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Introduction: The development of science and technology has driven rapid changes in the social environment, especially the rise of the big data environment, which has greatly increased the speed at which people obtain information. However, in the process of big data processing, the allocation of information resources is often unreasonable, leading to a decrease in efficiency. Therefore, optimizing task scheduling algorithms has become an urgent problem to be solved. Methods: The study optimized task scheduling algorithms using artificial intelligence (AI) methods. A task scheduling algorithm optimization model was designed using support vector machine (SVM) and K-nearest neighbor (KNN) combined with fuzzy comprehensive evaluation. In this process, the performance differences of different nodes were considered to improve the rationality of resource allocation. Results and Discussion: By comparing the task processing time before and after optimization with the total cost, the results showed that the optimized model significantly reduced task processing time and total cost. The maximum reduction in task processing time is 2935 milliseconds. In addition, the analysis of query time before and after optimization shows that the query time of the optimized model has also been reduced. The experimental results demonstrate that the proposed optimization model is practical in handling task scheduling problems and provides an effective solution for resource management in big data environments. This research not only improves the efficiency of task processing, but also provides new ideas for optimizing future scheduling algorithms. [ABSTRACT FROM AUTHOR]

Circular supply chain (CSC) networks improve sustainability and create socially responsible enterprises through recycling, harvesting, and refurbishing. This study develops a Lagrangian relaxation (LR) algorithm for solving location-inventory-routing (LIR) problems with heterogeneous vehicles in multi-period and multi-product sustainable CSC networks. The proposed Artificial Internet of Things (AIoT) enabled sustainable CSC is designed to increase network performance and create a secure and traceable environment. For the first time, an LR algorithm is proposed to solve the LIR problems in an AIoT-enabled CSC network with storage, backorder shortage, split-delivery, and time window potentials. Sixteen small- and medium-size simulated problems were produced to assess the performance of the proposed algorithm relative to the GAMS software. The results show the proposed algorithm can solve the small- and medium-size problems as effectively as GAMS software but faster and more efficiently. In addition, eight large-size simulation problems were produced and solved by the algorithm. While the GAMS software failed to solve the large-size problems, the LR algorithm solved them efficiently and successfully. [ABSTRACT FROM AUTHOR]

In this paper, by applying the supplementary variable method (SVM), some high-order, conformal structure-preserving, linearized algorithms are developed for the damped nonlinear Schrödinger equation. We derive the well-determined SVM systems with the conformal properties and they are then equivalent to nonlinear equality constrained optimization problems for computation. The deduced optimization models are discretized by using the Gauss type Runge-Kutta method and the prediction-correction technique in time as well as the Fourier pseudo-spectral method in space. Numerical results and some comparisons between this method and other reported methods are given to favor the suggested method in the overall performance. It is worthwhile to emphasize that the numerical strategy in this work could be extended to other conservative or dissipative system for designing high-order structure-preserving algorithms. [ABSTRACT FROM AUTHOR]

Ring rolling process is a plastic deformation process used in the production of seamless rings having diameters in a range of meters. During production, rings simultaneously undergo to a width and height reduction and a diameter expansion, however located in different ring cross sections as a function of idle, axial and driving rolls action. Despite roll motion law could be set independently, their combination influences ring accuracy, production time and energy required. Accordingly, based on the results of simulation plan, the authors present an analytical model able to optimize rolls motion laws as a function of required geometrical accuracy and minimizing production time and energy. The analysis of these latter, allowed the definition of their regression models as a function of Idle roll feed rate and ring rotational speed. These models were then expressed as a function of selected geometrical precision parameter and a weighting factor, for balancing time and energy of the process. Afterwards, geometry and energy models were arranged to define an objective function that, once minimized, allowed to assess the optimized values of the process parameters able to achieve the selected ring precision while decreasing process time and energy. The proposed methodology was applied on different combinations of geometrical and weighting factors, and the resulting optimized conditions were tested by finite element simulations. The good comparison between modeled and simulated ring accuracy and energy, demonstrates the model efficacy in the selection of proper motion laws of ring rolling equipment. [ABSTRACT FROM AUTHOR]

When addressing a decision making problem with incomplete preference relations, missing information could be estimated by proposing an optimization model. The previous studies always focus on a unique solution to optimization models through a software program. But theoretical and simulation investigations of multiple solutions are seldom considered. This paper investigates the existence of multiple solutions to an optimization model and proposes a simulation procedure. First, an optimization model is recalled for completing incomplete fuzzy preference relations. It is the first time to theoretically prove the existence of multiple solutions to the optimization model under various incomplete entries. The obtained results reveal that the optimal solution to an optimization model may be an interval value, which is dependent on the number and position of missing entries in an incomplete fuzzy preference relation. The idea of multiple solutions is further extended to the situation where an intransitive fuzzy preference relation should be adjusted to a weakly transitive matrix. Second, multiple solutions to the optimization model are simulated using the quantum-behaved particle swarm optimization algorithm. It is found that multiple solutions do appear under some conditions by only running the algorithm for multiple times. Finally, the impact of multiple solutions on the optimal alternative(s) of a decision making problem is analyzed by comparing with the existing studies. The result shows that the existence of multiple solutions reflects the uncertainty of optimization models, which is of concerns to propose an effective decision-making model. [ABSTRACT FROM AUTHOR]

