Preference Ranking Organization Method For Enrichment Evaluation Research Articles

Dynamic decision making for situational awareness using drones: Requirements, identification and comparison of decision support methods

Decision makers increasingly operate in real-time information-rich environments, where limited time is available for interpreting data to inform decisions. These environments are driven by static or mobile sensing devices that can provide numerous dynamic data points. A prominent approach in this space is to utilise drones, which can be deployed to gather targeted information. However, deciding how best to deploy available drones is nontrivial, and stands to benefit from decision support aids that plan routes. Such a system must operate under time constraints created by the changing attributes of routes as the situation unfolds. This study describes a dynamic decision support system (DSS) for situational awareness with drones. The system applies Multi-Criteria Decision Making (MCDM) methods within a dynamic genetic algorithm to provide a continuously revised ranking of routes. Five desiderata for dynamic decision support are presented. It is shown how a dynamic DSS can be equipped with declarative specification of preferences (Desiderata 1), dynamic revision of recommendations (Desiderata 2), and high diversity of options (Desiderata 3). The study then compares four MCDM methods, namely the Weighted Product Model (WPM), the Analytic Hierarchy Process (AHP), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), with regards to how consistently they trade-off between criteria (Desiderata 4) and the stability of results under small changes to criteria values (Desiderata 5). To evaluate the trade-offs between criteria we analyse the smoothness of change in criteria outcomes as criteria weightings increase for each algorithm. The outcomes are calculated by automating the selection of routes in a case study that applies drones to the task of harbour management. The stability of results for the different MCDM methods are compared. Perturbations were applied to sets of routes ranked by each algorithm then each algorithm was reapplied and the magnitude of the changes in ranking was assessed. Overall, TOPSIS was found to be the algorithm which made the most consistent trade-offs between criteria, only under-performing another algorithm with respect to a single criterion. AHP and WPM were the next most consistent algorithms and PROMETHEE was the least consistent algorithm. TOPSIS was also found to be the most stable method under small changes to criteria values. AHP was the second most stable, followed by PROMETHEE and WPM respectively. The results show that TOPSIS achieves the best result for both Desiderata 4 and 5 and consequently the study finds TOPSIS to be an appropriate MCDM method for dynamic decision support.

Combined compromise for ideal solution (CoCoFISo): A multi-criteria decision-making based on the CoCoSo method algorithm

Each decision-making tool should be tested and validated in real case studies to be practical and fit to global problems. The application of multi-criteria decision-making methods (MCDM) is currently a trend to rank alternatives. In the literature, there are several multi-criteria decision-making methods according to their classification. During our experimentation on the Combined Compromise Solution (CoCoSo) method, we encountered its limits for real cases. The authors examined the applicability of the CoCoFISo method (improved version of combined compromise solution), by a real case study in a university campus and compared the obtained results to other MCDMs such as Preference Ranking Organisation Method for Enrichment Evaluations (PROMETHEE), Weighted Sum Method (WSM) and Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). Our research finding indicates that CoCoSo is an applied method that has been developed to solve complex multi variable assessment problems, while CoCoFISo can improve the shortages observed in CoCoSo and deliver stable outcomes compared to other developed tools.The findings imply that application of CoCoFISo is suggested to decision makers, experts and researchers while they are facing practical challenges and sensitive questions regarding the utilization of a reliable decision-making method. Unlike many prior studies, the current version of CoCoSo is unique, original and is presented for the first time. Its performance was approved using several strategies and examinations.

Multi-attribute decision-making analysis based on the bipolar N-soft PROMETHEE method

The multi-attribute decision-making (MADM) methods can deeply mine hidden information in data and make a more reliable decision with actual needs and human cognition. For this reason, this paper proposes the bipolar N-soft PROMETHEE (preference ranking organization method for enrichment of evaluation) method. The method fully embodies the advantages of the PROMETHEE method, which can limit the unconditional compensation between attribute values and effectively reflect the priority between attribute values. Further, by introducing an attribute threshold to filter research objects, the proposed method not only dramatically reduces the amount of computation but also considers the impact of the size of the attribute value itself on decision-making. Secondly, the paper proposes the concepts of attribute praise, attribute popularity, total praise, and total popularity for the first time, fully mining information from bipolar N-soft sets, which can effectively handle situations where attribute values have different orders of magnitude. In addition, this paper presents the decision-making process of the new method, closely integrating theoretical models with real life. Finally, this paper analyses and compares the proposed method with the existing ones, further verifying the effectiveness and flexibility of the proposed method.

