Multi-objective Evaluation Research Articles

The Potential of Green Engineering Solutions for Energy Conservation in Residential Buildings Towards Sustainability: A Case Study of Saudi Arabia

Developing countries in hot climate regions such as Saudi Arabia have witnessed rapid population growth, which has led to greater resource consumption as a result of the increased demand for new buildings. This research proposes a multi-objective evaluation of the potential green engineering solutions to conserve energy using a building within the ROSHN housing project, which is one of the mega projects in Saudi Arabia, as a case study for this paper with the aid of simulation software, taking into consideration the context of the sustainability concept. The results showed that traditional passive architectural design, whether courtyards or Mashrabiya, had the nearly greatest influence, with percentages ranging from −4% to −5.15% for varied parameters and designs compared to the base case energy usage. Furthermore, energy efficiency solutions for the building envelope’s external insulation and finish system (EIFS) enabled a drop in the U-value that lowered energy usage to −5.40%. However, the wall insulation thickness beyond 300 mm in this system has no substantial influence on energy savings. This research’s most clear finding is that a P2P system for PV panels on a district scale can supply enough energy to meet its needs after implementing the optimal strategy of the other proposed solutions.

Training model of practical innovative talents in polytechnic colleges based on fuzzy set and its extended modeling

Currently, some problems still exist in the cultivation system of innovative and entrepreneurial talents. Therefore, the education department should also continue to carry out practice on the relevant talent training mode, to determine the talent training mode most suitable for the growth of polytechnic colleges. Colleges and universities should take entrepreneurship and innovation ability as the fundamental purpose so that students are not only job seekers, but also creators. The fuzzy set and its extended modelling are crucial for developing students' innovation skills. Therefore, on this basis, this paper proposed a model for cultivating innovative talents with fuzzy sets as the core. The fuzzy comprehensive evaluation (FCE) method is one of the most widely utilized and efficient evaluation methods, which applies to multi-objective evaluation models, thus reflecting the comprehensive and complex nature of the assessment. The ultimate purpose was to derive the decision rules and help the decision-makers make better decisions. The experimental findings of this article showed that there were 3 and 4 students with strong practical ability in Model 1 and Model 2 before the experiment, and there were 8 and 33 students with strong practical ability in Model 1 and Model 2 after the experiment.

Multi-Objective Disassembly Sequence Planning in Uncertain Industrial Settings: An Enhanced Water Wave Optimization Algorithm

Disassembly plays a pivotal role in the maintenance of industrial equipment. However, the intricate nature of industrial machinery and the effects of wear and tear introduce inherent uncertainty into the disassembly process. The inadequacy in representing this uncertainty within equipment maintenance disassembly has posed an ongoing challenge in contemporary research. This study centers on disassembly sequence planning (DSP) in the context of industrial equipment maintenance, with a primary aim to mitigate the adverse effects of uncertainty. To effectively address this challenge, we introduce a multi-objective DSP problem and utilize triangular fuzzy numbers from fuzzy logic to manage uncertainty throughout the disassembly process. Our objectives encompass minimizing disassembly time, reducing tool changes and directional reversals, and improving responsiveness to emergency maintenance needs. Recognizing the complexities of this problem, we present an innovative multi-objective enhanced water wave optimization (EWWO) algorithm, integrating propagation, refraction, and breaking wave operators alongside novel local search strategies. Through rigorous validation with real-world industrial cases, we not only demonstrate the algorithm’s potential in solving disassembly maintenance challenges but also underscore its exceptional performance in producing high-quality and efficient solutions. In comparison to other algorithms, EWWO provides significant advantages in multi-objective evaluation metrics, including Hypervolume (HV), Spread, and CPU time. Moreover, the application of triangular fuzzy numbers offers a comprehensive evaluation of solutions, empowering decision makers to make informed choices in diverse scenarios. Our findings lead to the conclusion that this research provides substantial support for addressing uncertainty in the field of industrial equipment maintenance, with the potential to significantly enhance the efficiency and quality of disassembly maintenance processes.

