Optimization Models Research Articles

Research on Multi-Parameter Optimization of Conical Roller Line Processing Technology Based on Satisfaction Function

In the process of conical roller line processing, there will be problems such as low precision of processing parameters, long processing time, low utilization rate of machine tools, high rejection rate, and high processing cost, which will lead to low production efficiency. In order to solve this problem, it is necessary to iteratively optimize the size parameters, including inner diameter, ovality, and taper. By obtaining the optimal parameter combination, the size parameters in the production process are kept consistent, that is, the accuracy and performance of the workpiece during the processing are guaranteed so as to avoid the problem of quality difference, improve the production efficiency and reduce the processing cost. In view of the fact that there are often some constraints on the accuracy and efficiency in the machining process of tapered roller lines, how to optimize the parameters affecting the accuracy and the parameters affecting the efficiency to achieve balance between accuracy and efficiency in order to better meet the needs of customers for multi-objective optimization of the machining process has become the focus of research. Based on the existing research, this paper explores the multi-parameter optimization modeling and application in the machining process of tapered roller lines by constructing a satisfaction function, and then uses a genetic algorithm to iteratively search for the optimal solution by simulating natural selection and the genetic mechanism. Based on Python software v3.12, the production process of tapered roller bearings is simulated. The AHP analytic hierarchy process and CRITIC weight method are used to redistribute the parameter weights respectively. After eight iterations, it is concluded that the weight value assigned by the AHP analytic hierarchy process makes the satisfaction function value reach the best value of 0.99795 and tend to converge stably. The optimized parameter configuration significantly improves the machining accuracy and production efficiency of the tapered roller line. The optimal parameter combination is obtained: inner diameter: 9.9982 mm, ovality: 0.7 mm, taper: 0.5 degrees, production efficiency: 101.0.97 piece/h. In order to verify the optimization effect, the single value (X) and moving extreme difference (Rs) control charts in the measurement value control chart are used to analyze and verify the tolerance values of important parameters in the processing technology of the tapered roller line. The results show that the data points are all within the control limit, indicating that the processing process is in a statistical control state.

Classified dataset, regression and machine learning modeling for prediction of phase transformation temperatures in steels

The prediction of the characteristic Martensite Start (Ms) temperature and Austenitic Nose Tip Temperature (ANTT) in steels is of scientific and technological importance; however, it faces significant challenges due to multiphysical complexity.In this study, we introduced a structured framework for data classification and hierarchical iterations aimed at predicting Ms (Martensite start temperature) and ANTT (Austenite non-transforming temperature). This framework was incorporated into two optimization models, leading to enhancements in accuracy, extrapolation capabilities, and generalization performance. First, we classified the collected Ms datasets hierarchically based on the alloying elements presented in steels, including carbon, austenite stabilizers, non-austenitization elements, and data credibility. Regression analyses of Ms temperatures concerning chemical compositions were then carried out using phenomenological variables from binary systems to multi-component systems in alignment with the spirit of CALPHAD modeling, which is renowned for its robust extrapolation abilities. By iteratively fitting the hierarchically classified datasets and implementing hierarchical iterations, we developed the CALPHAD-guided phenomenological variable (CGPV) Ms regression model. This model achieved improved accuracy levels, with R2 values of 0.9 for training and 0.87 for testing, surpassing most conventional regression models that do not account for compositional interactions. Furthermore, the CALPHAD-guided machine learning (CGML) model, constructed based on the classified datasets and hierarchical iterations but without utilizing phenomenological variables, demonstrated strong performance with R2 values of 0.98 and 0.86 for training and testing, respectively. The CGML model was demonstrated not only to reliably filter out problematic data in a dataset but also to unveil the unnoticed coupling between carbon and other alloying elements on Ms. Finally, the CGML method has been readily transferred to predict ANTT with high accuracy as well.

