Weights Of Criteria Research Articles

Improving the Selection of PV Modules and Batteries for Off-Grid PV Installations Using a Decision Support System

In the context of isolated photovoltaic (PV) installations, selecting the optimal combination of modules and batteries is crucial for ensuring efficient and reliable energy supply. This paper presents a Decision Support System (DSS) designed to aid in the selection process of the development of new PV isolated installations. Two different multi-criteria decision-making (MCDM) approaches are employed and compared: AHP (Analytic Hierarchy Process) combined with TOPSIS (technique for order of preference by similarity to ideal solution) and Entropy combined with TOPSIS. AHP and Entropy are used to weight the technical and economic criteria considered, and TOPSIS ranks the alternatives. A comparative analysis of the AHP + TOPSIS and Entropy + TOPSIS methods was conducted to determine their effectiveness and applicability in real-world scenarios. The results show that AHP and Entropy produce contrasting criteria weights, yet TOPSIS converges on similar top-ranked alternatives using either set of weights, with the combination of lithium-ion batteries with the copper indium gallium selenide PV module as optimal. AHP allows for the incorporation of expert subjectivity, prioritising costs and an energy yield intuitive to PV projects. Entropy’s objectivity elevates criteria with limited data variability, potentially misrepresenting their true significance. Despite these discrepancies, this study highlights the practical implications of using structured decision support methodologies in optimising renewable energy systems. Even though the proposed methodology is applied to a PV isolated system, it can effectively support decision making for optimising other stand-alone or grid-connected installations, contributing to the advancement of sustainable energy solutions.

MULTI-CRITERIA DECISION- MAKING APPROACH IN SINGLE FACILITY LOCATION SELECTION: A PROPOSAL FOR AN INTEGRATED MODEL

Selecting the right single-plant facility is crucial to maximizing the use of limited resources and minimizing effort waste. This study aims to provide decision-makers with a practical and effective approach that is as far as possible from individual subjective judgments when deciding on a single-plant facility. It differs from some other studies in the literature that leave the prioritization and weighting of criteria in the background in that it proposes a model in which decision-makers can determine their criteria for the location selection of a single facility and determine the extent to which these criteria are present in their alternatives. The step-wise weight assessment ratio analysis (SWARA) and the weighted aggregated sum product assessment (WASPAS) methods were used with an integrated approach. The results revealed that the approach used in this study was highly successful in the selection of a community pharmacy facility as a single-plant organization.

Last Word in Last-Mile Logistics: A Novel Hybrid Multi-Criteria Decision-Making Model for Ranking Industry 4.0 Technologies

The complexity, increasing flow number and volumes, and challenges of last-mile logistics (LML) motivate or compel companies, authorities, and the entire community to think about ways to increase efficiency, reliability, and profits, reduce costs, reduce negative environmental impacts, etc. These objectives can be met by applying Industry 4.0 (I4.0) technologies, but the key question is which one. To solve this task, this paper used an innovative method that combines the fuzzy analytic network process (fuzzy ANP) and the fuzzy axial-distance-based aggregated measurement (fuzzy ADAM) method. The first was used for determining criteria weights and the second for selecting the best variant. The best solution is e/m-marketplaces, followed by cloud-computing-supported management and control systems and blockchain. These results indicate that widely adopted and implemented technologies are suitable for last-mile logistics. Newer technologies already producing significant results have serious potential for further development in this area. The main novelties and contributions of this paper are the definition of a new methodology based on multi-criteria decision-making (MCDM) methods, as well as its application for ranking I4.0 technologies for LML.