Task allocation in multi-AUV dynamic game based on interval ranking under uncertain information

This paper presents a novel game allocation method for solving the task allocation problem in underwater multi-AUV dynamic confrontations. The proposed method addresses incomplete information using a multi-objective evaluation model and interval ranking. An improved particle swarm optimization (PSO) algorithm, incorporating a hybrid strategy, is introduced to determine the Nash equilibrium of the game model. Firstly, the paper introduces the problem of underwater multi-AUV confrontations and establishes a game model based on interval numbers. Secondly, it develops a multi-target interval evaluation model considering survival value, strike income, and ammunition consumption. Combined with the analytic hierarchy process (AHP), each objective in the evaluation model is weighted to adapt to the battlefield situation with changing confrontation tasks and decision preferences. TOPSIS, relative entropy, and probability degree sort the interval income for optimization. Finally, an improved PSO algorithm called GSPSO, combining the good point set theory (G) and speed control factor (S), solves the optimal allocation problem. Simulation results demonstrate that the probability degree-based ranking method achieves higher resolution incomes, aiding precise optimization. The algorithm improves convergence speed, accuracy, and global optimization ability by increasing population diversity and iterative effectiveness, ensuring real-time decision-making in complex game scenarios.

Health Prognostics Classification with Autoencoders for Predictive Maintenance of HVAC Systems

Buildings’ heating, ventilation, and air-conditioning (HVAC) systems account for significant global energy use. Proper maintenance can minimize their environmental footprint and enhance the quality of the indoor environment. The adoption of Internet of Things (IoT) sensors integrated into HVAC systems has paved the way for predictive maintenance (PdM) grounded in real-time operational metrics. However, HVAC systems without such sensors cannot leverage the advantages of current data-driven PdM techniques. This work introduces a novel data-driven framework, the health prognostics classification with autoencoders (HPC-AE), designed specifically for PdM. It utilizes solely HVAC power consumption and outside temperature readings for its operations, both of which are readily obtainable. The primary objective of the HPC-AE framework is to facilitate PdM through a health prognostic approach. The HPC-AE framework utilizes an autoencoder for feature enrichment and then applies an artificial neural network to classify the daily health condition of an HVAC system. A multi-objective evaluation metric is employed to ensure optimal performance of the autoencoder within this framework. This metric evaluates the autoencoder’s proficiency in reducing reconstruction discrepancies in standard data conditions and its capability to differentiate between standard and degraded data scenarios. The HPC-AE framework is validated in two HVAC fault scenarios, including a clogged air filter and air duct leakage. The experimental results show that compared to methods used in similar studies, HPC-AE exhibits a 5.7% and 2.1% increase in the F1 score for the clogged air filter and duct leakage scenarios.

Market power evaluation in the electricity market based on the weighted maintenance object

This article proposes a market power evaluation model for multiperiod and multiobject evaluation in the hydrodominated electricity market. This study aims to comprehensively evaluate the market power of hydropower enterprises in multiple periods, accurately identify the abuse of market power, and avoid monopolizing the market by hydropower enterprises. The market power evaluation model is constructed based on the improved weighted maintenance object (WMO), and the market power is evaluated through strength values. The developed model is applied to evaluate the market power problem involving the 52 hydropower stations owned by 16 owners in the southwestern region of China. The simulation results indicate that the individual market power of hydropower stations and owners with large installed capacity is generally higher. Moreover, the collusion market power of hydropower stations and owners is generally lower while the runoff is abundant. The abuse of collusion market power existed more commonly than that of individual market power. Furthermore, compared with the evaluation results of other multicriteria decision-marking models, the proposed model has better discrimination ability and higher agreement, and is more suitable for market power evaluation in this article. The research findings will help regulators supervise the market power of the hydrodominated electricity market and reduce the occurrence of electricity monopolies.

Research on Automatic Vertical Parking Path-Planning Algorithms for Narrow Parking Spaces