Experimental study on surface optimization of dosage in magnetic coagulation process for enhancing primary treatment of urban sewage

This work aims to investigate the minimum dosage required to achieve optimal removal of carbon, nitrogen, and phosphorus in the Chemical Enhanced Primary Treatment (CEPT) process using magnetic coagulation technology. A response surface methodology was adopted to generate multiple regression models to optimize the dosages of three types of coagulation, i.e., polyaluminum chloride (PAC), polyacrylamide (PAM), and magnetic medium Fe3O4 particles (MP), based on the removal rates of chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and total phosphorus (TP). The results showed that the regression models could predict results well with actual engineering results. Compared to the traditional coagulation process, the synergistic enhancement of PAC+PAM+MP in the CEPT process significantly improved the treatment effects. Based on the COD removal rates, PAC performed better than MP, followed by PAM; likewise, for the NH3-N and TP removal rates, PAC outperformed PAM, followed by MP. The optimized PAC, PAM, and MP dosages were 135.87, 1.51, and 108.36 mg/L, respectively, reaching 92.27 % COD, 71.98 % NH3-N, and 91.33 % TP removal rates with less than 5 % errors compared to the experimental verification results. These optimization models offer a practical and cost-saving reference for enhancing the primary treatment effect on carbon, nitrogen, and phosphorus removal using PAC+PAM+MP.

Iterative coupling of a fundamental electricity market model and an agent-based simulation model to reduce the efficiency gap

Fundamental optimization models can determine normative cost-optimal systems following theoretical assumptions of markets with perfect competition and perfect foresight. However, in real markets, deviations from ideal assumptions need to be considered for designing efficient policies: they may lead to higher costs than theoretically expected and contribute to the so-called “efficiency gap”. Agent-based simulation models can consider market inefficiencies but are not ideally suited for determining normative systems. This work aims to reduce the efficiency gap between theoretically cost-optimal system configurations and their implementations in non-perfect markets by coupling a fundamental electricity market model and an agent-based model. A fully automated iterative coupling is demonstrated, and its convergence behavior is investigated for two coupling parameters. Fast convergence is observed when integrating peak capacity usage from the previous agent-based simulation into the fundamental model. However, the efficiency gap is reduced only to limited extent as no information on hourly dispatch is transferred between the models. Conversely, when using hourly storage dispatch as coupling parameter, it takes longer to achieve convergence. In return, the efficiency gap is reduced to larger extent. The proposed iterative coupling thus allows the identification of realizable cost-optimal systems, which means that they are achievable under non-perfect market conditions.

A Review of Data-Driven Methods in Building Retrofit and Performance Optimization: From the Perspective of Carbon Emission Reductions

In order to reduce the contribution of the building sector to global greenhouse gas emissions and climate change, it is important to improve the building performance through retrofits from the perspective of carbon emission reductions. Data-driven methods are now widely used in building retrofit research. To better apply data-driven techniques in low-carbon building retrofits, a better understanding is needed of the connections and interactions in optimization objectives and parameters, as well as optimization methods and tools. This paper provides a bibliometric analysis of selected 45 studies, summarizes current research hotspots in the field, discusses gaps to be filled, and proposes potential directions for future work. The results show that (1) the building-performance optimization (BPO) process established through physical simulation methods combines the site, retrofit variables, and carbon-related objectives, and the generated datasets are either directly processed using multi-objective optimization (MOO) algorithms or trained as a surrogate model and iteratively optimized using MOO methods. When a sufficient amount of data is available, data-driven methods can be used to develop mathematical models and use MOO methods for performance optimization from the perspective of building carbon emission reductions. (2) The benefits of retrofits are maximized by holistically taking environmental, economic, and social factors into account; from the perspectives of carbon emissions, costs, thermal comfort, and more, widely adopted strategies include improving the thermal performance of building envelopes, regulating HVAC systems, and utilizing renewable energy. (3) The optimization process based on data-driven methods, such as optimization algorithms and machine learning, apply mathematical models and methods for automatic iterative calculations and screen out the optimal solutions with computer assistance with high efficiency while ensuring accuracy. (4) Only 2.2% and 6.7% of the literature focus on the impacts of human behavior and climate change on building retrofits, respectively. In the future, it is necessary to give further consideration to user behaviors and long-term climate change in the retrofit process, in addition to improving the accuracy of optimization models and exploring the generalization and migration capabilities of surrogate models.