Woven fabric selection and grading for ladies’ winter shawls using multicriteria decision-making approach

Purpose Quality assessment of textile products is of prime concern to intimately meet consumer demands. The dilemma faced by textile producers is to figure out the stability among quality criteria and efficiently deal with target specifications. Hence, the basic devotion is to attain the optimum value product which entirely satisfies the views and perceptions of consumers. Selection of best fabric among several alternatives in the presence of contradictory measures is a disputing problem in multicriteria decision-making. Design/methodology/approach In the current study, the analytic hierarchy process (AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE) are proficiently used to solve the problem in selection of branded woven shawls. AHP method verifies comparative weights of the criteria selection, while the ranking of fabric alternatives grounded on specific net-outranking flows is executed through PROMETHEE II method. Findings The collective AHP and PROMETHEE approaches are applied for the useful accomplishment of grading of branded shawls based on multicriteria weights, used for effective selection of fabric materials in the textile market. Practical implications In the apparel industry, fabric and garment manufacturers often rely on hit-and-trial methods, leading to significant wastage of valuable resources and time, in achieving the desirable fabric qualities. The implementation of the findings can assist apparel manufacturers in streamlining their fabric selection processes based on multiple criteria. By adopting this method, industry players can make informed decisions, ensuring a balance between quality standards and consumer expectations, thereby enhancing both product value and market competitiveness. Originality/value The methods of Visual PROMETHEE and AHP are assimilated to offer a complete method for the selection and grading of fabrics with reference to multiple selection criteria.

Enhancing Polybutylene Terephthalate (PBT) Composites: A PROMETHEE-Based Comparative Study of Reinforcement Materials

Polybutylene terephthalate (PBT) composites hold significant potential across various industrial applications due to their desirable mechanical, thermal, and electrical properties. In this study, we utilized the PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) method to evaluate six alternative reinforcement materials for PBT composites: Glass Fibers (GF), Carbon Fibers (CF), Natural Fibers (NF), Carbon Nanotubes (CN), Nano-clay Particles (NP), and Aramid Fibers (AF). The evaluation parameters considered were Tensile Strength (MPa), Flexural Strength (MPa), Thermal Conductivity (W/mK), Electrical Conductivity (S/m), and Cost ($). Through comprehensive analysis, Carbon Nanotubes emerged as the top-ranked reinforcement material, exhibiting exceptional performance across all evaluation parameters. With high Tensile Strength, Flexural Strength, and Thermal Conductivity, combined with significant Electrical Conductivity, Carbon Nanotubes demonstrated their suitability for demanding applications. Additionally, while the Cost factor was comparatively higher, its superior performance justifies the investment. Conversely, Natural Fibers received the lowest rank among the alternatives. Despite potential advantages in cost-effectiveness and environmental sustainability, Natural Fibers exhibited inferior mechanical and thermal properties compared to other materials. Their low Tensile Strength, Flexural Strength, and negligible Electrical Conductivity highlight limitations in performance for many industrial applications. This study provides valuable insights for engineers and material scientists in selecting suitable reinforcement materials for PBT composites based on specific performance criteria. The PROMETHEE method offers a systematic approach to decision-making, facilitating informed choices in material selection for diverse applications. Future research could explore optimization strategies and further investigate the properties and potential applications of emerging reinforcement materials for PBT composites.

