Fuzzy Decision-making Techniques Research Articles

Multi-Objective Energy Management in Microgrids: Improved Honey Badger Algorithm with Fuzzy Decision-Making and Battery Aging Considerations

A multi-objective energy management and scheduling strategy for a microgrid comprising wind turbines, solar cells, fuel cells, microturbines, batteries, and loads is proposed in this work. The plan uses a fuzzy decision-making technique to reduce pollution emissions, battery storage aging costs, and operating expenses. To be more precise, we applied an improved honey badger algorithm (IHBA) to find the best choice variables, such as the size of energy resources and storage, by combining fuzzy decision-making with the Pareto solution set and a chaotic sequence. We used the IHBA to perform single- and multi-objective optimization simulations for the microgrid’s energy management, and we compared the results with those of the conventional HBA and particle swarm optimization (PSO). The results showed that the multi-objective method improved both goals by resulting in a compromise between them. On the other hand, the single-objective strategy makes one goal stronger and the other weaker. Apart from that, the IHBA performed better than the conventional HBA and PSO, which also lowers the cost. The suggested approach beat the alternative tactics in terms of savings and effectively reached the ideal solution based on the Pareto set by utilizing fuzzy decision-making and the IHBA. Furthermore, compared with the scenario without this cost, the results indicated that integrating battery aging costs resulted in an increase of 7.44% in operational expenses and 3.57% in pollution emissions costs.

Multi-objective modeling of price and pollution in large-scale energy hubs with load management

This study introduces a novel multi-objective model that addresses emission costs, large-scale user operational expenses, and the efficiency of the electric storage system within an integrated energy hub encompassing heating, water, power, and gas sources. A key objective of this model is to maximize the performance of the electrical storage system. The study employs the epsilon-constraint method to construct the Pareto front, aiming to balance profitability and emission reduction from virtual power plant units. The final decision-making process utilizes a fuzzy decision-making technique. Additionally, demand response program (DRP) is incorporated to optimize peak-hour demand and align the load profile with defined objectives. The proposed approach is evaluated across various operational scenarios within a sample system, demonstrating its effectiveness and potential benefits in terms of cost reduction and environmental impact mitigation. Focusing on environmental impact, the carbon dioxide (CO2) emissions are also lower under the DRP scenario, amounting to 10253.92 kg without DRP versus 10127.74 kg with DRP. This reduction in emissions aligns with sustainable energy management goals, showcasing DRP's capability to mitigate environmental impacts associated with energy generation and consumption. The percentage improvements in total costs and emissions further highlight the advantages of employing DRP. The reductions in total costs range from approximately 1.2%–1.4%, demonstrating cost savings across different cost categories. Similarly, the decrease in CO2 emissions by approximately 1.2% underscores DRP's role in promoting environmentally friendly energy practices.

Integrating blockchain and strategic alliance in renewable energy supply chain toward sustainability: A comparative decision framework under uncertainty

Renewable energy development is important in the current era due to the attention paid to environmental issues and energy demand growth. On the other hand, the growth and expansion of the use of RE requires cooperation and participation in the Renewable Energy Supply Chain (RESC). The strategic alliance between the parties involved in the field of RE can play an important role in reducing risk and significant growth in RESC. Also, due to various capabilities such as transparency, traceability, decentralization, security, and immutability of information, blockchain plays an important role in improving trust and strengthening cooperation in strategic alliances between partners in RESC. Therefore, this study proposes a decision-making framework under uncertainty for integrating blockchain and strategic alliance in RESC. The proposed framework involves the CRiteria Importance Through Intercriteria Correlation (CRITIC) and Fuzzy Combined Compromise Solution (F-CoCoSo) techniques. Also, validation and sensitivity analysis are performed using several fuzzy decision-making techniques. The findings indicated that matching energy supply and demand through information sharing on the blockchain platform and cooperating with companies in the RESC was recognized as the most important solution.