Narrow parking spaces pose difficulties in path planning and spot turning caused by sudden changes and discontinuities in path curvature. To address these problems, this paper investigates the performance of three path-planning algorithms and proposes a path-optimization algorithm. First, a narrow parking space is defined based on single-step parking using the arc-line combination parking algorithm. Second, to compare the arc-line combination algorithm, Hybrid A* algorithm, and particle swarm optimization parking algorithm with respect to different narrow parking spaces, a multi-objective evaluation function is proposed, including three evaluation indicators, namely, the path length, the number of positive and negative conversions of vehicle speed, and the smoothness of the path. Their performance is compared using a simulation conducted in MATLAB. With the same starting point and different parking space widths, the three algorithms are simulated to generate different planned paths. Then, the evaluation indices are obtained to compare the performance of the algorithms based on the multi-objective function, the values of which indicate the fitness of the algorithm in a narrow parking environment. The results show that the Hybrid A* algorithm is better than the others for narrow parking spaces. Third, to smooth the planning path, a path optimization algorithm based on the cubic B-spline curve and gradient descent is proposed. Finally, the results of a simulation conducted on the proposed algorithm and the Hybrid A* algorithm are provided: the average minimum curvature of the path was reduced by 0.005 m−1, and the path meets the requirements of the minimum turning radius constraint of the analyzed vehicle. The results show that the proposed algorithm can effectively eliminate the curvature mutation point and constrain the path curvature to meet the requirements of a smooth path.

Assembly sequence planning and evaluating for deep oil and gas corer based on graph theory

Fidelity corers play a crucial role in exploring and evaluating deep oil and gas resources. Due to the complexity of the corer parts, an improper assembly sequence can lead to inefficiencies and reduced reliability, impacting the success of obtaining oil and gas samples. This paper presents a novel assembly sequence planning method based on graph theory and multi-objective evaluation. The graph-theoretic cut set algorithm is utilized to generate all possible assembly sequences based on a directed graph which represents corer component assembly information. To address deep oil and gas corer requirements, the interpretative structural modeling method (ISM) is employed to construct a comprehensive stability-reliability-accuracy assurance-easiness-cost economy (SRAEC) indicator system. Through indicators weight assignment and multi-objective evaluation, the optimal solution of assembly sequence is identified from a global perspective. The proposed method is applied to a deep-sea gas hydrate corer, resulting in the identification of the most suitable assembly sequence for both field drilling and laboratory testing. This optimized sequence improves the assembly reliability and efficiency, contributing to the overall success and efficiency of the corer in obtaining oil and gas samples. The study provides valuable insights for designing and practically applying deep oil and gas coring tools.

Enhanced Multi-Objective Evolutionary Algorithm for Green Scheduling of Heterogeneous Quay Cranes Considering Cooperative Movement and Safety

Heterogeneous quay cranes (HQCs) are the main energy-consuming equipment of automated container terminals, and they need to move from one bay to another along the rail and maintain a safe distance from one another. Improving the operational efficiency of HQCs and reducing the ineffective walking distance of HQCs are key to reducing the energy consumption of QCs. In this paper, an energy-efficient HQC cooperative scheduling problem is studied, and the HQCs are required to ensure safe and efficient operation. A multi-objective scheduling model is formulated to minimize the maximum completion time of containers, the average completion time of HQCs, and the total energy consumption of HQCs simultaneously. An Enhanced Multi-Objective Evolutionary Algorithm (EMOEA) is designed to solve this problem using a problem-feature-based encoding method to encode and initialize the population, a cooperative strategy to ensure the safe operating distance of HQCs, and a novel multi-objective evaluation mechanism with effective evolutionary operators. The results indicate that the different operational capacities of HQCs had a significant impact on the three studied objectives, especially for some large-scale problems, and that our algorithm outperforms three other well-known multi-objective algorithms in solving the EHQCCSP.

An Integrated Model for Multi-Mode Resource-Constrained Multi-Project Scheduling Problems Considering Supply Management with Sustainable Approach in the Construction Industry under Uncertainty Using Evidence Theory and Optimization Algorithms