China has emerged as a major global player in the pharmaceutical industry and a significant consumer of drugs. However, the rapid growth of the raw material pharmaceutical manufacturing sector has resulted in a substantial amount of waste generation, leading to environmental pollution issues. In order to mitigate the pollution problems associated with the pharmaceutical manufacturing industry, this research proposes a tripartite game model with two dynamic parameters and develops optimization models for green supply chains under three decision-making scenarios, aiming to contribute to the efforts for achieving low-carbon development. Additionally, the study considers the government's green subsidy system and incorporates the optimization of green pharmaceutical practices and goodwill of the enterprises as dynamic parameters. By employing differential game methods, equilibrium solutions among the three parties are determined. The research findings reveal that: firstly, the green pharmaceutical levels are equivalent among the three models; secondly, in scenarios where retailers receive subsidies and make decentralized decisions, API manufacturers achieve the highest level of goodwill and the supply chain profit is maximized; thirdly, when manufacturers have sufficiently high marginal profits, adopting a decentralized decision-making approach encourages retailers and enhances the overall goodwill level of the supply chain. This study provides valuable insights for the formulation of effective strategies and policies to promote sustainable development within the pharmaceutical industry. [ABSTRACT FROM AUTHOR]

The problems of uneven load and low operating efficiency in the oil-gathering system of old oilfields lead to higher operating costs. In order to reduce operating costs, the layout-reconfiguration optimization model is established, and the minimum comprehensive investment is taken as the objective function. The multi-constraint conditions, such as the current situation of the oil-gathering system, the processing capacity, the possibility of pipeline failure, and the obstacles, are considered. The hybrid arithmetic–fireworks optimization algorithm (AFOA) is proposed to solve the model. Combined with the experience of the hybrid metaheuristic algorithm, using hybrid metaheuristics, the hybrid of the arithmetic optimization algorithm (AOA) and the operator of the fireworks algorithm (FWA) is considered, and some improved operators of FWA are integrated into AOA to form a new algorithm (AFOA) to achieve a better solution effect. Compared with the 11 other algorithms, AFOA has better solution efficiency. This method is applied to the actual case of an old oilfield. The optimized scheme increases the average load rate of the station by 15.9% and reduces the operating costs by 38.1% per year. Overall, the reconstruction costs will be recovered in a short period. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cell (PEMFC) parameter extraction is an important issue in modeling and control of renewable energies. The PEMFC problem's main objective is to estimate the optimal value of unknown parameters of the electrochemical model. The main objective function of the optimization problem is the sum of the square errors between the measured voltages and output voltages of the proposed electrochemical optimized model at various loading conditions. Natural rabbit survival strategies such as detour foraging and random hiding are influenced by Artificial rabbit optimization (ARO). Meanwhile, rabbit energy shrink is mimicked to control the smooth switching from detour foraging to random hiding. In this work, the ARO algorithm is proposed to find the parameters of PEMFC. The ARO performance is verified using experimental results obtained from conducting laboratory tests on the fuel cell test system (SCRIBNER 850e, LLC). The simulation results are assessed with four competitive algorithms: Grey Wolf Optimization Algorithm, Particle Swarm Optimizer, Salp Swarm Algorithm, and Sine Cosine Algorithm. The comparison aims to prove the superior performance of the proposed ARO compared with the other well-known competitive algorithms. [ABSTRACT FROM AUTHOR]

The Combined Cooling, Heat, and Power (CCHP) System is an efficient technology that reduces primary energy consumption and carbon dioxide emissions by generating heat, cold, and electricity simultaneously from the same fuel source. This study developed an economic optimization model using linear mathematical program theory to determine the optimal sizes of different components in a CCHP system. The study found that CCHP systems with internal combustion engines have the largest optimal size due to lower capital expenditure and improved hourly changes in combined energy production by considering electrical and absorption chillers simultaneously. The analysis compared the size determination of CCHP systems with internal combustion engine (ICE), sterling engine (SE), and proton exchange membrane fuel cell (PEMFC) technologies. PEMFC had the highest annual overall cost among the technologies studied. The results of determining the size of the CCHP system are compared with ICE, SE, and PEMFC technologies. It has been noted that PEMFC has the highest annual overall cost among the studied technologies. The usefulness index of the CCHP system increased from 23% to almost 40% when electricity was sold to the grid using internal combustion engine technology. [ABSTRACT FROM AUTHOR]

This paper introduces a two-echelon optimization model for the integrated routing of an electric vehicle (EV) and a traditional internal combustion engine vehicle (ICEV) in an urban environment. The scientific context of this study is sustainable urban logistics. The case study focuses on the distribution of pharmaceuticals in the metropolitan area of Rome. Distributing pharmaceuticals in large cities presents significant challenges, including heavy traffic congestion, the need for strict temperature control, and the maintenance of the integrity and timely delivery of sensitive medications. Furthermore, the complexity of urban logistics and adherence to regulatory requirements introduce additional layers of difficulty. Therefore, the implementation of fast and sustainable distribution mechanisms is crucial in this context. Specifically, the model seeks to minimize both total CO2 emissions and transportation costs while optimizing the use of an EV and an ICEV, all while ensuring that service level requirements are met. Computational results demonstrate the effectiveness of the proposed method in improving the sustainability of pharmaceutical distribution. [ABSTRACT FROM AUTHOR]