IDEAL LOCATION SELECTION FOR CONTACTLESS PARCEL PICK-UP POINTS

The post-Covid era has witnessed the adoption of various new habits in our daily lives, particularly in relation to the ubiquitous e-commerce platforms that have become essential for urban populations. The surge in e-commerce activities and the intensified volume in delivery of packages during the pandemic sparked innovative ideas. This study explores one such creative concept: parcel pick-up points. We conducted a pioneering research endeavor to determine the optimal locations for these pick-up points in Istanbul, Turkey. Our methodology employed a novel hybrid approach, combining the Spherical Fuzzy Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The evaluation of criteria importance was facilitated by a literature review and experienced high-level managers in the cargo industry, who determined the criteria weights using the Spherical Fuzzy AHP method. Subsequently, the TOPSIS method was employed to identify the most ideal locations, leading to the selection of Kadikoy, Umraniye, and Atasehir. This study provides valuable insights into the selection of the optimal locations for parcel pick-up points in Istanbul, Turkey, which can inform policymakers, e-commerce companies, and logistics stakeholders. The proposed hybrid approach demonstrates the integration of modern smart technologies with fuzzy decision-making techniques, offering a robust framework for decision support in the field of e-commerce logistics. Future research can further explore the implementation and effectiveness of these pick-up points to enhance the efficiency and convenience of last-mile deliveries in urban areas.

A multi-agent digital twin–enabled decision support system for sustainable and resilient supplier management

Industry 4.0 is reshaping the manufacturing landscape, bringing about revolutionary changes in both product manufacturing and collaborative interactions among value chain constituents. Within the manufacturing value chain process, sustainable and resilient supplier management is recognized as a strategic endeavor that drives value creation, such as enhanced product quality, risk mitigation, and improved organizational reputation. Sustainable and resilient supplier management processes necessitate the integration of multiple criteria, efficient supplier coordination, and adaptability to evolving challenges. Nonetheless, conventional supplier management practices primarily rely on expert judgment, focus on the present situation, and lack a robust decision validation mechanism, leading to a limited capability to constitute proactive managerial decision-making under uncertainty. This paper introduces a digitally enabled decision support system for facilitating an intelligent and customized supplier management process in dynamic and complex environments. The system leverages stratified fuzzy decision-making techniques to account for the potential impacts of future events. Additionally, it integrates multi-agent digital twin technology to provide a reliable validation mechanism for assessing the effectiveness of supplier development strategies. The effectiveness of the proposed system was demonstrated through its successful implementation within a multinational solar water pump manufacturing company, highlighting its promising potential for practical applications.

Integrating data mining and fuzzy decision-making techniques for analyzing the key minimizing factors of carbon emissions

The purpose of this study is to identify the key factors to minimize carbon emission problem. Within this framework, an examination has been made by considering both data mining and fuzzy decision-making techniques. In the analysis process, N-gram methodology is implemented to the abstracts of 1711 studies in the “Sciencedirect” platform and five different indicators are selected. In the proposed decision-making model, firstly, selected criteria are weighted by Spherical fuzzy CRITIC. Secondly, E7 economies are ranked with RATGOS. Thirdly, a sensitivity analysis is performed, and a comparative evaluation is conducted by MAIRCA technique. The most important originality of this proposed model is generating a new technique named RATGOS. In the literature, there are various decision-making models to rank the alternatives. However, lots of researchers criticized these approaches due to some reasons, such as using Euclidean distance by calculating the distances to the negative ideal solutions. Thus, it is seen that there is a need for a new technique that considers geometric mean in proportional concepts. To reach this objective, the RATGOS technique is introduced so that it can be possible to reach more accurate results. The findings indicate that renewable energy usage is the most critical item to overcome carbon emission problem. Therefore, some measures should be taken to increase renewable energy investments. First, governments can offer incentives for renewable energy investments. These incentives may include various incentives such as tax exemptions and low interest loans. Moreover, more research and development works are required for the development of renewable energy technologies. In this way, it can make renewable energy technologies more effective and efficient. For future research directions, an evaluation can be carried out for developed countries because carbon emissions problem also plays a crucial role for the social and economic improvements of these economies.

