Decision-making Techniques Research Articles

Optimizing hybrid energy systems for remote communities in Asia’s least developed countries

In least-developed countries (LDCs), electricity shortages are the primary barrier to economic and social growth. Some remote areas in LDC rely on diesel-based systems. However, renewable energy must be taken into account for generating electricity because of the uncertainty of diesel fuel prices and the emissions of carbon dioxide. Hybrid energy systems (HES) are becoming increasingly popular, which is unsurprising given the rapid advancement of renewable energy technologies, which have made them the preferred method to respond to the current unreliable electricity supply, reduce the impact of global warming that occurs from electricity production, and contribute to cost reduction. This study explores the feasibility of utilizing a combination of solar PV, wind energy, and battery systems with the existing diesel generator in four different locations in Cambodia, Laos, Myanmar, and Bangladesh. Hybrid optimization multiples for electric renewables (HOMER) is used as a tool for techno-economic analysis and finding the possible combination of solar PV, wind, diesel, and battery. The multi-criteria decision-making (MCDM) technique was used to verify all configurations obtained from HOMER’s results. This approach considers environmental, economic, and technological factors by utilizing the AHP, TOPSIS, EDAS, and PROMETHEEE II techniques. The results show that PV/diesel with batteries is the optimum solution. This hybrid system comprises 89% PV penetration, a cost of electricity (COE) of 0.257 $/kWh, an initial capital cost (IC) of $244,277, and a net present cost (NPC) of $476,216 for a case study in Cambodia. Furthermore, this system can reduce almost 51,005 kg/year of carbon dioxide compared to a diesel-only system, while the cost of electricity is reduced.

Applying hesitant q-rung orthopair fuzzy sets to evaluate uncertainty in subsidence causes factors

Land subsidence is a widespread problem impacting communities worldwide. Understanding the causes and factors of land subsidence is crucial for identifying and prioritizing effective mitigation measures. One of the main reasons for prioritizing land subsidence causes is the potential impact on infrastructure and the environment. The main objective of this paper is to emphasize the importance of prioritizing the causes of land subsidence. By understanding and prioritizing the factors contributing to land subsidence based on their impact and urgency, the aim is to develop targeted strategies for mitigation, inform policy decisions, and prevent further exacerbation of this problems. The study comprises three phases, where experts in the field provide their opinions and propose a robust hybrid framework. This framework integrates the Failure Mode and Effect Analysis (FMEA) and Step-wise Weight Assessment Ratio Analysis (SWARA) with Hesitant q-rung orthopair fuzzy set (Hq-ROFS). The performance of the proposed technique was then compared with two other decision-making techniques for evaluating and ranking land subsidence causes. According to the results, extraction of groundwater, excessive irrigation using groundwater, and oxidation and drainage of organic soils were identified as primary drivers of subsidence.

Possibility to use decision-making methods for project management information system

Project management in today's world is an important task that requires constant decision-making to successfully complete projects. Project Management Information Systems (IMS) help with this, but their ability to use decision-making methods is often limited and the decision-making process itself is the responsibility of the project manager or other stakeholders. However, the use of decision-making techniques can significantly improve the efficiency and effectiveness of project management by considering various factors and alternatives. In the course of the study, an analysis of decision-making methods, such as the method of decision-making based on data, the method of optimization, the method of selecting scenarios, the method of prioritization, the method of balancing resources, the method of multi-criteria analysis, was carried out for the possibility of integration into the modules of the IMS to improve project management. The classification of project management information systems based on modules is also presented and described and a description of these modules is provided. As a result, it is found that the method of selecting scenarios, the method of prioritization and the method of multi-criteria analysis can be effectively used in separate, defined, modules for decision-making in the project environment. Integrating these methods into the IMS can help project managers make better decisions that meet the needs and goals of the project. This study confirms the importance of using decision-making techniques in IMS to improve project management. The integration of these methods will reduce risks, optimize costs and increase management efficiency. These findings stimulate further development of the functionality of the IMS and contribute to further research in this area.