Evaluation of new scintillation crystals with MCDM methods for brain PET

Brain Positron Emission Tomography (PET) imaging has been increasingly important in recent decades for improved detection and staging of brain cancer and other brain illnesses. Many elements, including the choice of radiation detecting medium, contribute to the overall image quality used to characterize a PET system's performance. In our previous study, novel transparent optical scintillator crystals for brain PET system were simulated. Therefore, there is a need to evaluate these materials using advanced computational techniques. Hence, in this study, we evaluated and compared them using two Multi-Criteria Decision-Making (MCDM) techniques, namely Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and Visekriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The crystals used in this study were strontium hafnate (SHO), gadolinium aluminum gallium garget (GAGG), gadolinium yttrium gallium aluminum garget (GYGAG), gadolinium lutetium gallium aluminum garget (GLuGAG), and lastly lutetium oxyorthosilicate (LSO) for comparison. Density, effective atomic number, energy resolution, light output, and decay time were selected as important criteria. Importance weights of each criteria were then assigned by considering the high resolution and high sensitivity detectors. With both MCDM methods, the results showed that SHO outranked the other scintillator materials followed by LSO and GLuGAG. GYGAG, and GAGG is revealed as the least favorable crystals, in agreement with the previous simulation studies. This study can be extended by including more scintillators as they become available in the future.

Optimizing Drug Delivery Vehicle with Multi-Criteria Decision Making (MCDM) - Based Excipient Selection

Excipients are used in drug delivery systems as a means of effectively delivering drugs to their target site. Multi-criteria decision-making (MCDM) methods are tools for decision-making that consider multidimensional factors. Such methods are a comparative technology used in medicine that combines individual criteria into the total assessment of selected alternatives. This study aims to enhance the solubility and bioavailability of drugs through the application of MCDM-based excipient selection. By incorporating the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), various excipients can be evaluated and ranked based on their suitability for specific applications, considering parameters related to drug solubility and bioavailability. The results highlight the potential of cyclodextrins (net flow: 0.0023) and Eudragit polymers (net flow: 0.0016) as preferred options for drug carriers, while Poloxamer 188 (P188) (net flow: -0.0030) is identified as the least preferred option. This study demonstrates the effectiveness of the PROMETHEE method in improving the performance of poorly soluble and bioavailable drugs, ultimately contributing to the development of new drug delivery systems. The findings have significant implications for therapeutic outcomes in the treatment of diseases.

An integrated multi-polar fuzzy N-soft preference ranking organization method for enrichment of evaluations of the digitization of global economy

Digitization represents the ultimate expression of globalization that has revolutionized every facet of global existence, enhancing connectivity, financial terms, trade opportunities, and public services. To further broaden or fortify this digital realm and advance global progress, various strategies for multi-criteria group decision-making (MCGDM) have been developed. This research article extends the literature study on the sensational MCGDM method known as Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE), which shares the burden of selecting the best alternative under many terms and conditions. This article aims to make a positive contribution to the development of the digital world. The main principle of the PROMETHEE technique is to calculate positive and negative flows of the selected options according to the divergence of the alternatives’ scores. It provides us with partial and full rankings of options by determining score degrees, suitable preference functions, and a multi-criteria preference index. To deal with the multi-polarity of the modern world and assign N-ordered levels, this paper proposes the multi-polar fuzzy N-soft (mFNS) PROMETHEE technique. Prior to this, an Analytic Hierarchy Process (AHP) technique is applied to guarantee the credibility of the criterion weights for each criterion. After that, the whole strategy of m-polar fuzzy N-soft PROMETHEE approach to order the selected options is explained, and all the course of action of this remarkable (MCGDM) technique are synchronized in an extensive flowchart, helping us to learn more about the technology keenly. Moreover, the utility of our discussed technology is illustrated by considering two applications of generating electricity through human movement and choosing the best digital currency. The most suitable choice is extracted with the help of the outranked directed graph. The results obtained by mFNS PROMETHEE technique benefits us with the selection of most reliable source to digitized the global economy, in addition, It enables us to rank the alternatives from most preferable to least one, that not only saves time but also produces the better outcomes. Subsequently, the eminence of mFNS PROMETHEE technology is checked by comparability with the prior art. Lastly, the advantages and disadvantages of our proposed technique are supplemented to demonstrate its productivity and shortcomings.