In this study, the multi-mode resource-constrained multi-project scheduling problems (MMRCMPSPs) considering supply management and sustainable approach in the construction industry under uncertain conditions have been investigated using evidence theory to mathematical modeling and solving by multi-objective optimization algorithms. In this regard, a multi-objective mathematical model has been proposed, in which the first objective function aims to maximize a weighted selection of projects based on economic, environmental, technical, social, organizational, and competitive factors; the second objective function is focused on maximizing profit, and the third objective function is aimed at minimizing the risk of supply management. Moreover, various components, such as interest rates, carbon penalties, and other implementation limitations and additional constraints, have also been considered in the modeling and mathematical relationships to improve the model’s performance and make it more relevant to real-world conditions and related issues, leading to better practical applications. In the mathematical modeling adopted, the processing time of project activities has been considered uncertain, and the evidence theory has been utilized. This method can provide a flexible and rational approach based on evidence and knowledge in the face of uncertainty. In addition, to solve the proposed multi-objective mathematical model, metaheuristic optimization algorithms, such as the differential evolution (DE) algorithm based on the Pareto archive, have been used, and for evaluating the results, the non-dominated sorting genetic algorithm II (NSGA-II) has also been employed. Furthermore, the results have been compared based on multi-objective evaluation criteria, such as quality metric (QM), spacing metric (SM), and diversity metric (DM). It is worth noting that to investigate the performance and application of the proposed model, multiple evaluations have been conducted on sample problems with different dimensions, as well as a case study on residential apartment construction projects by a contracting company. In this respect, the answers obtained from solving the model using the multi-objective DE algorithm were better and superior to the NSGA-II algorithm and had a more favorable performance. Generally, the results indicate that using the integrated multi-objective mathematical model in the present research for managing and scheduling multi-mode resource-constrained multi-project problems, especially in the construction industry, can lead to an optimal state consistent with the desired objectives and can significantly improve the progress and completion of projects.

Multi-objective evaluation and operation strategy analysis for combined cooling, heating and power (CCHP) systems

Multi-objective evaluation and operation strategy analysis for combined cooling, heating and power (CCHP) systems

Efficient Thermal-Stress Coupling Design of Chiplet-Based System with Coaxial TSV Array.

In this research, an efficient thermal-stress coupling design method for a Chiplet-based system with a coaxial through silicon via (CTSV) array is developed by combining the support vector machine (SVM) model and particle swarm optimization algorithm with linear decreasing inertia weight (PSO-LDIW). The complex and irregular relationship between the structural parameters and critical indexes is analyzed by finite element simulation. According to the simulation data, the SVM model is adopted to characterize the relationship between structural parameters and critical indexes of the CTSV array. Based on the desired critical indexes of the CTSV array, the multi-objective evaluation function is established. Afterwards, the structural parameters of the CTSV array are optimized through the PSO-LDIW algorithm. Finally, the effectiveness of the developed method is verified by the finite element simulation. The simulated peak temperature, peak stress of the Chiplet-based system, and peak stress of the copper column (306.16 K, 28.48 MPa, and 25.76 MPa) well agree with the desired targets (310 K, 30 MPa, and 25 MPa). Therefore, the developed thermal-stress coupling design method can effectively design CTSV arrays for manufacturing high-performance interconnect structures applied in Chiplet-based systems.

NSGA-III integrating eliminating strategy and dynamic constraint relaxation mechanism to solve many-objective optimal power flow problem

Increasing optimizing criteria in modern power systems promotes the birth of many-objective optimal power flow (Ma-OPF) problems in which more than three optimizing objective functions are considered. The increasing optimizing objectives as well as complex constraints of power balance and other system limits bring a decrease in selection pressure and challenges of constraint handling techniques for traditional multi-objective evaluation algorithms. Aiming at the solving difficulties of Ma-OPF problems, an improved NSGA-III integrating eliminating strategy and dynamic constraint relaxation mechanism (NSGA-III-EDR) is proposed. In the proposed approach, a new elimination strategy that eliminates the associated points having the largest angle with the corresponding reference line is employed to increase the selection pressure of the algorithm. An integrated constraint handling method, which employs a repair strategy based on assigning decision variables to feasible values and a penalty function approach together to handle power flow equality constraints, is also introduced into the NSGA-III-EDR. Aiming at lacking feasible solutions of the algorithm in the early searching stage, a strategy of relaxing constraint violations as well as a dynamic updating strategy for the tolerated threshold value of distinguishing feasible and infeasible solutions at the early evolution is proposed. Moreover, an improved domination sorting rule based on the proposed constraint handling method and the relaxing strategy to constraint violations is employed to promote the generation of feasible solutions. The effectiveness and feasibility of the proposed improved NSGA-III-EDR approach are studied and evaluated on different test cases of a famous standard IEEE 30-bus power system as well as the larger power systems of IEEE 57-bus and IEEE 118-bus. The numerical results show that the proposed NSGA-III-EDR method can provide solutions to many-objective OPF problems with tremendous potential compared with the traditional NSGA-III and other algorithms illustrated in the literature.