Risk Prioritization in A Manufacturing Project with Fuzzy SWARA and Fuzzy MOORA Methods

Businesses must constantly change and develop to keep up with customer needs and changes in the market. Not being able to adapt to change or not being ready enough can bring risks. These changes and risks can affect both the process and the outputs of the process. It is essential to be prepared for the possible risks experienced in the operations, especially in project-based, production-to-order enterprises, to meet the customer's demands and to comply with the delivery date. Businesses must implement an effective risk assessment process to achieve this. This study implemented a risk prioritization for a new business project that produces energy storage systems by having to order. Changes must be made in the production process for an additional feature to be added to the product in line with the customer's request. The risks posed by the change and the short deadline were analyzed and prioritized. A new model was developed by developing the classical Decision Matrix Risk Assessment (DMRA) method, one of the risk assessment methods to evaluate the ten identified risks. The "severity" component of the classical DMRA method is divided into three sub-components (the severity of delivery time, the severity of cost, and the severity of quality) to be evaluated more specifically. Moreover, since the weights of these sub-components on the risks are different, the components are weighted with the Fuzzy SWARA method. The weights obtained from the Fuzzy SWARA method are used in the Fuzzy MOORA method, and the risks are prioritized. Thus, the effectiveness of the classical risk assessment approach has increased by detailing the "severity" component of the risk assessment components and weighting and prioritizing the components using multi-criteria decision-making (MCDM) techniques.

Cloud service prioritization using a Multi-Criteria Decision-Making technique in a cloud computing environment

Many companies are now moving their business to the cloud because the cloud provides user with unlimited resources and uninterrupted service. The aim of the paper is to rank services provided by a set of cloud-providers based on multi-criteria decision-making (MCDM) methods. The ranking of the cloud-providers set is based on the Interval neutrosophic set theory (INS). The paper proposes INS and TOPSIS techniques to help users and experts express their evaluations and rank cloud-providers based on various criteria. INS is used to deal with the imprecise knowledge, generated by the ambiguity from the experts' evaluations. To demonstrate the effectiveness of the framework, the closeness index (CI) values of CSPs were ranked in decreasing order. A numerical example for a case study for a set 6 CSP and 10 criteria is presented. The numerical results show that SoftLayer's is the best cloud-provider with a CI value equal to 0.69. The worst cloud-provider is Microsoft Azure with a CI value equal to 0.20. A sensitivity analysis was performed to check the consistency and robustness of the model.

Supplying hydrogen for green steel through renewable energy sources: A case study of Turkiye

Steel is the most common industrial material worldwide due to its numerous applications, high strength and recyclability. Steel production involves alloying iron with various amounts of carbon and other elements to produce thousands of different grades and types of steel for various purposes. However, the intensive use of carbon in steel production is one of the factors contributing to global climate change. To use of hydrogen and renewable energy sources in steel production has emerged as a significant solution to reduce carbon emissions. The aim of this study is to obtain green hydrogen for the production of green steel. The focus is on utilizing offshore wind energy as a renewable energy source to give us the energy we need to electrolyze seawater. The study uses the seas of Turkiye for case analysis. In this paper, an integrated Multi-criteria decision-making method is used to integrate hybrid energy systems and their application for green industry and to select a location for a power generation plant. The study is a new contribution to the literature in terms of its scope. Green steel's location selection is addressed for the first time in the literature. The weights of the criteria are determined by employing the Best-Worst Method (BWM) as a multi-criteria decision-making technique, and the alternatives are evaluated using the Analytic Hierarchy Process (AHP). By using the BWM method, decision makers can have a clear understanding of the evaluation range for pairwise comparisons of criteria, which reduces potential biases. According to the study, the northern part of the Aegean Sea in Turkiye is the most suitable alternative for green steel production, with offshore wind energy being used for hydrogen production. The study also revealed that the most important main criterion for this hybrid system is “natural factors”, and the sub-criteria are “wind speed and power density” by far compared to other sub-criteria. The fact that the area determined for the facility is suitable for construction and the possibility that the customer of the energy produced is other industrial establishments here is the most important indicator that the methods applied give good results.