Diverse decarbonization pathways under near cost-optimal futures

Energy system optimization models offer insights into energy and emissions futures through least-cost optimization. However, real-world energy systems often deviate from deterministic scenarios, necessitating rigorous uncertainty exploration in macro-energy system modeling. This study uses modeling techniques to generate diverse near cost-optimal net-zero CO2 pathways for the United States’ energy system. Our findings reveal consistent trends across these pathways, including rapid expansion of solar and wind power generation, substantial petroleum use reductions, near elimination of coal combustion, and increased end-use electrification. We also observe varying deployment levels for natural gas, hydrogen, direct air capture of CO2, and synthetic fuels. Notably, carbon-captured coal and synthetic fuels exhibit high adoption rates but only in select decarbonization pathways. By analyzing technology adoption correlations, we uncover interconnected technologies. These results demonstrate that diverse pathways for decarbonization exist at comparable system-level costs and provide insights into technology portfolios that enable near cost-optimal net-zero CO2 futures.

Structural optimization design and compression performance of porous titanium prepared by laser powder bed fusion

Porous implants composed of periodically-repeating porous units have been applied in orthopedic surgeries to replace damaged bones. An important requirement for bone implants is to find a balance between biological and mechanical properties, that is, to ensure high pore connectivity and a certain strength. In this work, center-supported and perimeter-supported porous optimization models were established by a swarm intelligence optimization algorithm, and the corresponding titanium porous samples were fabricated by laser powder bed fusion (L-PBF). The effects of cell topology and porosity on the compressive behavior of L-PBF titanium porous structures were systematically investigated by a combination of compression simulation and experiments. Compression simulation shows that the deformation failure behavior of center-supported and perimeter-supported porous structures consists of three stages: elastic deformation, plastic deformation and compression collapse, showing the characteristics of elastic-plastic porous materials. According to the compression test results, the compressive strength of center-supported and perimeter-supported structure samples are 151–188 MPa and 178–196 MPa, respectively, and the compressive stability of the perimeter-supported structure is better than that of the center-supported structure. The porosity error of center-supported structure is 1.6%–2.9%, and the porosity error of perimeter-supported structure is 3.1%–4.5%. The compressive strength of the same type of structure decreases with the increase of the porosity of the porous structure. The simulation/experimental compressive strength errors of center-supported and perimeter-supported structures are less than 6% and 5.9%, respectively. The constructed quasi-static compression model has certain guiding significance for the study of the fracture of the titanium implants under external force.

Strategic implementation of variable-thickness insulation layers for stratospheric airships

Excessive heat transfer from photovoltaic systems to airship hulls poses a threat to flight safety. To address this issue, a theoretical model was developed that combines thermal, heat transfer, and optimization models for airships with variable-thickness insulation layers. The study investigates the daily thermal behavior of stratospheric airships, focusing on the effects of thermal conductivity and insulation thickness. To balance weight reduction with insulation effectiveness, temperature constraints were used, and insulation thickness was optimized across different sections of the airship using a simulated annealing algorithm (SAA). Results indicate that adjusting thermal conductivity and insulation thickness can mitigate overheating and overpressure, albeit with a reduced solar energy output. Notably, variable-thickness insulation achieved a 9.7 % weight reduction at 40°N latitude, enhancing both photovoltaic system design and insulation material selection for stratospheric airships.

Optimization in Visual Motion Estimation.

