorithm. Flight data collected from the online travel agency Kiwi.com between May 1st, 2022, and May 30th was found to show significant daily price fluctuation, whereas accommodation prices collected from Airbnb.com between May 1st, 2023, and May 13th, 2023, remained stable. This paper includes the model formulation, assumptions, and cost analysis of varying travel instances by rotating the origin city and altering the stay duration at each destination. Three solutions, the generous, greedy, and cost-minimized solutions, were calculated for each travel trip instance. By optimizing the route, the cost can be reduced by upwards of 20.3% and 6.7% relative to the generous and greedy solutions. The model evaluated the impact of varying staying durations on the optimal travel order and was found to be very robust in terms of stay durations such that the optimized cost remained constant. The results of this paper have implications for the global travel industry by lowering the barrier of entry for budget-friendly consumers. [ABSTRACT FROM AUTHOR]

The aerospace and medicinal industries pay close attention to lattice structures. Because they help reduce weight by replacing of the internal solid portion of the blade with different lattice structures. Applying of these technology taking into account for achieving appropriate homogenization between the material, unit cell, and additive manufacturing (AM) technology. Lattice structures with topology optimization have a great potential and flexibility offered to the designers working in the area of designing lightweight structures as well as high strength at the same time compared with solid form structures. The main goal of this paper is to investigate the designing of graded density structures of various lattice designs for dense materials with thermo-mechanical behavior of numerous lattice-based configurations (such as Gyroid and X-cell were applied for a turbine blade. The main goal is for reducing the weight of the turbine blade to achieve the best mechanical, thermal properties. [ABSTRACT FROM AUTHOR]

Cloud computing is an emerging technology, in which users demand the computing resources such as software, storage, etc based on pay-per-use services. Since cloud computing is widely expanding and there is also an increase in the number of users. Therefore, energy consumption and resource utilization are serious concerns in complex cloud systems. To this end, Virtual Machine (VM) placement or virtualization plays an important role in allocating the resources and power to their users. For efficient allocation, multiple factors such as migration overhead, Service Legal Violation (SLA), number of migrations, etc. has been considered. Since the problem is challenging, several optimization algorithms have been considered to solve this issue and have been implemented using different methods. Therefore, this paper presents a review to discuss the role of optimization techniques for VM migration and allocation. The current paper has been discussed and a review of existing techniques post-2015-2022 has been presented. Further, a detailed comparison has been made to illustrate the important factors utilized in the past. [ABSTRACT FROM AUTHOR]

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The quantum and reversible circuits represent a promising replacement of conventional computers in many applications in the future. The transition between conventional computing and this alternative is indeed a huge paradigm shift. During the transition, embedding of conventional Boolean logic functions and expressions within quantum and reversible logic circuits has to be considered, because conventional computing is generally based on Boolean algebra. This embedding comes with a serious drawback of additional increase in the problem complexity represented by additional constant inputs, the so-called ancillary lines. An algorithmic optimization technique is suggested in this paper to reduce the number of these ancillary lines. With this algorithm embedding of Boolean expressions within reversible/quantum circuits comes out with minimal increase in circuit cost and gate count in the optimized circuits. [ABSTRACT FROM AUTHOR]

The paper discusses a practical School Bus Routing Problem (SBRP) considering interscholastic transport in university contexts. This study focuses on the incorporation of a mixed load composed of multiple schools, to emphasize the need to provide collaborative transportation SBRP solutions in university contexts. A Systematic Literature Review was developed with the purpose of exploring the mathematical optimization models that have been elaborated in the last decade, showing the research opportunities on mixed load situations. Henceforth, the paper presents a real case scenario in a Mexican city to highlight the need for a mathematical model that optimizes the transportation offer of two universities on a common urban zone, that can be replicable in similar conditions. This study exhibits the development of two of the SBRP subproblems: Bus Stop Selection (BSS) and Bus Route Generation (BRG), through the Operations Research Methodology, to construct a solution that maximizes coverage by sharing resources within schools. A sensitivity analysis that focuses on the demand, capacity and time windows variations is conducted to prove the robustness of the approach and to propose an optimal solution for the schools of the specific case. [ABSTRACT FROM AUTHOR]

Traditional file systems, while fundamental in data management, often grapple with the demands of ever-evolving data workloads, varying access patterns, and escalating volumes of information. In response to these challenges, the integration of artificial intelligence (AI) and machine learning has emerged as a transformative approach to optimize file systems. This paper outlines the importance of file system optimisation in the contemporary data landscape and then provides potential solutions using AI algorithms. AI-driven file systems optimization has delivered substantial performance improvements, characterized by enhanced Input/Output Operations Per Second (IOPS), reduced latency, and increased throughput. Artificial intelligence-driven solutions in cloud storage environments address scalability and data management challenges associated with cloud storage, extending the applicability of AI techniques. [ABSTRACT FROM AUTHOR]

