This study tackles the multi-objective optimization (MOP) challenges in constructing steel-concrete columns by introducing a novel TCQE model that considers time, cost, quality, and carbon emissions. Employing relativ...
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This study tackles the multi-objective optimization (MOP) challenges in constructing steel-concrete columns by introducing a novel TCQE model that considers time, cost, quality, and carbon emissions. Employing relative deviation theory with dynamic weighting, the model normalizes MOP and applies an improved ant colony algorithm (IACA) to generate optimized solutions. Empirical research validates the model's efficiency and applicability. Furthermore, a decision-making framework based on AHP-TOPSIS is proposed, demonstrating superior weight distribution and fairness in the decision process. Compared to the ideal point method and VIKOR, the proposed approach consistently identifies optimal solutions, affirming its scientific validity and effectiveness. The findings suggest broad application prospects in practical construction projects and provide valuable insights for construction management research, highlighting the theoretical and practical significance of the model and framework.
This paper presents a multi-objective optimization for the self-acting valve of the ionic liquid hydrogen compressor. A Taguchi method was employed to investigate the effects of four design parameters on the performan...
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This paper presents a multi-objective optimization for the self-acting valve of the ionic liquid hydrogen compressor. A Taguchi method was employed to investigate the effects of four design parameters on the performance indicators of energy storage (E) and isothermal efficiency (eta). The signal-to-noise ratio (SNR) and mean signal-to-noise ratio (MSNR), along with analysis of variance (ANOVA), were used to identify the critical design parameters and their contribution rates to the compressor's performance. A bi-objectiveoptimization of E and eta was conducted with grey relational analysis (GRA) employed to obtain the parameter combination closest to the optimal setting. The results indicated that the Mach number had the greatest impact on E and eta, with contribution rates of 61.59 % and 81.85 %, respectively. The optimal design parameters were identified as spring stiffness of 100 N/m, Mach number of 0.015, valve disc density of 8.9 g/cm3, and initial liquid piston height of 50 mm.
This article addresses multi-objective optimization of a freight allocation problem and presents the case of a food grain organization in India (FOI). The inventory and warehouse parameters that are relevant in the re...
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This article addresses multi-objective optimization of a freight allocation problem and presents the case of a food grain organization in India (FOI). The inventory and warehouse parameters that are relevant in the regional level allocation of food grains (using freight trains) are represented using three penalty factors, namely rake penalty factor, capacity utilization penalty factor, and weekly penalty factor. The article formulates a tri-objectiveoptimization model to minimize each of the three penalty factors. Two customized multi-objective optimization algorithms are developed based on multi-objective Simulated Annealing (MOSA) and Elitist Non-dominated Sorting Genetic Algorithm II (NSGA II) to solve the formulated model. The algorithms are tested and validated via computational experiments designed using historical data collected from the FOI. The algorithms help the transportation managers at the FOI to generate improved and balanced transportation plans (with respect to the three objectives) in a quick time. Further, the performance of the algorithms is compared based on seven different performance metrics reported in literature. The MOSA-based algorithm performs equally or better than the NSGA II-based algorithm with respect to four performance metrics.
Rural areas possess abundant renewable energy sources, such as solar and biomass energy;however, the current methods of energy utilization suffer from low efficiency and serious pollution issues. As rural residents...
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Rural areas possess abundant renewable energy sources, such as solar and biomass energy;however, the current methods of energy utilization suffer from low efficiency and serious pollution issues. As rural residents' living standards continue to improve, there is an urgent need to optimize and adjust the structure of rural energy systems. multi-energy complementary systems (MECS) have the potential to enhance energy utilization efficiency, achieve high efficiency and energy savings, significantly reduce carbon emissions, and effectively address the challenges faced by rural energy development. This study explores a typical framework for rural MECS that integrates photovoltaic, wind turbine, and biomass biogas combined cooling, heating, and power technology while considering the partial load ratio of equipment components and coupling characteristics between different energy sources. Based on various scenarios of valley electricity utilization, multi-objective optimization models are established to determine the capacity of MECS with economy, environment, and primary energy saving rate as objective functions. The non-dominated sorting genetic algorithm (NSGA-II) along with Technique for Order Preference by Similarity to Ideal Solution decision-making method is adopted to obtain optimal solutions from the Pareto solution set. The case study conducted in a rural area of central China has demonstrated the effective enhancement of coupling capacity in MECS through battery storage. By actively storing energy during off-peak electricity periods, battery storage strengthens the complementary capabilities of photovoltaic systems, wind turbines, and itself. This approach allows for a reduction in planned capacity for photovoltaic and wind power systems within MECS while increasing the planned capacity for internal combustion engines, resulting in respective decreases in system investment costs by 16.19% and 13.18%. Furthermore, incorporating more biogas-fired cogeneration durin
Studies have shown that the morphology of residential blocks has a significant impact on the buildings' energy use intensity (EUI), solar energy utilization potential (SEUP), and average sunlight hours (ASH). This...