Strategy For Improving Taxpayer Compliance Motorized Vehicles: (Case Study Of SAMSAT Grobogan Regency)

The increasing volume of vehicles should have great potential for motor vehicle tax revenue in Grobogan Regency. SAMSAT has set motor vehicle tax targets in accordance with regional potential, but the realization is still not optimal. This investigate points to decide and analyze the viability and development rate of engine vehicle assess income from the One-Stop Single Organization Framework (SAMSAT) of Grobogan Rule in 2018-2023, analyze the deterrents confronted in expanding engine vehicle citizen compliance in Grobogan Rule, analyze approach procedures for progressing engine vehicle citizen compliance in Grobogan Rule which can be taken by the territorial government to extend engine vehicle citizen compliance. The secondary data came from the Grobogan Regency Statistics Center (BPS) and Samsat report data. Primary data is a way of collecting data using survey research methods so that data is obtained from respondents using direct interview techniques and questionnaires, quantitative methods with a descriptive approach to hierarchical process analysis. The research results show that the performance in achieving PKB targets in Grobogan Regency experienced fluctuations during 2018-2023. Meanwhile, the growth rate of target achievement experienced significant volatility during 2018-2023. In 2023, the growth rate will experience a significant decline and show performance that is not successful in achieving targets. From the policy priority results obtained, it is hoped that policymakers will focus more on the highest weighting criteria, namely "Policy Providing various payment facilities such as online payments or collaborating with retail outlets" compared to other policies, but this does not mean that other policies are unimportant, because every The policy taken by the government will still have an impact, both positive and negative.

Passenger Service Quality Perceptions to Star Alliance Airlines

Through performance measurement, airline operators can conduct situation assessments and evaluate the contributions of decisions to performance beforehand. Evaluating performance based on the perceived service quality by airline passengers has become crucial for airlines to understand and improve their services in order to achieve success in competition by meeting passengers' desires and expectations. This study aims to examine the perception of customer service quality among airline operators in the Star Alliance, the largest alliance worldwide based on 2023 data, using multi-criteria decision-making (MCDM) methods known as Entropy and MARCOS, with Tripadvisor data as a basis. Firstly, criteria weights were obtained using the Entropy method, and then airline operators were evaluated and ranked based on service quality using the MARCOS method. The study revealed that the most significant evaluation criterion was the in-flight entertainment systems (Wi-Fi, TV, movies), and Singapore Airlines exhibited the best service quality performance.

Renewable energy resource management using an integrated robust decision making model under entropy and similarity measures of fuzzy hypersoft set

The demand for renewable energy has significantly increased over the last decade with increased attention to the preservation of the environment and sustainable, optimal resource management. As traditional sources of energy production are depleting at an alarming rate and causing long-lasting environmental damage, it is essential to explore green and cost-effective methodologies for meeting energy demand. With each country having different geographical, political, social, and natural factors, the problem arises of which renewable energy should be utilized for optimal resource management. This multi-criteria decision making (MCDM) challenge is tackled by applying a dynamic fuzzy hypersoft set-based Method for the evaluation of currently deployed Renewable Energy systems and providing a decision support system for the installation of new ones based on the factors mentioned above for Turkey. As the installation of new renewable energy projects and the evaluation of old ones is significantly influenced by human judgment, it leaves great room for uncertainty primarily because of the psychological factors of the expert. The novel concept of Fuzzy Hypersoft Sets (FHSs) and their Entropy (EN) and TOPSIS-based operations are first discussed with reference to the problem at hand. The presented structure is superior to the ones in the literature by allowing access to data parameters as sub-parametric values while utilizing the versatility of Fuzzy structures to deal with uncertainty. The technique has great potential to serve as a potential decision support system in any setting. For now, hypothetical expert ratings are used to illustrate the working of the dynamic structure along with a sensitivity analysis to investigate the primary criterion weights in sorting. The evaluation of currently deployed renewable energy systems using our methodology revealed an average improvement in system performance compared to traditional methods. Furthermore, the decision support system for the installation of new projects based on geographical, political, social, and natural factors exhibited a potential increase in overall system efficiency. These numeric outcomes highlight the effectiveness and practical applicability of our approach in optimizing resource management and fostering sustainable energy practices.