Increasing load in a traditional transmission system has led to the increase of current flow which also increases the I2R loss leading to monetary losses in the power system community. This will also affect the economic status of the utility which may require the utility to take possible appropriate remedial action. Thus, a compensation process can be performed to alleviate the high current flow which in turn reduces the monetary losses that can be experienced by the utility. This paper presents Multi Load Variability for Composite Compensation in Loss Control Scheme. In this study a new optimization technique termed Integrated Cloning Accelerated Mutation Evolutionary Programming (ICAMEP) is proposed to optimize the location and sizing for composite compensation in power transmission system for loss control scheme. Several variations in the loading variability were conducted to evaluate the effect of reactive power changes which influence loss value in power system. The effectiveness of the proposed ICAMEP algorithm was compared with respect to the traditional evolutionary programming (EP) and artificial immune system (AIS) optimization techniques for performance comparisons. Results from ICAMEP outperformed AIS and EP in terms of achieving lower transmission loss through the validation process in the IEEE 30-Bus Reliability Test System (RTS). This will benefit power system operators and planners for their expansion planning efforts. [ABSTRACT FROM AUTHOR]

The paper is devoted to the consideration of the possibilities of managing complex energy systems. An approach related to the optimal planning of various types of energy services is shown. The paper proposes to apply approaches based on queuing systems. The divisions that will manage the energy flows in the distribution power system are considered from the point of view of using multiple flows of homogeneous events. The considered approaches will be used in the course of the formation of a simulation modeling scheme and optimization of parameters in the power system. The possibilities of energy monitoring are considered. When choosing indicators, optimization modeling is used. Further, expert analysis is seen as a very effective approach when analyzing a variety of promising options. As a result, the results are presented that demonstrate the effectiveness of the selected indicators. [ABSTRACT FROM AUTHOR]

This paper focuses on improving the flow efficiency of a production line using simulation software. The aim of this study is to simulate the actual flow using advanced simulation software and improvement in productivity was done by increasing the utilization of the underutilized facilities. Simulation modeling was used to evaluate the equipment and manpower performance for the manufacturing system. Each individual facility in the flow has to be analyzed carefully and allocated to stations in a balanced way over the available workstations. The paper is divided into 3 parts, the first part consists of data collection and distribution selection in which selection of best data distribution was carried out, the second part is about simulation modeling, analysis of the simulated model in which underutilized facilities were identified, and cause of the underutilization was identified, in the third part, the results of the model were analyzed, validated and finally, best modifications for the flow were suggested to improve the efficiency of the flow. And finally, Cost-benefit Analysis was performed to determine the tenure of recovering the total investment. Based on the simulation results, the top under-utilized facility was M5, M6, M7, M9, and M3 and the existing production was struggling to meet the customer demand due to machine shortages. So, to overcome this underutilization problem, we suggested some modifications by generating different scenarios in the flowline described in the proposed improvement section. [ABSTRACT FROM AUTHOR]

In order to solve the ill-posed problem in super-resolution image reconstruction, this paper proposes an adaptive regularization way use sparse representation. We build a new L1/2 non-convex optimization model and apply reweighted L2 Norm for the adaptive algorithm in this paper. Experimental results show the significant effect in denoising and preserving edge details. It outperforms some traditional methods in the value of peak signal to noise ratio and structural similarity [ABSTRACT FROM AUTHOR]

The constant changes in consumer demand, competition and challenges with the current pandemic situation would change the dynamics of the supply chain. Thus, in order to remain competitive, the challenge would be the ability to adjust by taking the right decisions at the right time in all nodes of the supply chain. Periodic optimization of the warehouse can contribute to this immensely. But, this needs to be done considering the limitations of the infrastructure of the facility as well as using a method that can be easily and timely used. Hence, the objective of this paper is to develop a model to optimize the warehouse operation using Generalized Reduced Gradient (GRG) Nonlinear modeling approach considering the existing constraints. The approach of the study would be to identify the variables and constraints involve in re-designing a warehouse, the objective function and determining the optimization model and finally evaluating the impact of the optimization model based on the changes in the variables and constraints in order to facilitate timely decision making. The proposed model can be periodically used by practitioners to analyze the different outcomes based on the changes in the variables. [ABSTRACT FROM AUTHOR]

In this Research paper removal of faults associated in real life situation related to 66KV substations at Guru Gobind Singh Super Thermal Power Plant situated at Ropar. The power system data is taken at different time periods under various fault conditions and voltage sag/swell both are mitigated from the real time data by using D-STATCOM. When Grey Wolf Optimization technique is associated with D-STATCOM, it has shown more efficient results for voltage sag/swell. Later, a FACT controller device called UPQC has been used to overcome the same problem of voltage fluctuation in distribution system. The results have shown that UPQC is more efficient in comparison to D-STATCOM and GWO as values obtained through MATLAB SIMULINK in case of UPQC are closer to normal values. [ABSTRACT FROM AUTHOR]

In this paper, we highlight the problems related to UN SDG number 7 (providing affordable, reliable and sustainable energy) by analyzing the current infrastructure that provides uninterrupted reliable energy supply to technologically isolated and hard-to-reach territories in the Russian Federation. We explain the different components of Hybrid Energy Complexes that include various elements of renewable energy sources, we analyze the factors influencing the efficiency of these complexes and we optimize the composition of these complexes by using a mathematical simulation program that predicts various output indicators. By comparing our estimates with actual data in Ust-Kamchatsk, we are able to highlight the accuracy of our program as well as make conclusions on how its future implementation may have a positive effect on both ecological and economic factors. [ABSTRACT FROM AUTHOR]