Analyzing the relationship between natural resource management, environmental protection, and agricultural economics for sustainable development in China

Sustainable development is crucial for achieving a balance between economic growth, social well-being, and environmental preservation. This study aims to evaluate the dimensions of natural resource management, environmental protection, and agricultural economics for promoting sustainable development in China. In this regard, the fuzzy Analytical Hierarchy Process (AHP) and fuzzy VIekriterijumsko KOmpromisno Rangiranje (VIKOR) approaches are used for the analysis. The hybrid approach combines the benefits of fuzzy logic and multi-criteria decision-making (MCDM) techniques, allowing for the consideration of uncertainties and imprecise data in decision-making. The study incorporates six criteria, twenty-four sub-criteria, and five strategies in a comprehensive evaluation framework. According to the results obtained using the fuzzy AHP method, it is evident that the primary criteria influencing sustainable development in China are economic viability, policy and governance, and environmental impact. Within these criteria, specific sub-criteria such as cost-effectiveness, policy alignment, air quality, and social inclusion emerge as key areas of focus. Furthermore, the results of the fuzzy VIKOR method show that investing in research and innovation, strengthening environmental regulations, and strengthening climate change adaptation are the most suited strategies to guide sustainable development efforts. To ensure sustainable development in China, policymakers need to give importance to implementing strategies stressing research and innovation, strengthened environmental regulations, and enhanced climate change adaptation.

Application of Fuzzy AHP in Priority Based Selection of Financial Indices: A Perspective for Investors

Abstract By providing important indicators, financial indices help investors make educated judgements regarding their assets, much like vital sign monitors for the financial markets. The best way for investors to keep up with the market and make strategic adjustments is to keep an eye on these indexes. Researching the most important financial indexes for making educated investing decisions is, thus, quite relevant. Finding the most essential financial indices from an investing standpoint and assigning a weight to each of those indexes are the main goals of this research. A weighted score is derived by combining four financial indices in a Multi-Criteria Decision-Making (MCDM) technique. These objectives are then pursued. Triangular Fuzzy Numbers (TFNs) and the Fuzzy Analytic Hierarchy Process (F-AHP) are used to determine the weights of criteria in this technique. Using these methods together, the research hopes to provide a thorough analysis of the role that different financial indexes have in informing investment choices. This study emphasizes the paramount importance of considering the Price Earning to Growth (PEG) ratio when making investment decisions, followed by the Debt Equity Ratio. Price to Book Value and Dividend Yield, while relevant, carry comparatively less weightage in the overall assessment. Investors are advised to use these insights as a guideline in their financial analysis and decision-making processes.

Controlling aeroplane crashing through aviation infrastructure using multi-agent technology

Aeroplane crashing is a major concern for aviation safety. This article aim is to control aeroplane crashing through aviation infrastructure by using Multi-Agent Technology (MAT). For this 6 variables of aeroplane crashing, 20 variables of aviation infrastructure and 34 variables of MAT are extracted from the literatureto develop the information framework for the study. Fuzzy Multi-Criteria Decision-Making (FMCDM) technique is utilised to analyse prioritise, ranking, and identify interrelationship among them. The human error is evolved most significant factor of aeroplane crashing followed by others. For aviation infrastructure, runway is found to be the most important variable in global ranking. Whereas, in MAT pilot assistance agent is most prioritised variable in overall ranking. The proposed framework can assist the aviation industry to understand the behaviour of aviation infrastructure and MAT variables to control aeroplane crashing. The results of this study can be utilised by aviation professionals in improving aviation safety.

Optimized TOPSIS technique for trajectory selection of self-driving vehicles on highways

The selection of an appropriate trajectory for self-driving vehicles involves the analysis of several criteria that describe the generated trajectories. This problem evolves into an optimization problem when it is desired to increase or decrease the values for a specific criterion. The contribution of this thesis is to explore the use and optimization of another technique for decision-making, such as TOPSIS, with a sufficiently robust method that allows the inclusion of multiple parameters and their proper optimization, incorporating human experience. The proposed approach showed significantly higher safety and comfort performance, with about 20% better efficiency and 80% fewer safety violations compared to other state-of-the-art methods, and in some cases outperforming in comfort by about 30.43%.