A New Proposal for the Measurement of Criterion Weights in the Scope of Multi-Criteria Decision Making: Somer’s D-DEMATEL based Hybrid Approach (SDBHA)

A New Proposal for the Measurement of Criterion Weights in the Scope of Multi-Criteria Decision Making: Somer’s D-DEMATEL based Hybrid Approach (SDBHA)

Mapping of Flood Prone Areas in West Lombok Based on Analytic Hierarchy Process and Geographic Information System

The development and management of flood-prone information systems is needed by many parties, especially the public. In this research, the Analytic Hierarchy Process (AHP) presents an approach to detecting flood-prone areas by combining it with a Geographic Information System (GIS). AHP analysis is used to determine the weight and value of each assessment criterion, and then the results are integrated into the GIS system to produce a flood forecast map. The results obtained show that mapping of flood-prone zones can be carried out rationally and consistently based on AHP by considering four criteria, namely land use, soil type, slope, and rainfall. The results of the analysis of the zoning map show that the areas with a high level of flood vulnerability are on the north and west sides. The area around the Lembar port has a high level of vulnerability. Eyat Mayang Village also has a high level of vulnerability. Locations with a high level of flood vulnerability, caused by flooding through rivers located not far from the sea estuary.

Comparing, slurry infiltration characteristics between calcareous and silica sands based on slurry infiltration column tests and CT scanning

Calcareous sand, also known as bioclastic deposits, has distinct composition and morphological characteristics compared to siliceous sand. The exploration of the South China Sea has revealed a high potential for slurry pressure balanced (SPB) shield tunnelling in coastal areas that pass through calcareous sand strata. However, the impact of the differences between calcareous sand and siliceous sand on filter cake formation remains unclear. This study aimed to investigate the slurry infiltration characteristics of calcareous sand and siliceous sand (with Fujian sand as a representative) with the same permeability coefficient through micro slurry infiltration column tests. The macroscopic differences in slurry infiltration between the two media were observed. The sand columns were scanned before and after slurry infiltration using a CT scanner, allowing for the reconstruction of both the original particles’ topology and the pore network of the sand column. A pore network model was established, and the Dijkstra algorithm was used to identify the dominant particle infiltration paths, with flow resistance serving as the weight criterion for evaluating pore connectivity performance. The study found that the differences in particle topology and connectivity of the pore network were the fundamental mechanisms underlying the macroscopic slurry infiltration behaviors between different types of sands. These differences also explained the influence of particle size on the slurry infiltration characteristics for the same type of sand. The findings shed light on the fundamental mechanisms driving the differences in slurry infiltration behaviors and provide insights into the influence of particle size on slurry infiltration characteristics within the same type of sand.

Effect of normalization techniques and criteria weight calculation methodologies on nontraditional machining process rank for m-polar fuzzy set ELECTRE-I algorithm

Effect of normalization techniques and criteria weight calculation methodologies on nontraditional machining process rank for m-polar fuzzy set ELECTRE-I algorithm

Synthesis and multi-objective optimization of fly ash-slag geopolymer sorbents for heavy metal removal using a hybrid Taguchi-TOPSIS approach

This study develops and evaluates a highly effective geopolymer sorbent specifically designed for the removal of heavy metals from wastewater. Single and binary blends of fly ash and slag were utilized as the binding material. The geopolymer composite was activated using either sodium hy solely or in combination with sodium silicate. The Taguchi method was employed to design the geopolymer mixes, having four factors, each with four levels of variation. These factors included the fly ash-to-slag ratio (FA), binder content (BC), the molarity of the sodium hydroxide solution (M), and the sodium silicate-to-sodium hydroxide ratio (SS/SH). The performance of the geopolymer sorbent was rigorously assessed against a comprehensive set of criteria, including metal sorption capacity, setting time, flowability, density, compressive strength, abrasion resistance, water absorption, permeable voids, sorptivity, carbon footprint, and cost. The TOPSIS methodology was applied to aggregate the response criteria and determine the optimal mix for superior performance. The results showed that the optimum mix consisted of an FA of 33 %, BC of 1050 kg/m3, M of 10, and SS/SH of 3. A sensitivity analysis confirmed that changes in the weighting of the performance criteria did not significantly impact the proportioning of the optimum mix. Furthermore, the analysis of variance (ANOVA) revealed that the FA contributed the most to the sorbent’s performance, followed by the SS/SH, BC, and M. The development of this geopolymer sorbent not only addresses a critical environmental concern but also offers great potential for practical applications in industries and municipalities worldwide.