A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology.

The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (n = 21/30). The second category, recurrent neural network (RNN), accounts for 10% (n = 3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (n = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (n = 1/30). The literature's findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.

Comparative Analysis of Pythagorean MCDM Methods for the Risk Assessment of Childhood Cancer

According to the World Health Organization (WHO), cancer is the leading cause of death for children in low and middle-income countries. Around 400,000 kids get diagnosed with this illness each year, and their survival rate depends on the country in which they live. In this article, we present a Pythagorean fuzzy model that may help doctors identify the most likely type of cancer in children at an early stage by taking into account the symptoms of different types of cancer. The Pythagorean fuzzy decision-making techniques that we utilize are Pythagorean Fuzzy TOPSIS, Pythagorean Fuzzy Entropy (PF-Entropy), and Pythagorean Fuzzy Power Weighted Geometric (PFPWG). Our model is fed with nineteen symptoms and it diagnoses the risk of eight types of cancers in children. We develop an algorithm for each method and calculate its complexity. Additionally, we consider an example to make a clear understanding of our model. We also compare the final results of various tests that prove the authenticity of this study.

Solution of constrained mixed‐integer multi‐objective optimal power flow problem considering the hybrid multi‐objective evolutionary algorithm

An Optimal power flow (OPF) is non-linear and constrained multi-objective problem. OPF problems are expensive and evolutionary algorithms (EAs) are computationally complex to obtain uniformly distributed and global Pareto front (PF). Therefore, here, hybrid two-phase algorithm integrated with parameter less constraint technique is applied to solve OPF problem. Proposed technique combines single and multi-objective EAs to find better convergence and evenly distributed PF. For the validation and effectiveness of proposed algorithm, various conflicting objective functions are formulated and implemented on IEEE 30 and 300-bus network. Each case is independently run twenty times. Hyper volume indicator technique is employed to find the best PF, and the best-compromised solution is obtained by using fuzzy decision-making technique. Recently, maximum integration of wind and solar power is highly encouraged. Complexity of OPF is increased with the integration of uncertain renewable energy resources. Hence, 30-bus test system is modified by replacing some conventional generators with the wind and solar generation. Uncertainties in wind, solar and load demand are modelled by appropriate probability distribution functions. Simulation results show that the proposed method can find the near global PF of highly complex problems subject to satisfying all the operational constraints.

Performance of Various Voltage Stability Indices in a Stochastic Multiobjective Optimal Power Flow Using Mayfly Algorithm

The performance of voltage stability indices in the multiobjective optimal power flow of modern power systems is presented in this work. Six indices: the Voltage Collapse Proximity Index (VCPI), Line Voltage Stability Index (LVSI), Line Stability Index (Lmn), Fast Voltage Stability Index (FVSI), Line Stability Factor (LQP), and Novel Line Stability Index (NLSI) were considered as case studies on a modified IEEE 30-bus consisting of thermal, wind, solar and hybrid wind-hydro generators. A multiobjective evaluation using the multiobjective mayfly algorithm (MOMA) was performed in two operational scenarios: normal and contingency conditions, using the MATLAB–MATPOWER toolbox. Fuzzy Decision-Making technique was used to determine the best compromise solutions for each Pareto front. To evaluate the computational efficiency of the case studies, a preference selection index was used. The results indicate that VCPI and NLSI yielded the best-optimized system performance in minimizing generation costs, transmission loss reduction, and simulation time for normal and contingency conditions. The best-case studies also promoted the most scheduled reactive power generation from renewable energy sources (RES). On average, the VCPI index contributed the highest penetration level from RES (13.40%), while the Lmn index had the lowest. Overall, VCPI and Lmn index provided the best and worst average performance in both operating scenarios, respectively. Also, the MOMA algorithm demonstrated superior performance against the multiobjective harris hawks algorithm (MHHO), multiobjective Jaya algorithm (MOJAYA), multiobjective particle swarm algorithm (MOPSO), and nondominated sorting genetic algorithm III (NSGA-III) algorithms. In all, the proposed approach yields the lowest system cost and loss compared to other methods.