Finding the Nation with the Highest Rate of Deaths due to Cancer Using PROMETHEE

Cancer is a growing problem in today’s world. Cancer is considered to be the leading cause of death in many countries. The incidence rates for various cancer forms vary by nation and can depend on many factors such as the lifestyle of the people, obesity rates, exposure to certain chemicals, family history, and others. As preventing cancer is a significant public health challenge, it is important to identify which country is leading in death rates due to the disease. This study uses a Multi-criteria decision-making (MCDM) method named Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) to perform this test and rank the countries on this basis. The outcome of the decision-making process is to create awareness among people to reduce the effects caused by cancer in that particular country

INTEGRATION OF TAGUCHI AND PROMETHEE FOR CNC MILLING MACHINING PARAMETER OPTIMIZATION ON AA6061

In the manufacturing industry, machining has developed quite rapidly from the use of conventional machines to unconventional machines. Unconventional machines that are often used today are optimize computer numerically controlled (CNC), the use of CNC in the manufacturing industry provides many benefits in product quality and productivity. One of them is CNC milling, this type is one of the main machines on the production floor. Machining optimization becomes the main goal to achieve the ideal response in order to produce products with good and consistent quality and productivity. Surface quality leads to surface roughness, while productivity leads to material removal rate. This study aims to optimize CNC milling machining parameters on AA6061 with Taguchi experimental design and preference ranking organization method for enrichment evaluation (PROMETHEE) method. Machining was controlled using wet machining conditions to maintain temperature during machining. Experiments were conducted nine times with three factors and levels. These factors included spindle speed, feed rate, and depth of cut. The result of this research is the ideal value of the combination of surface roughness and material burning rate which is 0.565 (experiment 3). This best experiment is influenced by spindle speed 2600 rpm, feed rate 65 mm/min, and depth of cut 2.5 mm. Feed rate has the largest contribution in influencing the response which is 43.23%, followed by depth of cut 25.24%, and spindle speed 15.91%.

Evaluation of personal protective equipment to protect health and safety in pesticide use

IntroductionAgriculture emerges as one of the most dangerous industries in the world, considering injury and illness rates. After the service sector in Turkey, the next large-scale sector is the agricultural sector, which constitutes 20% of the general employment. The exposure of farmers to pesticides, used to increase the quality and productivity of agricultural products, causes health risks via the mouth, respiration, skin, and eyes. Pesticide use in Turkey is increasing; the annual average increase is estimated at 1.2%. Exposure to pesticides can be reduced by wearing personal protective equipment to protect against health and safety hazards.ObjectiveThis study aimed to determine the importance of personal protective equipment using the multi-criteria decision-making method to prevent the risk of injury and disease resulting from pesticide use.Materials and methodsThe Analytical Hierarchy Process (AHP) method was used to find the weights of the criteria determined by expert opinion and a literature review. The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) was used to rank personal protective equipment.ResultsPersonal protective equipment includes masks, gloves, overalls, safety shoes, glasses, and hats. The use of multi-criteria decision-making methods in health and safety in the agricultural sector will contribute to the literature.ConclusionEmphasizing the use of personal protective equipment, especially when using pesticides, will increase the rate of use of protective measures.

Multi-Criteria Ranking of Z-Source Inverter Topologies for a Three-Wheel Fuel Cell Hybrid Electric Vehicle

Replacing the regular two-stage inverters with a Z-source inverter (ZSI), based on single conversion, has come under attention in fuel cell (FC) hybrid electric vehicles (HEVs). This substitution is to decrease the circuit complexity, cost, and required space. However, fulfilling these goals highly depends on the selection of a suitable topology. This paper performs a multi-criteria ranking to deeply analyze the impact of different ZSI topologies on the performance of a recreational FC-HEV, composed of FC stack and lithium-ion capacitor bank. First, the dual-energy sources are embedded into the Z-source network (ZSN). Subsequently, the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) is utilized to rank different possible ZSI topologies based on four scenarios (standard, compactness, input current ripple, and compactness versus input current ripple). This analysis indicates that each scenario can have a suitable topology with respect to its requirements. Finally, the two highest-ranked ZSI topologies according to standard scenario are implemented by signal hardware-in-the-loop (HIL) to validate the effectiveness of the performed analysis. The results indicate that the use of the ZSI topologies lead to higher average efficiencies (1.98% and 4.07%), and smaller size and volume of passive components, compared to a conventional two-stage inverter.