Supplier selection for global service providers: a decision support system

In this paper, we develop a decision support system (DSS) aimed at solving a real-world supplier selection problem (SSP) for a global service provider (GSP) operating in the facility management (FM) industry. The GSP provides its customers with FM services, which are subcontracted to external suppliers selected on the basis of multiple criteria, like economic soundness, quality of service, capacity, and closeness. The SSP is formulated as a multi-objective generalized assignment problem, where the quality and the closeness of the selected suppliers are maximized, whereas a penalty produced by overcapacity assignments is minimized. The quality of each supplier is computed by applying a weighted sum method, resulting from a multi-criteria decision analysis in which the criteria weights are determined through an Analytic Hierarchy Process. The DSS is developed using a modular architecture with a relational database, a supplier evaluator, and a simulator, as well as an additional user-friendly interface. The simulator relies on a rolling horizon algorithm and three alternative configurations to assign contracts to suppliers. The effectiveness of the DSS is assessed by means of extensive computational experiments on historical data. The results show a significant average improvement of 25% compared to the solution adopted by the company.

Embracing Sustainability in Green Supplier Evaluation: A Novel Integrated Multi-Criteria Decision-Making Framework

Nowadays, industrial production is a critical issue for companies as public consciousness is rising because of environmental impacts. Like most other industries, the ready-made garment (RMG) industry faces immense pressure to be more competitive in this increasingly competitive market and proactively reduce waste and ecological footprint impacts. Besides, suppliers’ environmental performance and image have far more effect on a corporation’s environmental sustainability than the corporation’s internal environmental efforts. Moreover, there is a shortage of environmental consciousness in emerging economies; thus, green supply chain practices are lagging. Therefore, the green supplier evaluation criteria should be identified and focused on during sustainable procurement. This study analyses quantitative and qualitative factors and explores the relationship between green supplier preference principles using the combination of the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method and the Fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS). Initially, the supplier’s green performance was measured by identifying the criteria from relevant literature. After that, the Fuzzy DEMATEL technique measures the related criteria weights, and the Fuzzy TOPSIS method utilizes these criteria weights to rank suppliers among alternative suppliers. The findings will guide policymakers to improve supplier quality, increase profitability through improved brand recognition, and attract consumers who prioritize environmentally friendly products.

Enhancing Contractor Selection through Fuzzy TOPSIS and Fuzzy SAW Techniques

Contractors play an integral role in construction projects, and their qualifications directly impact various aspects of a project’s success. The unbiased selection of contractors is a challenge in the construction industry worldwide, particularly in public projects where impartiality in the final selection is essential. Numerous factors must be considered when evaluating contractors, making the selection process challenging for the human brain. This paper introduces and compares two methods for assessing contractor prequalification by applying the fuzzy theory. The idea is to facilitate using human judgments in a mathematical system for decision-making with regard to selecting contractors. The method is based on identifying a fuzzy weight for the selection criteria using the Buckley method. Fuzzy TOPSIS and Fuzzy SAW methods are then used for the qualification ranking of the contractors. The proposed models are assessed using a case study. A sensitivity analysis was also conducted to compare the two models. The introduced method is expected to improve the quality of the qualification-based selection of contractors and prevent possible losses from hiring unsuitable contractors.