This study addresses the delicate balance between healthcare personnel burnout and medical accessibility in the context of endovascular thrombectomy (EVT) services in urban areas. We aimed to determine the minimum number of hospitals providing EVT on rotation each day without compromising patient access. Employing an optimization model, we developed shift schedules based on patient coverage rates and volumes during the pre-pandemic (2016–2018) and pandemic (2019–2021) periods. Starting with a minimum of two hospitals on duty per day, we gradually increased to a maximum of eight. Patient coverage rates, defined as the proportion of patients meeting bypass criteria and transported to rotating hospitals capable of EVT, were the primary outcomes. Sensitivity analyses explored the impact of varying patient transport intervals and accumulating patients over multiple years. Results from 7024 patient records revealed patient coverage rates of 92.5% (standard deviation [SD] 2.8%) during the pre-pandemic and 91.4% (SD 2.8%) during the pandemic, with at least two rotating hospitals daily. No significant differences were observed between schedules based on the highest patient volume and coverage rate months. A patient coverage rate of 98.99% was achieved with four rotating hospitals per day during the pre-pandemic period, with limited improvement beyond this threshold. Changing patient transport intervals and accumulating patients over six years (p = 0.83) had no significant impact on coverage rates. Our optimization model supports reducing the number of daily rotating hospitals by half while preserving a balance between patient accessibility and alleviating strain on medical teams. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of determining the optimal capacity of concentrated solar power (CSP) plants, especially in the context of hybrid solar power plants. This work presents an innovative analytical approach to optimizing the capacity of concentrated solar plants. The proposed method is based on the use of additional non-dimensional parameters, in particular, the design factor and the solar multiple factor. This paper presents a mathematical optimization model that focuses on the capacity of concentrated solar power plants where thermal storage plays a key role in the energy source. The analytical approach provides a more complete understanding of the design process for hybrid power plants. In addition, the use of additional factors and the combination of the proposed method with existing numerical methods allows for more refined optimization, which allows for the more accurate selection of the capacity for specific geographical conditions. Importantly, the proposed method significantly increases the speed of computation compared to that of traditional numerical methods. Finally, the authors present the results of the analysis of the proposed system of equations for calculating the levelized cost of electricity (LCOE) for hybrid solar power plants. The nonlinearity of the LCOE on the main calculation parameters is shown. [ABSTRACT FROM AUTHOR]

Gravel packing in deep-water horizontal wells is an effective and practical sand control method, which is a key technical method to ensure efficient exploitation of deep-water oil and gas. To ensure the successful implementation of gravel packing in deep water horizontal wells, it is crucial to carry out effective optimization design of packing parameters. This paper proposes a novel optimization design approach for gravel packing in deep-water horizontal wells. In the proposed approach, an optimization model is proposed for gravel packing in deep-water horizontal wells, in which the gravel packing length is regarded as the objective function. Then, an improved artificial electric field algorithm (IAEFA) is introduced for optimizing the key gravel packing parameters so as to determine the maximum gravel packing length. For a specific case study, we conducted optimization calculations for gravel packing in a deep-water horizontal well. Results of the case study demonstrate that the optimization design approach based on the IAEFA algorithm can effectively address the parameter optimization problem of deep-water horizontal well gravel packing. For the target well of the case study, the maximum packing length obtained by the IAEFA algorithm could reach 1000.22 m, and the corresponding 3 sets of optimal packing parameters were also obtained. In the scenario of optimal packing parameters, the total time of gravel packing in target well is 566.6 min, and the total amount of sand consumption is 54,050.94 lbs. The bottom hole pressure during the injection stage remains stable with about 9780 psi, then slowly rises from 9788 psi to 9837 psi in the α-wave packing stage, and rapidly increases from 9837 psi to 9986 psi in the β-wave packing stage. The proposed approach provides an efficient and practical optimization tool for the optimal design of gravel packing in deep water horizontal wells. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have been widely considered an essential element to contribute to green and smart transportation, which will further enhance the development of smart cities. Hong Kong, as one of the largest metropolises in the world, has promoted the deployment of EVs for both the private and public transportation sectors over the past decade, with substantial financial subsidies and encouraging policy incentives. With the rapid penetration of EVs, especially in the market of private passenger cars, Hong Kong may face the challenge of insufficient charging facilities in the next few years. As such, the research study aims to develop a mathematical model using a topological method to map out feasible locations for new EV charging facilities on Ap Lei Chau Island, to construct a small Python program to optimize the mapping process of these feasible locations, and to estimate energy consumption and associated economic analysis to foster the spatial planning of EV charging facility networks. In conclusion, optimal locations for new charging facilities for EVs have been revealed to match the rapid growth of EV usage and facilitate the emergence of green and smart transportation. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of determining the optimal capacity of concentrated solar power (CSP) plants, especially in the context of hybrid solar power plants. This work presents an innovative analytical approach to optimizing the capacity of concentrated solar plants. The proposed method is based on the use of additional non-dimensional parameters, in particular, the design factor and the solar multiple factor. This paper presents a mathematical optimization model that focuses on the capacity of concentrated solar power plants where thermal storage plays a key role in the energy source. The analytical approach provides a more complete understanding of the design process for hybrid power plants. In addition, the use of additional factors and the combination of the proposed method with existing numerical methods allows for more refined optimization, which allows for the more accurate selection of the capacity for specific geographical conditions. Importantly, the proposed method significantly increases the speed of computation compared to that of traditional numerical methods. Finally, the authors present the results of the analysis of the proposed system of equations for calculating the levelized cost of electricity (LCOE) for hybrid solar power plants. The nonlinearity of the LCOE on the main calculation parameters is shown.