Evaluating Geothermal Power Plant Sites with Additive Ratio Assessment: Case Study of Mount Seulawah Agam, Indonesia

Indonesia, a country rich in geothermal resources, has yet to fully exploit its potential, particularly in volcanic regions like Mount Seulawah Agam. This study investigates the application of the Additive Ratio Assessment (ARAS) method for the site selection of Geothermal Power Plants (GPP) in Indonesia. The ARAS method provides a systematic approach to evaluating and prioritizing geothermal development sites by integrating multiple criteria, including geological, environmental, and socio-economic factors. The study collects data from various sources and weights criteria using the Ordinal Priority Approach (OPA), incorporating expert opinions. The findings demonstrate the effectiveness of the ARAS method in identifying optimal locations for GPP development, ensuring sustainability and feasibility. The study also tests the ARAS method in existing GPP locations in Jaboi, Sabang, Indonesia, to investigate alignment with the results and validate the approach. Furthermore, the study presents recommendations for GPP site selection. This research emphasizes the significance of multi-criteria decision-making techniques in facilitating renewable energy projects. It promotes a more systematic and informed approach to geothermal energy development in Indonesia and other geothermal-rich regions.

Development of a Performance Assessment Model for Contractors in Saudi Arabian Construction Projects

Saudi Arabia leads the Gulf Cooperation Council countries in the construction industry, with 63% of the region’s future projects and $1.4 trillion in building and transportation projects. Previous studies have shown that 60% of construction projects are behind the schedule. One of the reasons behind this delay is the inappropriate selection of contractors for construction projects; therefore, the development of a contractor performance assessment model is needed for construction projects in Saudi Arabia. The proposed model includes the major key performance indicators (KPIs) used to measure contractors’ performance in construction projects. Afterward, a questionnaire is conducted with construction professionals to recommend the important KPIs to consider while developing the model. The analytic hierarchy process decision-making technique is applied for assessing relative priorities among the identified KPIs based on expert or decision-maker consensus. The results show that the time category is the most important in selecting the best contractor, emphasizing the significance of timely project completion.

Green supplier selection under supply risks using novel integrated fuzzy multi-criteria decision making techniques

Green supplier selection is one of the core business processes in the practice of sustainable supply chains, which enables firms to improve their performance to reduce harmful environmental impacts in all inbound activities. In this challenging and more dynamic business environment, materials supply can suffer from disruptions. This study aims to select suppliers based on environmental aspects by considering supply risks under an uncertain environment. Combined multi-criteria decision-making techniques are developed to address such complexity. Integrated fuzzy Analytical Hierarchy Process-Combined Compromise Solution (Fuzzy AHP-CoCoSo) and Fuzzy Stepwise Weight Assessment Ratio Analysis-Failure Mode and Effects Analysis (Fuzzy SWARA-FMEA) are applied to evaluate suppliers based on green criteria and supply risks. Data Envelopment Analysis (DEA) is used to aggregate the overall score for the final selection. A case study of green supplier selection in a manufacturing company is presented. Extensive analysis is performed to investigate the dependencies of supply risks based on the supplier selection criteria.

Sistem Pendukung Keputusan Pemilihan Smartphone Kelas Midrange 2023 dengan Menggunakan Metode MAUT

Smart phones (HP) have become a basic need for society in various aspects of life, from communication, entertainment, information, to work. There are various kinds of cellphones on the market, with various features and specifications offered. The price of cellphones is getting more and more expensive, so consumers' needs for using cellphones are also different. Midrange cellphones are suitable for users who want a cellphone with fairly good specifications, but at a relatively affordable price. Midrange is a category of cellphone that is between the entry-level and high-end categories. Midrange cellphones usually have better specifications than entry-level cellphones, but are not as expensive as high-end cellphones. Therefore, an appropriate midrange class cellphone selection process is needed for people to get a cellphone that suits their needs at an affordable price. In the selection process for midrange class cellphones, a decision-making technique known as SPK (Decision Support System) is required. In this research, the MAUT method is used for the midrange class HP selection process. The level of importance of each criterion is determined by the researcher himself. The final result obtained is that the A5 is the highest midrange class cellphone with a final value (Ui) of 0.865. The second ranked alternative is A13 with a final value (Ui) of 0.857 and the third ranked alternative is A17 with a final value (Ui) of 0.657.

