Search Results (3,852)

Alcohol-blended gasoline is recognized as an effective strategy for reducing carbon emissions during combustion and enhancing fuel performance. However, the carbon footprint associated with its production process in refineries deserves equal attention. This study introduces a refinery modeling framework to evaluate the long-term economic and environmental performance of utilizing alcohols derived from fossil, biomass, and carbon capture sources in gasoline blending processes. The proposed framework integrates Extreme Learning Machine-based models for gasoline octane blending, linear programming for optimization, carbon footprint tracking, and future trends in feedstock costs and carbon taxes. The results indicate that gasoline blended with coal-based alcohol currently exhibits the best economic performance, though its carbon footprint ranges from 818.54 to 2072.89 kgCO₂/t. Gasoline blended with biomass-based alcohol leads to a slight reduction in benefits and an increase in the carbon footprint. Blending gasoline with CCUM (CO₂ capture and utilization to methanol) results in the lowest economic performance, with a gross margin of 8.91 CNY/t_oil at a 30% blending ratio, but achieves a significant 62.4% reduction in the carbon footprint. In long-term scenarios, the additional costs brought by increased carbon taxes result in negative economic performance for coal-based alcohol blending after 2040. However, cost reductions driven by technological maturity lead to biomass-based alcohol and CCUM blending gradually showing economic advantages. Furthermore, owing to the negative carbon emissions characteristic of CCUM, the blending route with CCUM achieves a gross margin of 440.60 CNY/t_oil and a gasoline carbon footprint of 282.28 kgCO₂/t at a 20% blending ratio by 2050, making it the best route in terms of economic and environmental performance. Full article

Material handling is a widely used process in manufacturing and is generally considered a non-value-added process. The Dynamic Facility Layout Problem (DFLP) considered in this paper minimizes the total material handling and re-arrangement cost. In this study, an integrated DFLP model with unequal facility areas, assignment of material handling devices (MHD), and flexible bay structure (FBS) is considered, and it is aimed to propose fast solution approaches. Two different solution methods are proposed for the problem, which are the genetic algorithm and the simulated annealing algorithm, respectively. In both methods, a non-linear mathematical model solution was used to calculate the fitness values. Thus, the solutions in the feasible solution space are utilized. The proposed solution approaches were applied to solve four problems published in the literature. The computational experiments have validated the effectiveness of the algorithms and the quality of solutions produced. Full article

This paper provides a recent overview of the exact, approximate, and hybrid optimization methods that handle Multi-Objective Mixed-Integer Non-Linear Programming (MO-MINLP) problems. Both the domains of exact and approximate research have experienced significant growth, driven by their shared goal of addressing a wide range of real-world problems. This work presents a comprehensive literature review that highlights the significant theoretical contributions in the field of hybrid approaches between these research areas. We also point out possible research gaps in the literature. Hence, the main research questions to be answered in this paper involve the following: (1) how to exactly or approximately solve a MO-MINLP problem? (2) What are the drawbacks of exact methods as well as approximate methods? (3) What are the research lines that are currently underway to enhance the performances of these methods? and (4) Where are the research gaps in this field? This work aims to provide enough descriptive information for newcomers in this area about the research that has been carried out and that is currently underway concerning exact, approximate, and hybrid methods used to solve MO-MINLP problems. Full article

In some automated container terminals, railway lines have been implemented into the port, saving container transfer time. However, the equipment scheduling level of the railway yard needs to be improved for managers. In the equipment scheduling of loading and unloading containers for railway trains, the operation modes “full unloading and full loading” and “synchronous loading and unloading” are often adopted. Due to the long length of the railway yard and the line of one train, there are two ways to arrange loading and unloading tasks for automated rail-mounted gantry cranes (ARMGs): one is to pre-assign tasks for ARMGs, and the other is to not pre-assign tasks for ARMGs. To investigate the efficacy of these different operation modes and methods of assigning tasks, this study formulated three mixed-integer linear programming (MILP) models with the goal of minimizing the ARMG task completion time. An adaptive large neighborhood search algorithm was used to tackle the scheduling problem. The scheduling effects of different operation modes and methods for assignment tasks were compared in terms of their calculation time and the completion time of ARMG tasks. Notably, the findings reveal that, with an increase in the number of tasks, the “pre-assign” task arrangement had a limited effect on the completion time of the ARMG tasks, made the calculation time shorter, and reduced the complexity of the problem. From the perspective of the completion time of ARMG tasks, the time under the “synchronous loading and unloading” operation mode was less than that of the “full unloading and full loading” operation mode. Therefore, it is recommended that the managers of the railway yard in an automated container terminal adopt the “synchronous loading and unloading” operation mode but determine the task assignment method according to decision time requirements. In addition, when the number of tasks is large, to decrease the time to complete ARMG tasks, the manager can adopt the “non-pre-assign” task distribution method. Full article