Sighted animals use visual signals to discern directional motion in their environment. Motion is not directly detected by visual neurons, and it must instead be computed from light signals that vary over space and time. This makes visual motion estimation a near universal neural computation, and decades of research have revealed much about the algorithms and mechanisms that generate directional signals. The idea that sensory systems are optimized for performance in natural environments has deeply impacted this research. In this article, we review the many ways that optimization has been used to quantitatively model visual motion estimation and reveal its underlying principles. We emphasize that no single optimization theory has dominated the literature. Instead, researchers have adeptly incorporated different computational demands and biological constraints that are pertinent to the specific brain system and animal model under study. The successes and failures of the resulting optimization models have thereby provided insights into how computational demands and biological constraints together shape neural computation.

Optimal energy management via day-ahead scheduling considering renewable energy and demand response in smart grids

The energy optimization in smart power grids (SPGs) is crucial for ensuring efficient, sustainable, and cost-effective energy management. However, the uncertainty and stochastic nature of distributed generations (DGs) and loads pose significant challenges to optimization models. In this study, we propose a novel optimization model that addresses these challenges by employing a probabilistic method to model the uncertain behavior of DGs and loads. Our model utilizes the multi-objective wind-driven optimization (MOWDO) technique with fuzzy mechanism to simultaneously address economic, environmental, and comfort concerns in SPGs. Unlike existing models, our approach incorporates a hybrid demand response (HDR), combining price-based and incentive-based DR to mitigate rebound peaks and ensure stable and efficient energy usage. The model also introduces battery energy storage systems (BESS) as environmentally friendly backup sources, reducing reliance on fossil fuels and promoting sustainability. We assess the developed model across various distinct configurations: optimizing operational costs and pollution emissions independently with/without DR, optimizing both operational costs and pollution emissions concurrently with/without DR, and optimizing operational costs, user comfort, and pollution emissions simultaneously with/without DR. The experimental findings reveal that the developed model performs better than the multi-objective bird swarm optimization (MOBSO) algorithm across metrics, including operational cost, user comfort, and pollution emissions.

Whether ARIMA-based Portfolio Can Beat the Market Index

Portfolio optimization is a long-term topic in the financial field, which can maximize returns while minimizing risks. It is widely used in production and daily life, such as stock investment, production optimization, and engineering models. This article selects data from 10 stocks, namely KO, PG, PEP, CL, MDLZ, STZ, PM, KMB, GIS, and K, from January 24, 2024, to June 14, 2024. Firstly, this article uses the ARIMA model to learn the first 70% of the data and predict the last 30% of the data, and then uses portfolio strategy to evaluate the results. This article obtained the maximum Sharpe ratio model on the Monte Carlo effective boundary. By combining the maximum Sharpe ratio model with the SP 500 index within the same time frame, this article concludes that the market performs better. This article provides different portfolio allocation strategies for risk averse investors seeking stable positive returns in turbulent markets.

Hybrid machine learning framework for multi-well trajectory optimization in an unconventional field

The well placement problem in the petroleum industry is usually solved by integrating an optimizer with a reservoir simulation model. This is a time-consuming approach requiring thousands of simulation runs depending on the complexity of the reservoir system. This paper introduces two well placement optimization models: a Fast-Marching Model (FMM) based on fluid flow physics and a Hybrid Model integrating reservoir simulations and FMM results with a gradient boosting algorithm. These models prioritize speed and accuracy when integrated with genetic algorithm optimization, demonstrated using a synthetic unconventional field. From 290 randomly selected locations across the synthetic unconventional field, simulation runs were performed to determine cumulative gas production and used as ground truth. Using reservoir properties at these locations as inputs, Relative Opportunity Ranking (ROR) maps were generated using the FMM and Hybrid Model approaches. The ROR maps were then linked with a genetic algorithm to determine optimal well locations, iteratively updating ROR maps with penalty maps until all wells were appropriately placed. Results indicated that the standalone FMM generated ROR maps were highly correlated with the simulation results. Time complexity analysis revealed that both models were significantly faster than traditional methods, with the FMM independent of simulation and the Hybrid Model requiring only about 290 simulation runs. Ultimately, these models have shown tremendous potential for integration into current reservoir engineering workflows, reducing decision-making times in both greenfield and brownfield scenarios.