A Decision-Making Framework for Total Ergonomic Risk Score Computation in Companies

To ensure a safe production environment for blue-collar employees, physical ergonomic risk factors (PERFs) such as noise, the vibration must be considered in every workplace. However, PERFs require both theoretical and practical information to figure out their characteristics and negative health effects on employees. This paper proposes an easy-to-follow sustainable ergonomic guide that includes the theoretical and practical information about the calculation details of PERFs, a decision-making (DM) tool to evaluate ergonomic risk factors, and a formula to calculate the total ergonomic risk score of a company. In the DM process, the Pythagorean fuzzy extensions of the Analytical Hierarchy Process were utilized to evaluate PERFs with respect to the social, technical, environmental, and economic criteria. To show the applicability of this guide, a real-world application was conducted in a company located in Ankara. According to the results, technical criteria were determined as the most critical ([Formula: see text]), followed by the social, i.e., health-related factor ([Formula: see text]). The total risk score of the company was calculated using the measurement of the PERFs and the coefficients obtained from the DM process. A comprehensive sensitivity analysis was conducted to validate the robustness of the guide. The main contribution of this paper is that a formula that can be utilized to calculate the total ergonomic risk score of any type of company was developed, based on the fuzzy DM technique and Threshold Limit Values of risk parameters.

Single- and Multiobjective Optimal Power Flow with Stochastic Wind and Solar Power Plants Using Moth Flame Optimization Algorithm

ABSTRACT The proposed article recommends a method for the solution of single and multiobjective optimal power flow without and with integrating renewable energy resources along with traditional coal-based generating stations. In the first part, the different objectives of optimal power flow problem with a single- as well as conflicting multiobjective manners are optimized. The efficiency of the recommended technique has been verified on three diverse standard test systems like IEEE-30 bus system, IEEE-57 bus system and large system like IEEE-118 bus network with the statistical analysis. The simulated results are equated to other reported meta heuristic methods. The second part consists of optimal power flow problem with the incorporation of solar and wind output energy. For forecasting solar and wind production, the proposed approach uses log-normal and Weibull probability density functions, combined. Penalties costs for undervaluation and a backup fee for oversimplification of unusual nonconventional power sources are included in the objective feature. The optimization problem is formulated using a nondominated multiobjective moth flame optimization method. To find the best compromise solution, the fuzzy decision-making technique is used. The results are confirmed using an updated IEEE-30 bus test system that includes wind and solar power plants.

Techno-economic and environmental assessment of a hybrid renewable energy system using multi-objective genetic algorithm: A case study for remote Island in Bangladesh

Renewable hybrid energy systems are well-proven to be capable of supplying reliable power in the remote areas, where grid extension is not viable due to geographical constraints, but not absolutely emissions free. The present study investigates a hybrid energy system that entails photovoltaic module, wind turbine, biogas generator, and vanadium redox flow battery for supplying stable power to a remote Island, Saint Martin, Bangladesh. Two well-known multi-objective optimisation techniques such as non-dominated sorting genetic algorithm II and infeasibility driven evolutionary algorithm are applied to size the hybrid system components based on the cost of energy ($/kWh) and life cycle emissions (kg CO2-eq/yr) under a certain reliability. In addition, a fuzzy decision-making technique is applied to find the optimal solution. A comparative analysis of using single objective function is compared with the multi-objective one. In addition, results from the non-dominated sorting genetic algorithm II optimisation technique is compared with the widely utilized software hybrid optimisation of multiple energy resources tool and the infeasibility driven evolutionary algorithm. Although the cost of energy is relatively comparable between the objective functions considered, the multi-objective approach provides better environmental benefits than the single objective optimisation system. The analyzed results also indicate that the intelligent techniques are the superior to the hybrid optimisation of multiple energy resources software tool in terms of costs and environmental point of view. Furthermore, the unit electricity cost of the proposed hybrid system configuration is comparable with the grid electricity supply at the loss of power supply probability of over 8% with significantly lower life cycle emissions.