Regular physical exercise has proven to be an effective strategy for enhancing the health and well-being of older adults. However, there are still gaps in our understanding of the impacts of exercise on older adults with different health conditions, as well as in the customization of training programs according to individual capabilities. This study aimed to analyze the variables that influence the response of physical capabilities in older adults, considering their development over the aging process, with the goal of assisting professionals in creating personalized training programs. To achieve this, we conducted a cohort study involving 562 previously inactive adults and older adults who underwent anthropometric assessments, blood pressure measurements, and comprehensive physical tests. These assessments were conducted before and after a 14-week training program. Results indicated no significant variations in variables such as waist circumference (p = 0.0455, effect size = 0.10), body mass index (p = 0.0215, effect size = 0.15), systolic (p < 0.0001, effect size = 0.35) and diastolic blood pressure (p < 0.0001, effect size = 0.25) pre- and post-intervention. Strength tests, agility, the 6 min walk test (6MWT), and the back scratch test (BS) showed significant improvements post-intervention, with p-values all below 0.0001 and effect sizes ranging from 0.30 to 0.50. Multiple linear regression analyses revealed that lower initial values in physical capabilities were associated with more significant improvements during training (R² = 0.73, p < 0.001). These results underscore that individualized guidance in training can lead to clinically meaningful improvements in physical performance and health among older adults, with effect sizes indicating moderate-to-large benefits (effect size range = 0.30 to 0.50). Therefore, personalized training programs are essential to maximize health benefits in this population. Full article

School mental health (SMH) teams have been widely recommended to support multi-tiered mental health program implementation in schools. Available research suggests emerging best practices that promote effective SMH teaming and indicates the importance of having team members who are highly engaged (e.g., actively involved, retained on the team). Despite evidence that these factors improve team functioning, there is limited knowledge of SMH team prevalence, best practice use, and factors impacting member engagement among a diverse sample of elementary schools. This study surveyed a cross-sectional sample of elementary principals (n = 314) across the United States whose schools implement multi-tiered SMH programs. Most principals (89%, n = 280) reported using teams to organize these programs. Schools in urban/suburban communities, with 300 or more students, or with specific school funding for SMH activities were more likely to have SMH teams. Only one-third of principals reported that their team members participated in related training. Other SMH team best practices were commonly reported (by two-thirds or more teams). Results of a linear regression model indicate that larger teams (six or more members) and teams with access to resources had significantly higher member engagement scores. The study’s findings provide recommendations for practice and future research directions. Full article

Globally, building energy consumption has been rising, emphasizing the need to reduce energy usage in the building sector to lower national energy consumption and carbon dioxide emissions. This study analyzes the applicability of photovoltaic (PV) systems in enhancing the energy self-sufficiency of small-scale, low-rise apartment buildings. The analysis is based on a case study using Republic of Korea’s Zero-Energy Building Certification System. By employing the ECO2 simulation program, this research investigates the impact of PV system capacity and efficiency on the energy self-sufficiency rate (ESSR). A series of parametric analyses were carried out for various combinations of building-attached photovoltaic (BAPV) roofs and building-integrated photovoltaic (BIPV) facades, considering the initial cost of BIPV facades. The simulations demonstrate that achieving the target ESSR requires a combination of BAPV roofs and BIPV facades, due to limited roof areas for PV systems. Additionally, this study reveals that BIPV facades can be cost-effective when their unit price, relative to BAPV roofs, is below 62%. Based on the ECO2 simulations, a linear regression formula is proposed to predict the ESSR for the case study building. Verification analysis shows that the proposed formula predicts an ESSR of 74.1%, closely aligned with the official ESSR of 76.9% certified by the Korean government. Although this study focuses on the case of a specific apartment building and lacks actual field data, it provides valuable insights for future applications of PV systems to enhance energy self-sufficiency in small-scale, low-rise apartment buildings in Republic of Korea. Full article