Multi-Objective Control Strategy for Switched Reluctance Generators in Small-Scale Wind Power Generations

Switched reluctance generators (SRGs) are widely used in wind power generation. However, due to the natural tendency of SRG, there are always nonnegligible conflicts to achieve high efficiency and low output voltage ripple at the same time. This creates difficulties for the high-performance of SRG. Thus, a multi-objective optimization control strategy is proposed in this paper to improve the static performance of SRG. The proposed control strategy contains following steps. First, in order to gain the maximum output power range at different rotor speeds, the turn-off angle is optimized off-line by simulated annealing algorithm (SAA). The optimized results are fitted as a function of rotor speed for on-line regulating; then, a closed-loop controller is built, and the reference current is regulated according to the difference between actual output power and required output power. Second, a multi-objective function is constructed as the evaluation result of SRG performance, which takes system efficiency, output voltage ripple and power converter loss into consideration. In the end, the turn-on angle is tuned by SAA according to the real-time multi-objective evaluation result. The proposed control strategy can be flexibly applied to SRGs with different structures and avoids the disadvantage of single-objective optimization. The simulation and experiments results show that the overall performance of SRG is improved.

Multi-objective evaluation of different retrofitting scenarios for a typical Greek building

In this study, multiple retrofitting scenarios of a typical Greek house were investigated. These scenarios encompass the addition of insulation to the building’s external walls and roof, a green roof construction, the use of high-quality windows and the installation of an efficient heat pump. The aforementioned techniques were simulated separately and in combination with the use of the DesignBuilder software, on an annual basis, for the city of Athens, Greece. The pivotal innovation of this work lays in the energy simulation and the comprehensive comparison of the 34 retrofitting scenarios according to a multi-objective evaluation procedure. An additional novelty was the investigation of the energy impact of each renovation action when applied separately. The goal of the multi-objective evaluation procedure was to determine the optimum renovation case, for maximizing the energy savings and minimizing the life cycle cost. According to the results, the optimum retrofitting case included the installation of insulation to the building’s external surface and the heat pump retrofitting. The optimum scenario resulted in a 41.7% reduction in the building’s heating loads and a 14.9% reduction in its cooling loads. Finally, the primary energy savings were 66.1% and the life cycle cost analysis showed 6,645 € lifespan savings.

Reverse Logistics Network Model of Dual-Channel Recycling Boxes Based on Genetic Algorithm Optimization: A Multi-Objective and Uncertain Environment Perspective

In the context of carbon neutrality, plastic ban, and green development, this paper aims to maximize the comprehensive interest of manufacturers in building a sustainable logistic network. It proposes a reverse logistics network model of a dual-channel model with multiple objectives and a random environment for the construction of recycling boxes projects and uses a Stackelberg game to develop pricing strategies for dual-channel recyclers and a genetic algorithm to optimize reverse logistics networks. This paper found the following: multi-objective evaluation is more conducive to sustainable development; when consumers are price-sensitive, a stronger rebate can significantly increase revenue; online platform recyclers should invest more in marketing than traditional recyclers; retailers’ willingness to cooperate in recycling has a significant impact on the overall benefits; the impact of government subsidies is marginal and needs to be controlled to a certain extent; the impact of green credit is insignificant, and the strength of commercial banks’ scrutiny of companies has little effect; an increase in environmental uncertainty within a certain range can lead to an overall loss of benefits, and an excessive impact can be out of line with extreme values. It provides a basis for decision-making on strategies for enterprises to build a logistics network of recycling boxes, government subsidies, green credit from banks, and dual-channel recyclers.

Multi-objective optimization methodology for green-gray coupled runoff control infrastructure adapting spatial heterogeneity of natural endowment and urban development