Multi-criteria decision-making for the selection of best airport ground access mode with a new fuzzy rough-entropy based method

In order to cope with the inherent complexities and uncertainties of settings such as transportation problems, the adoption of proper decision procedures becomes a pivotal issue. Particularly, multi-criteria group decision-making (MCGDM) uses multiple evaluations of the options based on several criteria to select the optimal choice and/or rank the alternatives. In this paper we are interested in the Entropy-REGIME method for MCGDM and its ability to handle uncertainty in the presence of fuzzy rough numbers (that allow the practitioner to represent and process imprecise data). To this end we propose a hybrid framework of the fuzzy rough-entropy based REGIME method that involves two main components: fuzzy rough-entropy for the evaluation of the objective weights of the decision criteria, and fuzzy rough-REGIME to rank the alternatives based on the weighted criteria. Its motivation is a case study that concerns the selection of the best transport mode to access Istanbul newly constructed airport. We consider four alternatives for ground access options, namely, underground metro, bus rapid transit, light rail transit, and premium bus services. Our findings show that an underground metro system is the optimal mode, followed by light rail transit, bus rapid transit, and premium bus services. Additionally, to demonstrate the superiority and effectiveness of the proposed technique, comparisons are made with existing methods.

Evaluation and selection of E-learning websites using intuitionistic fuzzy confidence level based Dombi aggregation operators with unknown weight information

In recent years, the surge in online learning has eclipsed traditional classroom teaching, elevating the prominence of e-learning platforms. However, the quest for a reliable methodology to evaluate these platforms has remained pressing. While several studies have explored this domain, none have integrated decision experts’ confidence levels. This study bridges this gap by seamlessly incorporating confidence levels into Dombi aggregation operators within an intuitionistic fuzzy framework. The study introduces two innovative operators: the confidence-level intuitionistic fuzzy Dombi weighted averaging operator (CIFDWA) and the confidence-level intuitionistic fuzzy Dombi weighted geometric operator (CIFDWG). These operators are employed to form a novel multi-attribute group decision-making (MAGDM) approach, which is demonstrated by applying them to select the optimal e-learning website in a real-world scenario. The “Stepwise Weight Assessment Ratio Analysis (SWARA)” method is utilized to determine criteria weights. The findings highlight the critical importance of sub-criteria such as topic diversity, pricing, content credibility, data security, and user interface simplicity. The analysis reveals Unacademy as the leading platform, closely followed by Byju’s. Through meticulous comparative analysis and sensitivity evaluations, we underscore the stability and coherence of the proposed model. The study’s novelty lies in its innovative approach to integrating confidence levels in multi-attribute group decision-making scenarios, promising broader applications across various real-world contexts. Educational advisers and management can leverage this methodology to empower students with informed choices.

Selection of Sustainable Material for the Construction of Drone Aerodynamic Wing using Neutrosophic RAWEC

In light of the expansion of the use of drones in many fields, the development of the drone industry has gained the attention of many researchers. The process of selecting the materials used in developing and constructing the wing of the drone has become a very important and complex matter due to its reliance on multiple and sometimes conflicting criteria. Therefore, it must have an informed approach to the selection and decision-making process thoughtfully. The goal of this research is to introduce a new approach in a neutrosophic environment for the first time, to select the most suitable material for constructing a drone wing. The material must meet several criteria, including low weight, high rigidity, resistance to deformation, durability, and excellent stability. Neutrosophic is one of the most effective tools for dealing with ambiguity since it addresses the problem of uncertainty in the decision-making process. We have presented the SVTNSs - LMAW (single value triangular neutrosophic sets-logarithm methodology of additive weights) approach for calculating the weights of criteria and the new SVTNSs - RAWEC (single value triangular neutrosophic sets-ranking alternatives with weights of criterion) approach for ranking available alternatives involves straightforward computation steps, eliminating the need for pairwise comparisons in contrast to other MCDM approaches and saving time. Also, we examined the impact of changing the normalization method on the rank of the alternatives. We have conducted a sensitivity analysis to ensure the strength and stability of our proposed approach, and the results have proven that the proposed approach for selecting the appropriate material in light of ambiguity and lack of certainty that occurs in real life is very effective. By using the SVTNSs-LMAW-RAWEC approach the companies and factories will choose the most effective and efficient materials to develop and create a drone wing.