Mobile edge computing (MEC), specifically wireless powered mobile edge computing (WPMEC), can achieve superior real-time data analysis and intelligent processing. In WPMEC, different user nodes (UNs) harvest significantly different amounts of energy, which results in longer delays for lower-energy UNs when data are offloaded to MEC servers. This study involves quantifying the delays in energy harvesting and task offloading to edge servers in WPMEC via user cooperation. In this paper, a method for transferring the tasks that need to be offloaded to edge servers as quickly as possible is investigated. The problem is formulated as an optimization model to minimize the delay, including the time required for the energy harvesting and offloading tasks. Because the problem was non-deterministic polynomial hard (NP-hard), a delay-optimal approximation algorithm (DOPA) is proposed. Finally, with the training data generated based on the DOPA, a deep learning-based online offloading (DLOO) framework is designed for predicting the transmission power of each UN. After each UN’s transmission power is obtained, the original model is converted to a linear programming problem, which substantially reduces the computational complexity of the DOPA for solving the mixed-integer linear programming problem, especially in large-scale networks. The numerical results show that compared with the non-cooperation methods for WPMEC, the proposed algorithm significantly reduces the total delay. Additionally, in the delay optimization process for a scale of six UNs, the average computation time of the DLOO is only 0.2% that of the DOPA. Full article

Smart Transformer (ST)-based Meshed Hybrid AC/DC Microgrids (MHMs) present a promising solution to enhance the efficiency of conventional microgrids (MGs) and facilitate higher integration of Distributed Energy Resources (DERs), simultaneously managing active and reactive power dispatch. However, MHMs face challenges in resource management under uncertainty and control of electronic converters linked to the ST and DERs, complicating the pursuit of optimal system performance. This paper introduces a Data-Driven Distributionally Robust Optimization (DDDRO) approach for day-ahead operation planning in ST-based MHMs, focusing on minimizing network losses, voltage deviations, and operational costs by optimizing the reactive power dispatch of DERs. The approach accounts for uncertainties in photovoltaic generator (PVG) output and demand. The Column-and-Constraint Generation (C&CG) algorithm and the Duality-Free Decomposition (DFD) method are employed. The initial mixed-integer non-linear planning problem is also reformulated into a mixed-integer (MI) Second-Order Cone Programming (SOCP) problem using second-order cone relaxation and a positive octagonal constraint method. Simulation results on a connected MHM system validate the model’s efficacy and performance. The study also highlights the advantages of the meshed MG structure and the positive impact of integrating the ST into MHMs, leveraging the multi-stage converter’s flexibility for optimal energy management under uncertain conditions. Full article

This article proposes a novel robust invariance condition for uncertain linear discrete-time systems with state and control constraints, utilizing a method of semidefinite programming duality. The approach involves approximating the robust invariant set for these systems by tackling the dual problem associated with semidefinite programming. Central to this method is the formulation of a dual programming through the application of adjoint mapping. From the standpoint of semidefinite programming dual optimization, the paper presents a novel linear matrix inequality (LMI) conditions pertinent to robust positive invariance. Illustrative examples are incorporated to elucidate the findings. Full article

The scheduling problem in additive manufacturing is receiving increasing attention; however, few have considered the effect of scheduling decisions on machine energy consumption. This research focuses on the nesting and scheduling problem of a single selective laser melting (SLM) machine to reduce total energy consumption. Based on an energy consumption model, a nesting and scheduling problem is formulated, and a mixed integer linear programming model is proposed. This model simultaneously determines part-to-batch assignments, part placement in the batch, and the choice of build orientation to reduce the total energy consumption of the SLM machine. The energy-saving potential of the model is validated through numerical experiments. Additionally, the effect of the number of alternative build orientations on energy consumption is explored. Full article