Cost-effective runoff control scheme drafting involves localization, multi-sector coordination, and configuration of multifunctional infrastructures. Numerous independent variables, parameters, weights, and objectives make runoff control optimization quantitatively arduous. This study innovatively proposed a multi-objective optimization methodology for green-gray coupled runoff control infrastructure adapting spatial heterogeneity of natural endowment and urban development. The quantitative methods of multi-objective evaluation, hydrological feature partition, and pressure-adapted multi-objective weight assignment were proposed. Remote sensing inversion of water quality, hydrological model simulation (using SWAT and SWMM software), landscape pattern index calculation, life cycle cost (LCC), life cycle assessment (LCA) on ecological impact, and NSGA-II optimization algorithm were applied. Wuhan, the most water-sensitive city in China, was studied as a case. Runoff control function (RCF), capital investment (CI), and ecological return on investment (EROI) served as optimized objectives. High, medium, and low built-up regions in Wuhan urban development planning district were extracted by topographic factors and landscape patterns, which comprised 28, 34, and 38% of the case area, respectively. Three corresponding hydrological models were then built to illustrate distinct runoff control cost-efficiency in each region. Pressure distributions on runoff control, economic constraints, and ecological resource scarcity were quantitatively evaluated. And four pressure zones were clustered, which occupied 36, 29, 16, and 19% of the case area, respectively. Then the zonal weighted optimization decision-making matrix (with 3 hydrological models and 5 wt) was established by overlaying the pressure zone and built-up zone. In high, medium, and low built-up regions, optimized solutions reduced annual runoff volume by 86, 82%, and 77%The average runoff investments per square meter of impervious underlying surface in high, medium, and low built-up regions were 34.2, 18.7, and 7.9 RMB yuan, respectively. Medium and low built-up regions may only need 55 and 23% of the high built-up region for the unitary impervious underlying surface to balance runoff control and ecological benefits. Runoff control and financial utilization efficiency enhance with hydrological differentiation zones. Thus, the optimization solutions are zonal adaptive, refined, comparable, replicable, and implementable.

An Integrated Flow Shop Scheduling Problem of Preventive Maintenance and Degradation With an Improved NSGA-II Algorithm

The main objective of this research is to establish and solve a scheduling model for the degraded flow shop taking completion time and average device idle time as optimization objectives. Therefore, considering the interaction among schedule, production, maintenance and degradation, a mathematical model with completion time as well as average device idle time is constructed. Based on the NSGA-II algorithm, a local search strategy is first proposed to obtain junction points and sparse points according to the non-dominated sorted results. Subsequently, the comparative experiments are conducted utilizing NSGA-II-ALS, NSGA-II, and PSO algorithms. The optimal solution sets and Pareto diagrams of the three algorithms are obtained. The Gantt charts of the optimal solutions are drawn. Eventually, the multi-objective evaluation indexes of each algorithm are calculated and compared. Using statistical experiments and analysis, it can be found that NSGA-II-ALS exhibit excellent performance compared with other algorithms. The scheduling model proposed in this paper is able to effectively coordinate and balance the relationship among production, equipment maintenance and degradation, thus improving the efficiency of manufacturing systems, saving production costs, and realizing the optimal utilization of resources.

Thermal performance analysis and multi-objective optimization of thermal energy storage unit with cascaded packed bed in a solar heating system

• Established the CPBTES’s continuous solid phase simplified model and exergy analysis model. • The numerically predicted temperatures match well with the experimental measurements. • Compared the thermal performance of CPBTES and PBTES in the solar heating system. • The regression model of main influencing factors and evaluation indexes was established by the response surface method, • Obtained the optimal parameter combination of multi-objective optimization. The capacity expansion and optimal design of the TES has an important impact on the operational efficiency of the solar heating system. In this study, a CPBTES system with a three-stage PCM was designed for a solar heating system. A one-dimensional, two-phase model based on thermal resistance analysis was established, and the adaptability of the model was verified by different experiments. A multi-objective evaluation system was established, and the response surface methodology was used to carry out the influence factor analysis and multi-objective optimization. The result showed that in the same operating conditions, CPBTES had a more uniform temperature distribution than single-stage TES, which was the key factor to improve the exergy efficiency. CPBTES improved the ideal charging completion time and the exergy efficiency by 7% and 5%, respectively, and reduced the effective TES capacity by 4% compared to single-stage TES. The optimal combination of parameters after multi-objective optimization was aspect ratio of 1.97, dimensionless diameter of 0.03, void ratio of 0.24, and intermediate filling ratio of 0.66. The multi-objective optimization result and method in this study can contribute to new ideas for similar engineering problems.

Multi Objective Evaluation Between Learning Behavior and Learning Achievement

Multi Objective Evaluation Between Learning Behavior and Learning Achievement