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Studies have shown that the morphology of residential blocks has a significant impact on the buildings' energy use intensity (EUI), solar energy utilization potential (SEUP), and average sunlight hours (ASH). This paper utilizes the Rhino and Grasshopper platforms, employing the Wallacei multi-objective optimization algorithm, to study the relationship between the morphology of residential blocks in Xingtai City, EUI, SEUP, and ASH. First, 108 residential blocks in Xingtai City were surveyed, based on varying design criteria, they were classified into three categories: multi-story, high-rise Type I, and high-rise Type II. Next, after integrating microclimatic factors, the Wallacei multi-objective optimization algorithm was employed to optimize three objectives: EUI, SEUP, and ASH. Finally, the simulation results were subjected to a quantitative analysis using statistical methods, such as K-means clustering. The spatial morphology of residential blocks had a maximum impact of 11.69% on EUI, 39.8% on SEUP, and 36.85% on ASH. Therefore, energy saving can be achieved by controlling the building density, average number of floors, building shape factor and other morphological indicators of residential blocks.
Many factors affect the quality of the injection molding of plastic products, including the process parameters, mold materials, type and geometry of plastic parts, cooling system, pouring system, etc. A multi-objectiv...
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Many factors affect the quality of the injection molding of plastic products, including the process parameters, mold materials, type and geometry of plastic parts, cooling system, pouring system, etc. A multi-objective optimization method for injection molding process parameters based on the BP neural network and NSGA-II algorithm is proposed to address the problem of product quality defects caused by unreasonable process parameter settings. Taking the junction box shell as the object, numerical simulation was carried out using Moldflow2019 software and a six-factor five-level orthogonal experiment was designed to explore the influence of injection molding process parameters, such as the mold temperature, melt temperature, injection pressure, holding pressure, holding time, and cooling time, on the volume shrinkage rate and warpage deformation of the junction box. Based on a numerical simulation, the BP neural network and NSGA-II algorithm were used to optimize the optimal combination of injection molding process parameters, volume shrinkage rate, and warpage deformation. The research results indicate that the melt temperature has the most significant impact on the quality of the injection molding of junction boxes, followed by the holding time, holding pressure, cooling time, injection pressure, and mold temperature. After optimization using the BP neural network and the NSGA-II algorithm, the optimal process parameter combination was obtained with a melt temperature of 230.03 degrees C, a mold temperature of 51.27 degrees C, an injection pressure of 49.13 MPa, a holding pressure of 69.01 MPa, a holding time of 15.48 s, and a cooling time of 34.91 s. At this time, the volume shrinkage rate and warpage deformation of the junction box were 6.905% and 0.991 mm, respectively, which decreased by 33.2% and 3.8% compared to the average volume shrinkage rate (10.34884%) and warpage deformation (1.030764 mm) before optimization. The optimization effect was significant. In a
The construction industry accounts for around 30% of global energy consumption and 33% of CO2 emissions. For the carbon neutrality initiative, reducing carbon emissions from construction projects become a critical obj...
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The construction industry accounts for around 30% of global energy consumption and 33% of CO2 emissions. For the carbon neutrality initiative, reducing carbon emissions from construction projects become a critical objective for project success. However, a dilemma arises in balancing carbon emissions and project cost, particularly during the work package-based project planning phase. To address this issue, this article presents a novel multiobjectiveoptimization model for the work package scheme problem, aimed at minimizing both project carbon emissions and cost. multi-objective Evolutionary Algorithms (EAs) are developed to solve the model. Firstly, a multi-objective Mixed-Integer Programming (MIP) model is developed to establish the functional relation between work package attributes (duration and work content) and optimizationobjectives (carbon emissions and cost). Secondly, two multi-objective optimization EAs, NSGA-II and SPEA2, are developed to obtain the Pareto frontier. The experimental results indicate that NSGA-II and SPEA2 exhibit superior trade-off capabilities compared to the Gurobi and the state-of-the-art heuristic algorithm. Compared to Gurobi, the proposed EAs achieve an approximately 68% reduction in carbon emissions, accompanied by about an 11% cost increase. Compared to the heuristic algorithm, the EAs achieve around 10% reductions in carbon emissions with an approximately 5% cost increase. Additionally, sensitivity analysis conducted on a project instance dataset demonstrates the robustness of the proposed model and algorithms. This article paves the way for achieving lowcarbon and sustainable construction project management in the context of carbon neutrality.