Power quality measurements are essential to monitor, analyse and control the operation of smart grids within power systems. This work aims to develop and calibrate a PQ network analyser. As the penetration of non-linear loads connected to power systems is increasing every day, it is essential to measure power quality. In this sense, a power quality (PQ) analyser is based on the high-speed sampling of electrical signals in single-phase and three-phase electrical installations, which are available in real time for analysis using wireless Wi-Fi (Wireless-Fidelity) networks. The PQAE (Power Quality Analyser Embedded) power quality analyser has met the calibration standards for Class A devices from IEC 61000-4-30, IEC 61000-4-7 and IEC 62586-2. In this paper, a complete guide to the tests included in this standard has been provided. The Fast Fourier Transform (FFT) obtains the harmonic components from the measured signals and the window functions used reduce spectral leakage. The window size depends on the fundamental frequency of, intensity of and changes in the signal. Harmonic measurements from the 2nd to 50th harmonics for each phase of the voltage and each phase and neutral of the current have been performed, using the Fast Fourier transform algorithm with various window functions and their comparisons. PQAE is developed on an open-source platform that allows you to adapt its programming to the measurement needs of the users. Full article

In this paper, a fast method to generate a constant-modulus (CM) waveform for a multiple-input, multiple-output, (MIMO) radar is proposed. To simplify the optimization process, the design of the transmit waveform is decoupled from the design of transmit beamformers (TBs) and subpulses. To further improve the computational efficiency, the TBs’ optimization is conducted in parallel, and a linear programming model is proposed to match the desired beampattern. Additionally, we incorporate the perturbation vectors into the TBs’ optimization so that the TBs can be adjusted to satisfy the CM constraint. To quickly generate the CM subpulses with the desired range-compression (RC) performance, the classical linear frequency modulation (LFM) signal and non-LFM (NLFM) are adopted as subpulses. Meanwhile, to guarantee the RC performance of the final angular waveform, the selection of LFM signal parameters is analyzed to achieve a low cross-correlation between subpulses. Numerical simulations verify the transmit beampattern performance, RC performance, and computational efficiency of the proposed method. Full article

China’s economic development has achieved great success in recent years, but the problems of energy scarcity and environmental pollution have become increasingly serious. To enhance the reliability and efficiency between energy, the environment and the economy, sustainable development is an inevitable choice. In the context of measuring sustainability efficiency, a network data envelopment analysis model is proposed to formulate the two-stage process of energy use and operations management. A double frontier is derived to optimize the available energy for sustainable development. Due to nonlinearity, previous linear methods are not directly applicable to identify the double frontier and calculate stage efficiencies for inefficient decision-making units. To address this problem, this study develops the primal-dual relationship between multiplicative and envelopment network models based on the Lagrange duality principle of parametric linear programming. The newly developed approach is used to evaluate the sustainability efficiency of 30 administrative regions in China. The results show that insufficient sustainability efficiency is a systemic problem. Different regions should take different measures to conserve energy and reduce pollutant emissions for sustainable development. To increase sustainability efficiency, regions should support energy-saving and emission-reducing technologies in production processes and strengthen their capacity for technological innovation. Compared with energy use efficiency, operations management efficiency in China has a wider range of changes. During the operations management stage, there is not much difference between the capacity and quantity of each region. Based on benchmark regions at the efficiency frontier, there is an opportunity to improve operations management in the near future. Blockchain technology can effectively improve energy allocation efficiency. Full article

The manufacturing industry, with globalization and information technology, is facing cost pressure, low profits, technology difficulties and many other challenges. Enterprises need to strengthen cost management, improve production efficiency, and meet customer demand to cope with the increasingly complex competitive market. Here, our research in this area becomes important. This paper presents a new approach for building a smart decision-making process specifically in manufacturing organizations in terms of profitability. It emphasizes the importance of profitability-based decision making. The basic steps and modules required to build a profitability-based decision process are described. The advantages of this decision-making system are that it can help companies maximize profits, increase productivity, and meet customer demand for order adjustments. Full article