Multi‐objective manta ray foraging algorithm for efficient operation of hybrid AC/DC power grids with emission minimisation

The current paper presents a multi-objective manta ray foraging algorithm (MO-MRFA) for efficient operation of hybrid AC and multi-terminal direct current (MTDC) power grids. The multi-objective framework aims at achieving economical, technical and environmental benefits by minimising the total production fuel costs, minimising the transmission power losses and minimising the environmental emissions in the AC/MTDC transmission systems. The MRFA imitates three separate independent foraging organisations of the manta rays. It is updated incorporating an additional Pareto archive to preserve the non-dominated solutions. A dynamic adaptation of the fitness feature is employed by iteratively varying the form of the employed fitness function. Furthermore, a fuzzy decision-making technique is activated to finally pick the appropriate operating point of the AC/MTDC power grids. The proposed technique is compared with other reported algorithms in the literatures. The applications are conducted on three test systems. These systems are IEEE 30-bus, IEEE 57-bus test power systems in addition to real part of the Egyptian grid at West Delta region. Numerical results demonstrate that the proposed MO-MRFA has great effectiveness and robustness indices over the others. Nevertheless, the proposed MO-MRFA is successfully extracting several Pareto solutions that meet the techno-economic requirements with accepted environmental concerns.

Cloud theory-based multi-objective feeder reconfiguration problem considering wind power uncertainty

Abstract Due to the spread and dispersed of load points and also growing penetration of wind farms to distribution systems, feeder reconfiguration strategy has been encountered with a high degree of uncertainty in such networks. In this way, efficient stochastic framework analysis based on cloud theory is proposed to handle the feeder reconfiguration problem considering uncertainty in load demands and wind turbine power. The cloud model, as a linguistic approach, comprises a correlation between randomness and fuzziness and provides good information to study the uncertain parameter effects on system performance through qualitative cloud models. According to qualitative-quantitative bidirectional transmission characteristic of the cloud theory through backward-forward cloud generator algorithm, a stochastic multi-objective feeder reconfiguration problem is formulated and solved utilizing powerful non-dominated sorting group search optimization algorithm. After obtaining Pareto fronts, the best compromise solution is determined by using the fuzzy decision-making technique. To demonstrate the applicability of the proposed method and to compare obtained results with the other literature, deterministic and stochastic analysis is implemented on the IEEE 33-bus and 69-bus radial distribution systems. The superiority and satisfying performance of the proposed algorithm can be inferred from the quality of simulation solutions.

What are the key determinants of maintenance performance?

Paper aims The main objective of the research is to present a combination of fuzzy decision-making techniques to measure the performance of preventive maintenance systems. Originality This research is a timely response to studying the prominent role of preventive maintenance performance in reducing cost, profitability, and overall organization’s output. Research method This study considers the application of “fuzzy DEMATEL” and ANP techniques for measuring maintenance performance and determining the causal relationships between the criteria and sub-criteria. Main findings It is conjectured that functional and technical criteria, along that with individual and the environmental are of great importance. Among the sub-criteria, employee satisfaction, growth and learning, availability of machinery and equipment, quality of maintenance by the skilled and highly-trained workforce, deem to be the most important ones. Implications for theory and practice The application of the decision techniques and the proposed measurement model for continuous improvement and promotion of maintenance performance.