The Combined Cooling, Heat, and Power (CCHP) System is an efficient technology that reduces primary energy consumption and carbon dioxide emissions by generating heat, cold, and electricity simultaneously from the same fuel source. This study developed an economic optimization model using linear mathematical program theory to determine the optimal sizes of different components in a CCHP system. The study found that CCHP systems with internal combustion engines have the largest optimal size due to lower capital expenditure and improved hourly changes in combined energy production by considering electrical and absorption chillers simultaneously. The analysis compared the size determination of CCHP systems with internal combustion engine (ICE), sterling engine (SE), and proton exchange membrane fuel cell (PEMFC) technologies. PEMFC had the highest annual overall cost among the technologies studied. The results of determining the size of the CCHP system are compared with ICE, SE, and PEMFC technologies. It has been noted that PEMFC has the highest annual overall cost among the studied technologies. The usefulness index of the CCHP system increased from 23% to almost 40% when electricity was sold to the grid using internal combustion engine technology.

Abstract Proton exchange membrane fuel cell (PEMFC) parameter extraction is an important issue in modeling and control of renewable energies. The PEMFC problem’s main objective is to estimate the optimal value of unknown parameters of the electrochemical model. The main objective function of the optimization problem is the sum of the square errors between the measured voltages and output voltages of the proposed electrochemical optimized model at various loading conditions. Natural rabbit survival strategies such as detour foraging and random hiding are influenced by Artificial rabbit optimization (ARO). Meanwhile, rabbit energy shrink is mimicked to control the smooth switching from detour foraging to random hiding. In this work, the ARO algorithm is proposed to find the parameters of PEMFC. The ARO performance is verified using experimental results obtained from conducting laboratory tests on the fuel cell test system (SCRIBNER 850e, LLC). The simulation results are assessed with four competitive algorithms: Grey Wolf Optimization Algorithm, Particle Swarm Optimizer, Salp Swarm Algorithm, and Sine Cosine Algorithm. The comparison aims to prove the superior performance of the proposed ARO compared with the other well-known competitive algorithms.

Background/purpose: This study addresses the delicate balance between healthcare personnel burnout and medical accessibility in the context of endovascular thrombectomy (EVT) services in urban areas. We aimed to determine the minimum number of hospitals providing EVT on rotation each day without compromising patient access. Methods: Employing an optimization model, we developed shift schedules based on patient coverage rates and volumes during the pre-pandemic (2016–2018) and pandemic (2019–2021) periods. Starting with a minimum of two hospitals on duty per day, we gradually increased to a maximum of eight. Patient coverage rates, defined as the proportion of patients meeting bypass criteria and transported to rotating hospitals capable of EVT, were the primary outcomes. Sensitivity analyses explored the impact of varying patient transport intervals and accumulating patients over multiple years. Results: Results from 7024 patient records revealed patient coverage rates of 92.5% (standard deviation [SD] 2.8%) during the pre-pandemic and 91.4% (SD 2.8%) during the pandemic, with at least two rotating hospitals daily. No significant differences were observed between schedules based on the highest patient volume and coverage rate months. A patient coverage rate of 98.99% was achieved with four rotating hospitals per day during the pre-pandemic period, with limited improvement beyond this threshold. Changing patient transport intervals and accumulating patients over six years (p = 0.83) had no significant impact on coverage rates. Conclusion: Our optimization model supports reducing the number of daily rotating hospitals by half while preserving a balance between patient accessibility and alleviating strain on medical teams.

Abstract Bridges serve as critical links in road networks, requiring continuous maintenance to ensure proper functionality throughout their lifespan. Given their pivotal role in the urban landscape, connecting various parts of a city, this research presents a multi-objective optimization model for the maintenance and repair of bridges in Babolsar, Iran. The model takes into account budget constraints and aims to minimize the total life cycle and user costs, encompassing traffic delays and vehicle expenses, while maximizing the reliability of the bridge network. Recognizing the inherent complexity of this problem, a multi-objective particle swarm optimization algorithm has been developed for an accurate solution. The study further conducts sensitivity analysis on the objective function concerning the available budget, evaluating key parameters such as hourly costs and the time value of vehicles. The results show that with an increase in the budget level, the number of repairs related to the most costly maintenance has significantly risen. In other words, as the budget expands, the model tends to favor repairs with higher costs because their impact on the bridge’s performance is more substantial.