An empirical analysis of evolutionary computing approaches for IoT security assessment

The Internet of Things (IoT) strategy enables physical objects to easily produce, receive, and exchange data. IoT devices are getting more common in our daily lives, with diverse applications ranging from consumer sector to industrial and commercial systems. The rapid expansion and widespread use of IoT devices highlight the critical significance of solid and effective cybersecurity standards across the device development life cycle. Therefore, if vulnerability is exploited directly affects the IoT device and the applications. In this paper we investigated and assessed the various real-world critical IoT attacks/vulnerabilities that have affected IoT deployed in the commercial, industrial and consumer sectors since 2010. Subsequently, we evoke the vulnerabilities or type of attack, exploitation techniques, compromised security factors, intensity of vulnerability and impacts of the expounded real-world attacks/vulnerabilities. We first categorise how each attack affects information security parameters, and then we provide a taxonomy based on the security factors that are affected. Next, we perform a risk assessment of the security parameters that are encountered, using two well-known multi-criteria decision-making (MCDM) techniques namely Fuzzy-Analytic Hierarchy Process (F-AHP) and Fuzzy-Analytic Network Process (F-ANP) to determine the severity of severely impacted information security measures.

Enhancing Natural Language Processing (NLP) through VIKOR Method: A Comprehensive Approach for Improved Computational Linguistics

Natural Language Processing (NLP) is a revolutionary discipline that resides at the crossroads of computer science and linguistics, with its primary emphasis on the interaction between computers and human language. With roots in artificial intelligence, NLP seeks to equip machines with the ability to comprehend, interpret, and respond to natural language, enabling more intuitive and meaningful communication between humans and computers. This multidisciplinary domain leverages advanced algorithms and models to tackle a range of linguistic challenges, from language translation and sentiment analysis to speech recognition. Recent breakthroughs, particularly in deep learning and neural networks, have propelled NLP to new heights, with applications spanning diverse sectors such as healthcare, finance, and education. As NLP continues to advance, its potential impact on enhancing human-machine interaction and information processing is increasingly evident, promising a future where technology seamlessly integrates with our natural modes of communication. The significance of Natural Language Processing (NLP) in research lies in its capacity to revolutionize human-computer interaction. NLP empowers machines to understand and generate human language, enabling advancements in sentiment analysis, language translation, and conversational AI. This transformative capability has profound implications across various industries, including healthcare, finance, and education. NLP research not only enhances the efficiency of information retrieval and processing but also holds the key to developing more intuitive and user-friendly technologies. As the frontier of NLP research expands, its potential to bridge the communication gap between humans and machines continues to shape the future of technology and information access. VIKOR functions as a multi-criteria decision-making technique that determines the best option by comparing it to the ideal option. The ranking procedure comprises determining distances concerning the optimal solution. VIKOR uses linear normalization to get the best possible outcomes. This technique was first presented by Opricovic in 1998 and was intended to optimize multi-attribute complex systems. Its main focus is on ranking lists that allow for flexibility in the strategy weight interval and incorporate compromise alternatives to obtain desired results. Alternative taken as Open AI GPT-4, Google BERT, Microsoft Azure Text Analytics, IBM Watson Natural Language Understanding, spaCy, NLTK (Natural Language Toolkit), Amazon Comprehend. Evaluation Parameter taken as Sentiment Analysis (F1 Score), NER Performance, Language Support, Processing Speed (Response Time) (ms), Translation Accuracy (BLEU Score), Data Privacy. The result it is seen that Amazon Comprehend is got the first rank where as is the IBM Watson Natural Language Understanding is having the lowest rank.