Proposal of a Combined AHP-PROMETHEE Decision Support Tool for Selecting Sustainable Machining Process Based on Toolpath Strategy and Manufacturing Parameters

Sustainable manufacturing technologies are the new challenge faced by enterprises, industries, and researchers. The development of a sustainability-based assessment method considering the environmental and economic impacts is crucial to realize viable manufacturing. However, few studies have addressed environmental economics and social flows using a common perspective. Mechanical machining is one of the most-used manufacturing techniques. The overall ecological, economic, and social footprint requires accurate and effective estimation and optimization. Several studies have addressed this issue by examining the entire process of machining, but sustainability flows for machining parameters and toolpaths have remained relatively unexplored. The lack of systematic assistance tools bridging the gap between decision-maker preferences and the three sustainability pillars—economic, social, and environmental—has impeded the widespread adoption of sustainable machining practices. To this end, this paper proposes an integrated approach to the decision-making problem that combines the Analytical Hierarchy Process (AHP) with the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) for selecting a sustainable machining strategy. The sustainability criteria are driven by manufacturing process parameters commonly employed and regulated during the manufacturing phase. This includes toolpath strategies as a qualitative input factor and manufacturing parameters such as cutting speed, feed rate, depth of cut, and stepover as quantitative input factors, affirming the practical applicability of the method in industrial contexts. New fundamental methods are also presented for selecting the most efficient machining parameters and toolpaths according to the weights assigned to each ecological, social, and economic footprint by the decision-maker (the manufacturer or production manager). In this way, sustainable machining strategies in the manufacturing industry will be strengthened in integrity. In a case study of part-end milling, both manufacturing parameters and toolpath strategies are considered to establish sustainable feature-based machining decisions.

Radiopharmaceutical selection for tau PET imaging

AbstractBackgroundThe accumulation of pathologically misfolded tau is a feature that is shared by a group of neurodegenerative disorders that are collectively referred to as tauopathies. AD is the most prevalent of these tauopathies. Neurofibrillary tangles are characterized by the presence of hyperphosphorylated protein (tau), and senile plaques are characterized by the presence of amyloid peptide aggregates. PET imaging is fully quantitative and enables accurate spatial assessment. However, as a result of non‐specific binding and the limited spatial resolution of PET scanners, this technique requires not only the presence of tau inclusions in a brain region but also sufficient density and spatial extent of those inclusions in order to generate a sufficient signal‐to‐noise ratio. With the development of tau‐specific ligands for use with PET, it is now possible to investigate the role that tau pathology plays in the development and progression of these disorders. The purpose of this study is to analyze, compare, and rank the currently available tau PET tracers for imaging in research and routine clinical settings.MethodWe proposed using the fuzzy Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE), a multicriteria decision‐making (MCDM) tool. First‐generation tau PET tracers ([18F]AV1451, [11C]PBB3, [18F]THK5117, [18F]THK5351, [18F]THK5317, and [18F]THK5105) and second‐generation compounds ([18F]MK‐6240, [18F]JNJ‐067, [18F] PI‐2620, [18F]RO‐948 (previously referred to as [18F]RO69558948), [18F]PM‐PBB3, [18F]GTP1, [18F]JNJ‐64349311 ([18F]JNJ311), [11C]RO‐6931643, and [11C]RO‐6924963) were selected to investigate in this study. The evaluation is based on relative parameters, such as specificity, target binding accuracy, neocortical uptake, radiation exposure, brain penetration, free fraction in plasma, defluorination, and adverse reactions, and the importance that is placed on each of these parameters is weighted differently.ResultThe results from this study show that [18F]RO‐948 is the most favorable tau PET ligand, while[18F]THK5105 is the least favorable one based on the considered weighted criteria in this study.ConclusionFuzzy PROMETHEE can be used as a decision‐aid system to select the proper radiopharmaceuticals for tau PET imaging. This study can be extended to include more tracers and criteria and might help researchers and concerned readers to select the optimal tau PET tracer.

MCDM-AHP and PROMETHEE methods integrated for base service strategy vendor evaluation and selection

Business competition is very important in controlling product-savvy customers. Strong basic service techniques will be the main factor that binds vendors as the final destination in the supply chain through the strength of business network processes. This research aims to create a strategic basis for evaluating and selecting vendors through the integration process services of the multi-criteria decision-making method analytic hierarchy process (MCDM-AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE) methods. Empirical studies show how this approach can provide optimal decision support for the vendor evaluation and selection process. Eight different types of criteria are required in its apps and must be realized as a barometer of the strategic basis for selecting vendors so that business processes are of high quality. These criteria include quality of goods, payment methods, payment terms, minimum transactions, discounts, delivery times, inventory, and service. The optimal weight for each criterion will be determined based on its importance to the synthesis process and its feasibility tested using mathematical algebra matrices and expert choice apps. Decision-making was based on the results of ranking evaluation of selected vendors through the development of 342 preference matrices, ten vendors were deemed worthy of acceptance and nine other vendors were rejected.