Lot sizing is a prevalent issue within manufacturing companies, where determining the optimal procurement and production lot sizes is crucial for maximizing profits. This problem has become more complex, given that numerous suppliers can provide the same raw materials with different prices and quantity discount schemes. A company should also determine optimal carriers to deliver materials to the company’s warehouse. In a manufacturing process, the company should determine the optimal production lot size and its schedules. In this paper, a model was developed to solve simultaneously procurement and production lot sizing, as well as production scheduling problems. The model encompasses multiple suppliers offering quantity discounts, aiming to maximize company profit by accounting for various costs, including procurement, production, inventory, and quality costs. A case study is taken from a company producing noodles and its related derivative products to illustrate the application of the model. Based on the optimization results, the company obtained a total profit of IDR. 14,656,550,000 or $950,921.30 (the exchange rate of $1 at IDR. 15,413). The sensitivity analysis results show that the objective function is sensitive to changes in the purchase cost, sale revenue, and discount rate parameters. The decision variables for accepted product demand, product quantity, and the starting and completion time of product family are only sensitive to changes in certain parameters. Meanwhile, the decision variables for product inventory, product backlog, raw material inventory, and purchased raw material quantity are sensitive to the changes in all the analyzed parameters.

Biomass to biofuel conversion is carried out using complex technologies, which are often capacity restricted. Although these restrictions are used to satisfy the design limitations of the processing units, they often lead to the selection of suboptimal choices for technologies to fulfil the demand. This article proposes a distributed multi-unit mixed integer linear programming model for a biorefinery supply chain capable of utilizing multiple technologies for each biomass. A single-unit superstructure from the literature is improved to accommodate decisions related to multiple technological choices for different biomass and extended to a multi-unit model by allowing the installation of more than one process unit of the selected technology. This study incorporates the investment and operational costs for processing facilities to demonstrate the long-term cost analysis of the supply chain. The efficacy of the proposed model is demonstrated in a distributed biorefinery case study involving industrial units known as secondary processing facilities, which fulfil their biofuel demand using onsite production and external processing facilities. The objective is to obtain an optimal production plan by minimizing the total cost while satisfying the demands of all users and other resource constraints. The optimal solution of the multi-unit model has reduced the total cost by 1.31%, compared to the single-unit model. A rigorous solution analysis of the single and multi-unit models is performed to validate the proposed model. [ABSTRACT FROM AUTHOR]

This study proposes a novel approach to support sustainable decision-making in knitted shoe manufacturing by integrating activity-based costing (ABC), the theory of constraints (TOC), and carbon emission costs into a comprehensive mathematical programming model. The model is applied to evaluate the impact of different carbon tax and carbon trading policies on the profitability and product mix of a knitted shoe company in Taiwan. The model considers single-period and multi-period scenarios, as well as continuous and discontinuous carbon tax functions, with and without carbon trading. The results show that a continuous carbon tax leads to higher profitability in single-period models, while a continuous carbon tax function combined with carbon trading yields the highest profits in multi-period models. Reducing the carbon emission cap is found to be more effective in curbing emissions than raising carbon taxes. This research contributes to sustainable operations management by providing a holistic approach that integrates cost control, profit optimization, and environmental sustainability in the context of Industry 4.0. The findings offer valuable insights for footwear manufacturers in making strategic decisions and for governments in designing effective carbon tax and emission trading schemes to drive industrial transformation towards a low-carbon economy. [ABSTRACT FROM AUTHOR]

The increasing political momentum advocating for decarbonization efforts has led many governments around the world to unveil national hydrogen strategies. Hydrogen is viewed as a potential enabler of deep decarbonization, notably in hard-to-abate sectors such as the industry. A multi-modal, hourly resolved, linear programming model was developed to assess the infrastructure requirements of a low-carbon supply chain over a large region. It optimizes the deployment of infrastructure from 2025 up to 2050 by assessing four years: 2025, 2030, 2040, and 2050, and is location agnostic. The considered infrastructure encompasses several technologies for production, transmission, and storage. Model results illustrate supply chain requirements in Texas and Louisiana. Edge cases considering 100% electrolytic production were analyzed. Results show that by 2050, with an assumed industrial demand of 276 TWh/year, Texas and Louisiana would require 62 GW of electrolyzers, 102 GW of onshore wind, and 32 GW of solar panels. The resulting levelized cost of hydrogen totaled $5.6–6.3/kgH 2 in 2025, decreasing to $3.2–3.5/kgH 2 in 2050. Most of the electricity production occurs in Northwest Texas thanks to high capacity factors for both renewable technologies. Hydrogen is produced locally and transmitted through pipelines to demand centers around the Gulf Coast, instead of electricity being transmitted for electrolytic production co-located with demand. Large-scale hydrogen storage is highly beneficial in the system to provide buffer between varying electrolytic hydrogen production and constant industrial demand requirements. In a system without low-cost storage, liquid and compressed tanks are deployed, and there is a significant renewable capacity overbuild to ensure greater electrolyzer capacity factors, resulting in higher electricity curtailment. A system under carbon constraint sees the deployment of natural gas-derived hydrogen production. Lax carbon constraint target result in an important reliance on this production method due to its low cost, while stricter targets enforce a great share of electrolytic production. • Novel hydrogen supply chain model, solved at granular temporal and geographical scales. • Evaluates the decarbonization of industrial hydrogen demand in the Gulf Coast region. • Both full electrolytic hydrogen production and a mix with conventional production are considered. • Levelized cost of electrolytic hydrogen tallies $5.6–6.3/kgH 2 in 2025, decreasing to $3.2–3.5/kgH 2 in 2050. [ABSTRACT FROM AUTHOR]