Facilitating Circular Transition in the Construction Industry: Optimizing a Prefabricated Construction Site Layout Using a Novel BIM-Integrated SLP-GA Model

Prefabricated construction is being developed as one of the pathways toward circularity in the construction industry. However, compared to traditional cast-in-place construction methods, the design of prefabricated construction site layouts presents unique challenges, such as managing the space for prefabricated components and lifting equipment, and coordinating the precise timing between off-site fabrication and on-site assembly. Existing research has primarily focused on traditional cast-in-place construction, leaving room for improvement in optimisation models for prefabricated site layouts. This study develops a BIM-based System Layout Planning-Genetic Algorithm (SLP-GA) model specifically tailored for prefabricated construction site optimisation. The proposed model improves the accuracy and visualisation of layout planning through BIM technology, enabling dynamic adjustments and real-time data integration. It also incorporates genetic algorithms to address complex multi-objective optimisation problems, avoiding local optima and overcoming the limitations of traditional SLP methods that rely on subjective judgements. Unlike previous studies that do not consider secondary handling, the optimisation objectives of this study focus on minimising material handling costs associated with secondary handling and maximising comprehensive relationships, including efficiency, safety and space utilisation. The application of this model in a case study shows a reduction in logistics costs of 8.58% and an improvement in comprehensive relationships of 11.61%, indicating significant improvements. This research advances optimisation methods for prefabricated construction site layouts, enriches optimisation objectives by considering secondary handling, and provides practical guidance for improving the efficiency and effectiveness of prefabricated construction projects.

Sustainable Concreting: Optimization Modelling of the Strength Properties of Bio-Self Compacting Concrete Incorporating Sporosarcina Pasteurii, Calcined Clay and Limestone Powder

Sustainable concreting is prerequisite for infrastructural development in developing countries so as to meet up with the sustainable development goal of adequate mass housing and other critical infrastructure. Thus, research is ever ongoing aimed at developing cheaper and more durable concrete via the incorporation of bio-based by-products in concrete to improve its properties, as well as optimizing the quantities of these secondary materials for maximum and optimal concrete production. One such revolutionary concrete that is yet to find full application in the developing world is self-compacting concrete, because of the cost and attendant environmental effects. There is thus a need to arrive at optimal materials quantities that can maximize concrete properties without recourse to many trial and error experimentations that are both time and resources consuming. The application of modelling tools in concrete technology aids in the optimization of concrete constituents for optimal self-compacting concrete performance. This research uses optimization techniques to optimize the bacteria dosage as well as model the Compressive and Tensile strength properties of a calcined clay and Limestone powder blended ternary self-compacting concrete using sporosarcina pasteurii as Microbial induced calcite precipitation agent and calcium lactate as nutrient source. The Bacteria was incorporated into the concrete at a bacterial content of 1.5x108cfu/ml, 1.2x10 cfu/ml and 2.4x109cfu/ml corresponding to the McFarland turbidity scale of 0.5, 4 and 8 while the nutrient (calcium lactate) content was 0.5, 1.0 and 2.0% by weight of cement for each bacterial content. The Compressive strength and tensile strengths at 28 days were determined and the results used for both the model development, strength optimization and model validation, with the strengths as the dependent variable (y) and the bacterial content corresponding to a McFarland scale of and calcium lactate content as the independent variables, X1 and X2 respectively. The results show an improvement in the compressive strength from 32N/mm2 to 45.2N/mm2 at the optimal bacterial and nutrient content of 1.2x10 cfu/ml and 0.5% respectively, and tensile strength from 4.01N/mm2 to 5.0N/mm2. Also, the non-linear regression models proved adequate for optimizing the bacterial content for optimal self-compacting concrete performance. Journal of Engineering Science 15(1), 2024, 11-19

A Review on the Optimization of Irrigation Schedules for Farmlands Based on a Simulation–Optimization Model