Optimized flare gas recovery technology with consideration of life cycle assessment, reliability analyses, and uncertainity of parameters

One of the most important sources of carbon dioxide emissions is gas flaring from oil wells. In addition to having an adverse effect on the environment, burning gases with a high energy value causes significant economic losses. The present work evaluated the best allocation for flare gas recovery technologies between eleven technologies considering life cycle assessment, reliability, and uncertainty analysis of some economic parameters in Iran's oil field. By choosing three objective functions, i.e., the carbon dioxide emission reduction, return payback period, and global warming by life cycle assessment analysis, the best combination of system was selected from eleven technologies using the genetic (NSGAII) algorithm and decision-making technique (TOPSIS) simultaneously. It was shown that the technology's reliability affects the best solution. Correspondingly, the integration of an enhanced oil recovery plant and a natural gas liquid at the capital investment of 120 M$ and a benefit of 263 M$ annually, with a repayment period of nine months, CO2 reduction of 1314,579 tons per year, and global warming effect of 699,820 tone CO2 equivalent was introduced as the best technology.

Enhancing Human Resource Management Through Advanced Decision-Making Strategies: Harnessing The Power Of Artificial Intelligence For Strategic, Data-Driven, And Judicious Choices

This research paper explores the topic of improving HRM procedures by incorporating sophisticated decision-making techniques, with an emphasis on artificial intelligence (AI). AI technologies have completely changed many facets of organizational operations, and human resource management is no exception. HR practitioners may optimize workforce management procedures and promote company performance by utilizing AI to make strategic, data-driven, and wise decisions. The various ways that AI can support HRM tasks, such as hiring and selection, performance management, training and development, and employee engagement, are examined in this paper. This paper provides an in-depth analysis of the literature and actual data to clarify the advantages, difficulties, and optimal approaches related to AI implementation in HRM. Additionally, it examines the ethical implications and potential societal impacts of deploying AI-driven decision-making systems in the HR domain. This research adds to a better understanding of how businesses can use cutting-edge tools to optimize HRM procedures and develop an agile, engaged, and empowered workforce that can flourish in the digital age by providing insights into the strategic integration of AI technologies.

Revolutionizing art education: Integrating AI and multimedia for enhanced appreciation teaching

Recent teaching trends are increasingly integrating diverse multimedia and computer-aided practices to enhance representation and understanding. Leveraging data-focused strategies, these methods are further refined with artificial intelligence and decision-making techniques. To improve data handling in multimedia-based teaching representation, this article introduces an integrated data representation model aided by regression learning (IDR-RL). The proposed model satisfies the data required for different teaching methods/ subjects based on curriculum and student requirements. The data integration from different sources is performed using checksum assessment. The checksum assessment for data provides precise assimilation and content-related data-to-visual representation. In this representation, the checksums are verified for their linearity, wherein the saturation/ threshold factors are identified, and data integration is recommended. This recommendation is performed as the decision-making for preventing paused representation in live teaching sessions. The checksums are used for content coherency and complete data representations to prevent time lags in teaching. The proposed model’s performance is verified through data handling rate, integration ratio, pause time, representation time lag, and failures.

Reducing emissions from production and distribution in three-echelon supply chains

Reducing carbon emissions is one of the key goals of contemporary supply chains and logistics. This paper aims to formulate a novel three-echelon (supplier-3PL-retailer) supply chain model comprising green technology investment, variable demand, variable lead time, a dynamic shipping strategy, and an option for vehicle selection. The demand function considers the product's freshness index and maximum lifetime, along with the availability of stocks. Besides, the delivery from the 3PL to the retailer is dynamically determined based on previous shipment information. A green vehicle is selected using a multi-criteria decision-making (MCDM) technique based on q-alternatives with n-characteristics for the transportation process and mathematically we link this selection process to traditional three echelon supply chain optimization problem. The results show that combining green investment with an appropriate dynamic shipping strategy by using a selected green vehicle reduces the supply chain's emissions and total integrated cost. Furthermore, reducing lead time without compromising other supply chain factors stimulates demand of customers and produces fewer emissions with a significant reduction in the total integrated cost. A real-case-based study shows the performance of the proposed model and method.. The study illustrates that 47.744% of the carbon emissions in Scenario 1 and 47.53% in Scenario 2 are reduced when an emission reduction strategy is implemented. In terms of total integrated costs our proposed model could save 4.85% cost in Scenario 1 while 4.96% for Scenario 2, which is significant.