The Regional Integrated Energy System (RIES) represents a crucial technological strategy for future energy solutions, with an emphasis on regional integration as a key developmental direction. The substantial integration of renewable energy and the diversification of load types introduce multifaceted uncertainties that significantly impact the operation of the electrical grid. In response to these issues, this paper presents an optimized RIES model that incorporates multiple uncertainties. multi-energy coupling, and Integrated Demand Response (IDR). The proposed RIES optimization model encompasses renewable energy sources. coupling devices. energy storage units, and both electrical and thermal loads. This model undergoes a comparative analysis of the mathematical characteristics of source and load uncertainties. To address these uncertainties, Robust Optimization (RO) is applied to new energy source variability, while Stochastic Optimization (SO) was utilized for load uncertainty characterization. The model was then empirically validated using actual RIES data. Research findings indicated that the application of RO and SO methods for modeling source and load uncertainties enhances robustness and economic efficiency. The RIES model, considering these uncertainties, effectively reduces the disparity between peak and valley loads, decreases the pressure on equipment supply, and guarantees collaborative optimization of the system's operations, thereby improving both the reliability and economic viability of the system. [ABSTRACT FROM AUTHOR]

Rural fires have been a constant concern, with most being associated with land abandonment. However, some fires occur due to negligent attitudes towards fire, which is often used to remove agroforestry leftovers. In addition to the fire risk, this burning also represents a waste of the energy present in this residual biomass. Both rural fires and energy waste affect the three dimensions of sustainability. The ideal solution seems to be to use this biomass, avoiding the need for burning and recovering the energy potential. However, this process is strongly affected by logistical costs, making this recovery unfeasible. In this context, this study aims to propose an optimization model for this chain, focusing on the three dimensions of sustainability. The results of the present study comprise a summary of the current state of the art in supply-chain optimization, as well as a disruptive mathematical model to optimize the residual biomass supply chain. To achieve this objective, a literature review was carried out in the first phase, incorporating the specificities of the context under study to arrive at the final model. To conclude, this study provides a review covering several metaheuristics, including ant colony optimization, genetic algorithms, particle swarm optimization, and simulated annealing, which can be used in this context, adding another valuable input to the final discussion. [ABSTRACT FROM AUTHOR]

Collaborative distribution is the core of modern logistics, and the collaborative distribution centre is the physical location of distribution. This article aims to study the use of green computing energy management to promote a collaborative distribution optimization model and algorithm for an intelligent supply chain. A multiobjective genetic algorithm for energy management using green computing and a multiobjective hybrid genetic algorithm based on parallel selection methods are designed and implemented. A joint optimization model of VRP & VFP for logistics distribution is established. Collaborative system design and collaborative system operation inventory control issues are integrated. Considering uncertain demand, a multiobjective mixed-integer programming model of energy management using green computing is established to solve this problem. Experimental research shows that the optimal solution is found before the optimal operation of the 24th-generation collaborative system. The designed functional value of the collaborative system is 66109, and the optimal operating value of the collaborative system is 57348. [ABSTRACT FROM AUTHOR]

Edge-preserving image smoothing plays a vital role in the field of computational photography. In this paper, we propose a weighted and truncated L 1 -regularized optimization model for image smoothing. We show that the weighted and truncated scheme significantly promotes the edge-preserving property. Furthermore, we propose a deep unsupervised learning-based filter based on the loss function defined by the proposed optimization model. The proposed filter leverages a U-Net structure, which fully exploits the spatially varying smoothing scales of the edge-preserving filtering. We have conducted extensive experiments to evaluate the proposed filter. The results suggest that our filter outperforms the state-of-the-art filters in image quality on various tasks, such as image smoothing, detail enhancing, HDR tone mapping, and edge detection. Meanwhile, our filter is extremely efficient. It is able to process 720P images in real-time (more than 16 frames per second) on a modern desktop with an Intel i7-8700K CPU, an NVIDIA GTX 1080 GPU and 16GB memory. [ABSTRACT FROM AUTHOR]

Edge-aware image decomposition is an essential topic in the field of multimedia signal processing. In this paper, we propose a novel non-convex penalty function, which we name the generalized Welsch function. We show that the proposed penalty function is more than a generalization of most existing penalty functions for edge-aware regularization, thus, it better facilitates edge-awareness. We embed the proposed penalty function into a novel optimization model for edge-aware image decomposition. To solve the optimization model with non-convex penalty function, we propose an efficient algorithm based on the additive quadratic minimization and Fourier domain optimization. We have experimented with the proposed method in a variety of tasks, including image smoothing, detail enhancement, HDR tone mapping, and JPEG compression artifact removal. Experiment results show that our method outperforms the state-of-the-art image decomposition methods. Furthermore, our method is highly efficient, it is able to render real-time processing of 720P color images on a modern GPU. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The post-earthquake emergency period, which is a sensitive time segment just after an event, mainly focuses on saving life and restoring social order. To improve the seismic resilience of city road networks, a resilience evaluation method used in the post-earthquake emergency period is proposed. The road seismic damage index of a city road network can consider the influence of roads, bridges and buildings along the roads, etc. on road capacity after an earthquake. A function index for a city road network is developed, which reflects the connectivity, redundancy, traffic demand and traffic function of the network. An optimization model for improving the road repair order in the post-earthquake emergency period is also developed according to the resilience evaluation, to enable decision support for city emergency management and achieve the best seismic resilience of the city road network. The optimization model is applied to a city road network and the results illustrate the feasibility of the resilience evaluation and optimization method for a city road network in the post-earthquake emergency period. [ABSTRACT FROM AUTHOR]