Agriculture is the most important sector that is consuming water resources. In the context of global water scarcity, how to use limited water resources to improve water use efficiency in agriculture or achieve maximum crop yield and fruit quality is of great significance for ensuring food and water security. Optimizing irrigation schedules is an effective measure to improve water use efficiency, where crop models also play an important role. However, there is little research summarizing the optimization of irrigation schedules based on crop models. This study provides a systematic review on how to optimize irrigation schedules based on crop models and simulation–optimization models. When optimizing irrigation schedules based on crop models, the selected models are usually mechanistic agro-hydrological models. Irrigation scenarios and optimization objectives are mainly focused on both crop and water aspects, such as maximizing crop yield, fruit quality, water productivity, and irrigation water productivity. Minimizing crop water consumption and total irrigation amounts serve as optimization objectives, and irrigation quantity, irrigation frequency, and irrigation interval serve as decision variables. In saline areas or low fertilizer utilization areas, the optimization objectives and decision variables also involve some indicators related to salt and nitrogen, such as the maximum desalination rate, minimum salt content, fertilizer utilization efficiency, nitrogen fertilizer productivity, nitrogen fertilizer utilization efficiency, nitrogen leaching rate, which serve as the optimization objectives, and the irrigation water salinity, or fertilization schedules serve as the decision variables. When optimizing irrigation schedules based on simulation–optimization models, the models have mainly been upgraded from water-production function to crop mechanism models. In addition, optimization algorithms have been upgraded from traditional optimization techniques to intelligent optimization algorithms. Decision-making techniques are used to make decisions on optimization results. In addition, the spatial scale for the optimization problem of irrigation schedules was developed from fields to regions, and the time scale was developed from the growth stage, beginning with months, and shortening to ten days, then to a day, and then to an hour. This study also provides a detailed introduction to widely used optimization algorithms, such as genetic algorithms, as well as decision techniques. At the same time, it is proposed that the future should focus on improving crop models and analyzing uncertainty in research on irrigation schedule optimization, which is of great significance for the precise regulation of irrigation schedules.

A hybrid data-driven approach for state of health estimation in lithium-ion batteries

ABSTRACT The assessment of the state of health (SOH) in lithium-ion batteries is essential for ensuring safe operation and implementing scientific management practices. This paper proposes a novel hybrid data-driven approach for accurately estimating the SOH of lithium-ion cells. Initially, the sparrow search algorithm (SSA) was implemented to optimize the structural configuration of a hybrid long-short-term memory neural network and convolutional neural network model. Second, four health indicators, namely, constant current charging time (CCCT), constant current charging capacity (CCCC), discharging time (DT), and discharging capacity (DC), were identified as inputs to the estimation model by Pearson correlation analysis. The CALCE battery dataset is utilized for conducting experiments, yielding remarkably low average values of root mean square error (RMSE) and mean absolute error (MAE), standing at merely 0.6737% and 0.5286%, respectively. And comparing with GRU, TCN, CNN, LSTM, and PSO optimization models, the proposed method has higher SOH estimation accuracy, which is more than 11% better than that. The robustness of three additional kinds of batteries is tested to validate their performance under varying current rates and temperatures. The results demonstrate that the accurate estimation of SOH, and the estimated values’ relative errors for each cycle of the three different types of batteries are all basically within 1%, with their maximum average RMSE are only 0.4840%, 0.3609%, and 0.5681%, respectively, and their maximum average MAE are only 0.3900%, 0.2828%, and 0.4149%, respectively.

Home-based care and center-based care: From being alternatives to being synergistic. Optimization models to support flexible care delivery