Carbon Fiber Reinforced Thermoplastic Plastic (CFRTP) has been increasingly used in aerospace and automotive manufacturing with its excellent mechanical properties. Based on the melt-curing characteristics of thermopl...
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Carbon Fiber Reinforced Thermoplastic Plastic (CFRTP) has been increasingly used in aerospace and automotive manufacturing with its excellent mechanical properties. Based on the melt-curing characteristics of thermoplastic composites, combining the full-thickness reinforced joining technology with induction welding can provide an effective way for high-strength joining. In this paper, the full factorial experimental design method is used to deeply explore the influence law of welding time, consolidation force and heating current on the tensile properties of welded joints. Combined with Sparrow Search Algorithm (SSA) and BP neural network, a welding joint tensile strength prediction model was constructed. In addition, a multi-objective model based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was developed. The multi-objective optimization method of TOPSIS was used to select the optimal parameter combinations with ultimate tensile strength and first debonding strength as the optimizationobjectives: welding time of 29.995 min, consolidation force of 638.669 N, and heating current of 443.351 A. Experimental studies have shown that the optimized welding joints have an increase in ultimate tensile strength of up to 32.4 % and an increase in first time debonding strength of up to 47.0 % with respect to the non-optimized welding joints.
As a green and low-carbon cooling technology, the improvement of evaporative cooling performance has always been the focus of attention. However, traditional research often lacks concurrent consideration of trade-offs...
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As a green and low-carbon cooling technology, the improvement of evaporative cooling performance has always been the focus of attention. However, traditional research often lacks concurrent consideration of trade-offs in design parameters and multi-objective contradictions, focusing on unilateral decision-making and localized optimization in fixed environments. Therefore, this study innovatively couples non-dominated sorting genetic algorithm II with mathematical modeling. Based on MATLAB programming, a complex multi-objective optimization model of counter-flow dew-point evaporative cooler capable of parameter prediction, multi-scenario application and multi-dimensional optimization is developed. To reveal the driving mechanism of multivariable on performance parameters, comparative studies of single-objectiveoptimization under three decision modes for two typical environments are reported. The results indicate that the better optimization is achieved by adopting five decision variables with a dew-point efficiency of 98.25 %. Compared with the original working condition, the cooling capacity and dew-point efficiency could be unilaterally increased by 128.73 % and 121.28 %, respectively, and the corresponding increment of space utilization rate reaches up to 56.39 % and 56.11 %. Subsequently, setting the cooling capacity, dew-point efficiency and fan energy consumption as the synergistic objective function, a trade-off optimization with multi-decision variables is performed for four different latitudes. The obtained Pareto frontier could flexibly invert the most potential structural and operation parameter recommendations. Especially in dry regions, a cooling capacity of 4800 W and a dew-point efficiency of 91.2 % could be realized. The method realizes the controllability of performance parameters and adjustability of energy-saving effect, which could provide a solution for the efficient design of cooling equipment.
In this study, a multi-objective optimization methodology is used to assess the crashworthiness of an aluminum foam-filled battery box designed for passenger cars. Unlike most research focusing on axial crushing, this...
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In this study, a multi-objective optimization methodology is used to assess the crashworthiness of an aluminum foam-filled battery box designed for passenger cars. Unlike most research focusing on axial crushing, this work investigates the less-explored side pole impact scenario in electric vehicle battery boxes. Finite element simulations are conducted to reduce peak crushing force (PCF) and increase specific energy absorption (SEA) compared to the initial design. Key design variables include aluminum foam densities, wall thickness, and cross-sectional dimensions of battery box components. Four surrogate models are evaluated to approximate the simulation results, and the Non-Dominated Sorting Genetic Algorithm (NSGA-II) is employed to achieve optimal outcomes. The results show that the optimized design significantly improves crashworthiness, achieving a 50.71% increase in SEA and an 11.56% reduction in PCF. Foam density plays a crucial role in controlling deformation behavior under impact conditions. These findings offer a new approach to designing battery boxes with enhanced crashworthiness for electric vehicles.
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