Service composition is widely employed in cloud manufacturing. Due to the abundance of similar cloud manufacturing services, the search space for optimizing service composition tends to be expansive. Existing optimization models primarily focus on QoS (quality of service) while often neglecting QoC (quality of collaboration). Furthermore, there remains scope for improving the quality and stability of service composition optimization. Therefore, this paper proposes a two-phase method for optimizing service composition in cloud manufacturing. In the first phase, we introduce a service cluster-oriented service response framework, efficiently generating the candidate response service set to reduce solution search space. In the second phase, we construct an optimization model that integrates QoS and QoC. Subsequently, we devise an artificial bee colony (ABC) algorithm incorporating a multi-search strategy island model to optimize cloud manufacturing service composition. Experimental results demonstrate that the introduction of service clusters enhances search efficiency, with the proposed method outperforming compared ABC algorithms and other swarm intelligence algorithms in optimization quality and stability. [ABSTRACT FROM AUTHOR]

Background: The most significant changes caused by the COVID-19 crisis were the sharp increase in working from home and the growing importance of e-commerce, which affected the development of some industries. This change also affects the investors' investment operations, which are based on analysis to ensure an unquestionable certainty of the invested financial amount and a satisfactory return. It is, therefore, interesting to analyze the possible return of the chosen investment strategy based on the optimization model of portfolio selection based on the CVaR risk measure. Purpose: The paper aims to present the possible use of the analysis of returns of effective portfolios constructed based on the optimization model of portfolio selection based on the CVaR risk measure during the crisis (COVID19) and the pre-crisis period. Study design/methodology/approach: Paper presents the impact of the COVID-19 crisis on investor decisionmaking through the CVaR risk measure, which was implemented on the historical data of the components of the Standard and Poor's 500 stock index (S&P 500) in the crisis period as well as in the pre-crisis period. Findings/conclusions: The presented approach based on the CVaR risk rate measure and the relevant portfolio selection model provides the investor with an effective tool for allocating funds to the financial market in particular segments in both monitored periods. Limitations/future research: Time series data are divided into two periods based on visible factors such as the number of COVID-19 cases. In future research, we aim to divide monitored periods based on unobservable factors influencing investors' decisions, such as bull or bear mood on the market. [ABSTRACT FROM AUTHOR]

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate positioning is crucial in fields such as indoor positioning, autonomous driving, and robot navigation. However, traditional base station positioning algorithms are often affected by factors such as sensor errors, environmental noise, and uncertainty, resulting in certain errors in the positioning results. The article constructed a probability graph model using Radio Frequency Identification (RFID) by using position estimation results as variables. In this model, each position estimation result was represented as a node, and the dependency relationship between positions was represented as an edge. This article observed sensor data and uses Bayesian inference methods to update the probability distribution of each node. The experimental results showed that compared to traditional base station positioning algorithms, the probability graph optimization model based on radio frequency identification can significantly improve the accuracy and reliability of positioning. The highest values for both reached 0.989 and 0.95, respectively. This model has significant advantages in positioning tasks and provides an effective solution for practical applications. [ABSTRACT FROM AUTHOR]

Given the ever-growing electricity consumption and environmental anxiety with the predominant usage of conventional fuels in power plants, it is crucial to explore suitable alternatives to address these issues. Renewable energy sources (RESs) have emerged as the preferred choice for meeting energy requirements due to their minimal pollution. This study proposes a new idea to minimize operational costs and achieve the most cost-effective grid with minimum cost. Meanwhile, the transportation sector is gradually replacing conventional fossil-cars with electric ones, specifically plug-in electric vehicles (PEVs) and plug-in hybrid electric vehicles (PHEVs), which have gained significant consideration. These vehicles can join to the main grid and engage in energy exchange through grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies. Additionally, the concept of microgrid (MG) is proposed to optimize the potential of PEVs through smart infrastructure. Using the V2G capability, the operating costs are reduced, providing opportunities to incorporate PEVs into the network. Therefore, effective operation of MGs becomes highly significant. This paper suggests management of a MG consisting of PEVs and RESs. The approach utilizes a stochastic programming technique called unscented transformation (UT). The problem is addressed as a single-objective stochastic optimization problem with the aim of minimizing the operation cost. The proposed approach employs the hybrid whale optimization algorithm and pattern search (HWOA–PS) to solve the stochastic problem. The obtained outcomes are compared with those of other approaches to validate its effectiveness. [ABSTRACT FROM AUTHOR]