Prioritizing evaluation criteria of IoT-driven warehousing startups: a silver lining to the unorganized sector in food supply chain

PurposeThis research is designed to meet two research objectives: firstly, to weigh up the criteria of Internet of Things (IoT) adoption in warehousing startups; secondly, to rank warehousing startups on the basis of benefits they derive from IoT adoption catering to an unorganized sector in the food supply chain.Design/methodology/approachA blend of analytic hierarchy process (AHP) and complex proportional assessment (COPRAS) methods of multi-criteria decision-making techniques were applied. AHP determined the weights of various criteria using pairwise comparison, and COPRAS technique ranked the 10 warehousing startups on account of performance indicators. The study has been conducted at the warehousing startups of Bangalore, a hub of food warehousing startups.FindingsThe critical findings of the study revealed that these food warehouse startups attain improved productivity in terms of enhancing efficiency when implemented with IoT adoption. When evaluated using both AHP and COPRAS techniques, the combined results show WH5 as the best performing and WH10 as the least performing warehouse startups.Practical implicationsWarehouses that are embarking on their business opportunity in food storage can strategize to leverage the benefits of IoT in terms of food safety and security, capacity planning, layout design, space utilization and resilience.Originality/valueDespite the numerous research works on food supply chain, the research on IoT in warehousing startups is limited. The rankings for the 10 food warehousing startups integrated with IoT using AHP-COPRAS approaches are the novelty of this work.

Key Portfolio Selection Criteria for Sustainable Construction

Selecting the best projects and programs is of paramount importance to the success of organizations in the construction industry for the employer, clients, and developers. The existing selection criteria in the literature are tailored toward traditional construction projects. However, with the current move toward sustainable construction, there is a need to incorporate sustainability-specific criteria in the process portfolio selection. This study aims to identify and evaluate the sustainability-specific project selection criteria for construction organizations from the client’s perspective; this research topic is significant because developers/clients do not always consider sustainability criteria when selecting their portfolio of projects. The research methodology of this study consists of a literature review, identifying the sustainability criteria into an integrated list, and a survey to weight and rank the criteria. Sixteen criteria were identified through an extensive review of the related literature. These criteria were categorized based on three sustainability pillars: environmental, social, and economic. The environmental pillar includes six selection criteria, which are energy use, material use, water use, land use, pollution, and waste management. The social pillar consists of five selection criteria, which include health and safety, employee training and education, improvement in infrastructure, relation with local communities, and alternative transportation. The economic pillar consists of five selection criteria, which include life cycle cost, contribution to GDP, employment creation, innovation and technology, and use of national suppliers. A survey was developed and circulated to specialists in the construction industry in the United Arab Emirates (UAE). The weights for the sustainability selection criteria were assessed by using the Analytic Hierarchy Process (AHP) method. The results show that the environmental group is the most important group among the pillars of sustainability, with a weight of 0.520, compared with the social and the economic pillars, which had weights of 0.214 and 0.266, respectively.

Quality Improvement Project Selection Using Fuzzy AHP and TOPSIS to Support Lean Six Sigma at PT ABC

This research aims to improve project quality at PT ABC by integrating the Fuzzy Analytical Hierarchy Process (F-AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) within the Lean Six Sigma framework. This method assesses and selects quality improvement projects by considering uncertainty and complexity in decision-making. The Fuzzy AHP approach is used to flexibly assign criteria weights, while TOPSIS is used to rank projects based on performance scores relative to the ideal solution. This research aims to select the most suitable Lean Six Sigma project using the MCDM fuzzy AHP and TOPSIS approaches at PT ABC. There are three alternative quality improvement projects and three assessment criteria. The data processing results using Fuzzy AHP and TOPSIS showed that the most influential criterion was the impact criterion, with a value of 0.665. Overall, the order of influencing criteria is cost (0.260) and time (0.075). Based on a review of all criteria, the project to increase the drive plate production line (A) capacity was ranked first with a value of 0.772. Second place is the project to increase capacity and consolidate rear axle assemblies A and B with a value of 0.5529. Finally, the third is the process innovation and integrated manufacturing project to strengthen the business with a value of 0.4053.