The delivery of health care services, especially for patients with chronic conditions and/or requiring cyclical treatment, can be accomplished by resorting to two organizational models: center-based care and home-based care. In developed countries, government pressure to reduce healthcare spending and the COVID-19 pandemic have led to the spread of home-based care. This type of care is proven to outperform the center-based one in terms of cost-effectiveness, patient satisfaction, and adherence to treatment guidelines. However, even for treatments usually suitable to be delivered at home, the patient's health status or other constraints may make it inappropriate to deliver service at the patient's home. This calls for a new organizational model, referred to as flexible care, where home-based and center-based care are not seen as mutually exclusive models but as options that can be activated according to the patient's and provider's needs. This paper presents a novel network-based deterministic optimization model and two matheuristics to address a scheduling problem typically faced by providers adopting a flexible care model. The model considers a provider relying on a treatment room with a fixed number of medical chairs, a fleet of vehicles, and a team of operators. It allows for determining on which days of the planning horizon, in which setting (home or center), and by which operator each patient will be treated. The model takes into account patients’ preferences and considers two objective functions: minimizing provider costs and patients’ travel time. In addition, we propose a two-stage mixed-integer stochastic programming model with recourse actions. This model allows incorporating the uncertainty due to the occurrence of adverse events. Adverse events are sudden changes in the patient's condition randomly happening at a specific point in the planning horizon. These events render the patient unsuitable for home care and require them to be visited at the center from that moment onward. The models have been inspired by a real case and tested on multiple random instances.

Structural and Parametric Synthesis of a Hybrid Repetitive Control System for Uncertain Multi-Mode Plant

Article deals with the problem of constructing a hybrid adaptive-robust repetitive control system for one class of a MIMO plants which operating with structural and parametric uncertainty and constant action of an external disturbances. A procedure for structural and parametric synthesis of a control system is proposed. At the structural synthesis stage with the help of hyperstability criterion the authors are developing a decentralized combined controller. After that using the method of continuous models the authors are built a continuous-discrete control system. The main feature of a control loop structure synthesis stage is to ensure the validity of V. M. Popov’s integral inequality. For this purpose, the authors determining the special estimates that guarantee positivity for nonlinear non-stationary part of the system under consideration. At the system’s parametric synthesis stage the authors are use the environment of engineering and technical calculations "Matlab — Simulink" to perform optimization modeling of the continuous and hybrid repetitive control systems. This action is carried out using one of the functional optimization methods — a genetic algorithm. Authors use the criterion of generalized operation for automatic control systems to form the functionality for assessing the quality of the proposed system. At the simulation stage initially for continuous repetitive system it was searched its controller parameters which ensure the minimum value of the specified functional. Then, with a given discretization step of the control loop elements, the hybrid periodic system was optimized in order to improve the quality of its operation.

Vehicle-to-Grid: quantification of its contribution to security of supply through the F-Factor methodology

The increasing adoption of electric vehicles is expected to substantially raise electricity demand. This could require significant grid investment to maintain secure electricity supply, which has traditionally been provided through infrastructure upgrades. The potential of smart technologies like Vehicle-to-Grid (V2G) to contribute to security of supply has prompted the need to quantify their impact. We hypothesize that the F-Factor methodology can effectively quantify V2G’s security of supply contribution. Applying F-Factor analysis to V2G through optimization modeling and sensitivity studies, we find that key parameters like V2G charger ratings, EV battery capacities, and load profile peakiness significantly influence the results. We conclude that the F-Factor provides a valuable tool for assessing V2G’s potential to enhance security of supply, with implications for more efficient grid planning in the context of transport electrification.

Robust pricing and inventory decisions in ship‐from‐store omnichannel operations

AbstractThis work studies the deployment of the ship‐from‐store omnichannel strategy and the pricing and inventory decisions for an online retailer. Robust optimization models are constructed for the online‐only and the ship‐from‐store modes under a budgeted uncertainty set. The ARIMA model is used to predict the parameter values of the budgeted uncertainty set using historical demand data. The closed‐form optimal solution for the online‐only mode is obtained. The robust counterpart model for the ship‐from‐store mode is converted to a mixed integer quadratic programming model. Numerical studies are conducted to validate the theoretical results and to verify the effectiveness and practicality of the developed robust optimization solution approach. The results show that adopting a ship‐from‐store strategy may hurt the retailer's profit if a significant proportion of consumers are time‐sensitive with high travel cost. The ship‐from‐store strategy is optimal if it significantly boosts market growth.