Application of decision making and fuzzy sets theory to evaluate the healthcare and medical problems: A review of three decades of research with recent developments

Decision making techniques have been widely used in healthcare and medical industry. This study systematically reviews the conventional and fuzzy decision-making techniques applied in healthcare and medical problems. In total, we evaluated 202 published studies selected from 85 high-ranking journals, in which 130 of those published studies were directly related to the decision-making processes in healthcare and medical issues. Selected studies were classified into nine application areas: environmental sustainability, waste management, service quality, risk management, medical equipment and material selection, health technology, operation researches in healthcare, hospital healthcare services and other application areas. Furthermore, we classified the selected studies by numerous significant perspectives such as the authors, application areas, utilized techniques, publication time periods, decision-making and study goals, and significant results of the papers. The statistical results show that the year 2012 was ranked first in terms of developed studies within the period of 1989–2018. The techniques of AHP and hybrid approaches were the most frequently implemented decision-making technique in healthcare fields. Notably, the results also show that decision-making approaches and techniques are mostly applied to evaluate and rank different applications of service quality in healthcare and medical industries.

Profit-Based DG Planning Considering Environmental and Operational Issues: A Multiobjective Approach

In this paper, a methodology is presented to find the optimal size, location and technology of distributed generation (DG) units considering economic, technical, and environmental issues simultaneously. This technique can be implemented to both renewable and nonrenewable DGs. From economic perspective, minimizing cost and maximizing profit are investigated separately and compared with each other. In addition, from technical and environmental viewpoints, a complete set of indices are defined. In order to find the optimal solution, multiobjective particle swarm optimization, and fuzzy decision-making techniques are utilized. Furthermore, to evaluate the proposed method more accurately, two scenarios are defined. In fact, the first scenario, which is only based on cost minimization and technical objective functions, plays an introductory role for better comprehension of the second scenario. In the second scenario, which includes the presentation of the methodologies of this paper, the effect of selecting either profit maximization or cost minimization as an economic objective function along with the minimization of technical and environmental objective functions are precisely investigated. By carrying out the simulations on the IEEE 33-bus distribution test system, it is concluded that the cost-based framework is not the most profitable one, whereas the profit-based one may well lead to a more appropriate result from economic viewpoints.

An enhanced self-adaptive differential evolution based solution methodology for multiobjective optimal power flow

This paper presents an Enhanced Self-adaptive Differential Evolution with Mixed Crossover (ESDE-MC) algorithm to solve the multiobjective optimal power flow problems with conflicting objectives that reflect the minimization of total production cost, emission pollution, L-index, and active power loss. In this algorithm, a combination of eigenvector and binomial crossovers has been used to move the current population towards better search positions to provide good quality solutions. Besides, an adaptive dynamic parameter adjusting strategy is adopted to obtain the appropriate parameter settings in differential evolution algorithm during the evolution process. Further, an external archive is used to preserve all the nondominated solutions evaluated in each iteration and a fuzzy decision-making technique is applied to extract the best compromise solution from all the nondominated solutions in the archive set. Finally, in order to investigate the usefulness of the proposed algorithm, IEEE 30-bus, IEEE 57-bus and Algerian 59-bus systems with different single and multiobjective OPF problems have been solved and the simulation results are evaluated and compared with the other algorithms recently reported in the literature. The results indicate that the proposed algorithm is competent, effective and quite suitable for solving single/multi objective optimal power flow problems.

Implementing fuzzy decision making technique in analyzing the Nile Delta resilience to climate change

In terms of the importance of Egypt’s natural resources, the prominence of minimizing the risks and the significance of taking an action to adapt to climate change, a multi-criterion analysis (i.e. in ranking the effectiveness of adaptation strategy) was proposed and implemented. The strategy is fuzzy decision making technique (FDMT). Primarily, the literature was reviewed. A study area was chosen. The impact of sea level rise was investigated. The vulnerability and adaptation of the study area was examined. The vulnerability index of the Nile Delta was investigated. The vulnerability indicators of the study area were examined. The available techniques were investigated and the FDMT was chosen to be incorporated to the study area. The FDMT was studied and applied to the Nile Delta. It was found that this technique is suitable in evaluating situations that deal with ambiguity and vagueness, involve subjectivity, and imprecise information. It was thus concluded that fuzzy decision making technique enables maximum benefit from practical know-how as it takes into account several variables and it could perform “weighted merging” to influencing variables.