Battery-electric buses (BEBs) are considered suitable technology for transit to tackle climate change and promote environmentally friendly mobility solutions. However, the systemic configuration of BEBs in transit requires sophisticated planning efforts due to contradictory objectives and decisions. The optimal design of a BEB transit system is often approached from various perspectives, leading to different system configurations and distinct impacts on the electricity grid. Towards that end, this study develops three BEB system configuration optimization models, including minimizing capital costs, electricity costs, and greenhouse gas (GHG) emissions. All three models inform the optimal charging system configuration, BEBs battery capacity, and BEBs charging schedule for a general hub-and-spoke transit network. The proposed models are applied to a case study of the Belleville City, Ontario, Canada, bus transit network. The results demonstrate that BEB system configuration and GHG emissions vary significantly according to the optimization perspective. Moreover, the findings emphasize the importance of using the energy storage system to reduce electricity costs and GHG emissions. • Assess the impacts of optimization approaches on the BEB system configuration. • Investigate the benefits of implementing ESS in BEB system configuration. • Propose an integrated BEB system planning and operation optimization model. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The house of quality (HOQ), which serves as the initial matrix in quality function deployment (QFD), is widely employed to set the technical objectives for engineering characteristics. Nevertheless, there exist methodological deficiencies within the HOQ, concerning the assessment of relationship ratings between customer requirements and engineering characteristics, as well as the lack of a structured process for determining design specifications. Therefore, this study proposes a formal HOQ procedure to determine the technical targets of engineering characteristics. The swing method and a specific normalization technique are utilized to incorporate correlations between engineering characteristics, aiming to improve relationship ratings. Additionally, an optimization model has been devised to maximize customer satisfaction within the constraints of available organizational resources. The procedure is illustrated using a wooden dining chair design as an example. [ABSTRACT FROM AUTHOR]

Construction projects encounter a variety of risks, which require a thorough risk response phase to identify, evaluate, and determine solutions. This study presents a methodology for effectively choosing an appropriate risk response strategy and allocating suitable funds for the risk response phase of building projects. The framework employs optimization approaches and evolutionary principles, specifically utilizing the Genetic Algorithm technique. The objective of the model is to reduce costs and determine a suitable fund allocation strategy with minimum risk. The effectiveness of the framework is assessed in an actual building project involving a ventilation and air conditioning system, demonstrating its ability to optimize risk response and assist decision-makers in making well-informed choices. [ABSTRACT FROM AUTHOR]

The deployable telescopic boom, whose mass and stiffness play crucial roles, is extensively used in the design of space-deployable structures. However, the most existing optimal design that neglects the influence of the locking mechanisms in boom joints cannot raise the whole stiffness while reducing the boom mass. To tackle this challenge, a novel optimization model, which utilizes the arrangement of the locking mechanisms to achieve synchronous improvement of the stiffness and mass, is proposed. The proposed optimization model incorporates a novel joint stiffness model developed based on an equivalent parallel mechanism that enables the consideration of multiple internal stiffness factors of the locking mechanisms and tubes, resulting in more accurate representations of the joint stiffness behavior. Comparative analysis shows that the proposed stiffness model achieves more than at least 11% improved accuracy compared with existing models. Furthermore, case verification shows that the proposed optimization model can improve stiffness while effectively reducing mass, and it is applied in boom optimization design. [ABSTRACT FROM AUTHOR]

IntroductionThe development of science and technology has driven rapid changes in the social environment, especially the rise of the big data environment, which has greatly increased the speed at which people obtain information. However, in the process of big data processing, the allocation of information resources is often unreasonable, leading to a decrease in efficiency. Therefore, optimizing task scheduling algorithms has become an urgent problem to be solved.MethodsThe study optimized task scheduling algorithms using artificial intelligence (AI) methods. A task scheduling algorithm optimization model was designed using support vector machine (SVM) and K-nearest neighbor (KNN) combined with fuzzy comprehensive evaluation. In this process, the performance differences of different nodes were considered to improve the rationality of resource allocation.Results and DiscussionBy comparing the task processing time before and after optimization with the total cost, the results showed that the optimized model significantly reduced task processing time and total cost. The maximum reduction in task processing time is 2935 milliseconds. In addition, the analysis of query time before and after optimization shows that the query time of the optimized model has also been reduced. The experimental results demonstrate that the proposed optimization model is practical in handling task scheduling problems and provides an effective solution for resource management in big data environments. This research not only improves the efficiency of task processing, but also provides new ideas for optimizing future scheduling algorithms.

Island electricity generation systems (IES) pose challenges in the integration of renewable energies that are compatible with security of supply. This work proposes a methodology and a proposed decision tool that allows the optimization of the production of different generation systems, both renewable and non-renewable, setting a series of objectives such as the reduction of greenhouse gases (GHG), production costs and at the same time fulfilling the best coverage in dynamic response, security, scalability, and integration. This tool is based on operational research, mathematical optimization methods, specifically the simplex algorithm and the generalized reduced gradient (GRG) and proposes different combinations to achieve an energy production that meets the demand, minimizing fuel consumption and greenhouse gas (GHG) emissions.

Railway vehicles, due to a large number of hours spent in service operation, have a high frequency of breakdowns, thus, maintenance planning can be complex and expensive. The concept of maintenance is certainly connected to the concept of reliability, and the consequences of neglect can cause enormous resources losses. This research develop tool to optimize the required stocks of spare parts / materials and required number of employees for a given level of vehicle reliability. In the first phase of the research, data for the total fleet of 216 passenger wagons divided into 36 different technical groups were structured and analyzed according to the criteria defined by domestic regulations and international standards. In the second phase, reliability dependences on resource costs for all groups were modelled with using of regression analysis and enhanced by neural networks. Furthermore, goal and constraint functions were set and optimization algorithms were used in order to obtain optimal resources depending on the given system reliability. All the results of the applied algorithms were compared. The new model in practice serve as a tool to support the management of passenger car maintenance in making strategic decisions about resource planning with regard to available financial resources or the required reliability of the entire rolling stock.