Comparative evaluation of FDA‐approved drugs for managing the symptoms of AD

AbstractBackgroundAD is a neurodegenerative disorder characterized by the gradual deterioration of mental abilities over time. According to estimates, 5.8 million Americans age 65 and older currently have AD dementia, and by the year 2050, that number is projected to reach 13.8 million. There is currently no cure for AD but medications have been shown to help some people manage the disease’s symptoms. Although there are disease modifying drugs target the amyloids such as Aducanumab and Lecanemab, high costs (around $30,000/year) and severe side effects make them unfavorable for many patients suffering from AD. However, prescribing the appropriate drug considering many factors affecting the decision‐making such as cost of the drug, safety of drug, and patient’s medical history might be a relatively challenging task for a physician and time‐consuming for patients in case the first prescribed drug is not tolerated or worsens the symptoms. Therefore, there is an urgent requirement for better tools to aid in prescribing the most appropriate drug.MethodsIn this regard, we propose using fuzzy Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE), a multi‐criteria decision‐making (MCDM) method, to compare, evaluate, and rank the FDA‐approved drugs for managing AD symptoms. Drugs used in this study include Donepezil [Aricept], Rivastigmine [Exelon], Galantamine [Razadyne], Memantine [Namenda], and Memantine + Donepezil [Namzaric] with the following criteria: cost, safety of drug, tolerability, interaction with other drugs, response to cognitive impairment, symptoms management rate, and side effect. To the best of our knowledge, any decision‐making methodologies have not been proposed for use in identifying the most appropriate drugs for AD patients.ResultResults showed that [Namzaric] came first in the ranking, and followed by [Aricept], [Namenda], and [Razadyne] while [Exelon] ranked as the least favorable drug.ConclusionMCDM methods can be used as a decision‐aid system for evaluating AD drugs and this study can be extended including more criteria and new drugs as they become available.

An integrated approach to design and evaluate Chinese-style stools

To inherit and promote the excellent design characteristics of Chinese-style furniture, this study focuses on Chinese-style stools and proposes an integrated design and evaluation approach with combination of shape grammar, KANO model, and entropy-weighted VIekriterijumsko KOmpromisno Rangiranje (VIKOR) methods. Firstly, based on the initial forms of five Chinese-style stools, a shape feature library is constructed by extracting shape features using regional cultural symbols. Secondly, combining shape grammar and inference rules, innovative design alternatives are generated for Chinese-style stools, incorporating regional cultural symbol features. Thirdly, an in-depth investigation of Chinese-style furniture market is conducted, and user requirements are analyzed using KANO model questionnaire, categorizing the requirements into three attributes: appearance, technological, and economic. Based on KANO model’s classification of user requirements, a set of 14 evaluation criteria for Chinese-style stools is established. Finally, to avoid subjective factors in weighting the criteria, the entropy-weighted method is applied, and VIKOR method is utilized to obtain the optimal ranking of the design alternatives for Chinese-style stools, ultimately selecting the optimal alternative. The results show that based on VIKOR method, the optimal solution is the same with comparison to the results obtained from Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), preference ranking organization methods for enrichment evaluations (PROMETHEE) and elimination and et choice translating reality (ELECTRE) methods. In addition, to verify its ergonomic characteristics, feasibility and rationality, the optimal alternative is simulated by JACK software. By integrating shape grammar, KANO model, and the entropy-weighted VIKOR method, this study provides some insights for incorporating regional cultural symbols into the design of Chinese-style furniture and exhibits certain advantages in terms of comprehensive evaluation, user orientation, decision objectivity, and consideration of diversity.

An application of the hybrid AHP-PROMETHEE approach to evaluate the severity of the factors influencing road accidents

The evaluation of the severity of the factors influencing road accidents with a detailed severity distribution is critical to plan evidence-based road safety improvements and strategies. However, currently available studies use statistical and machine learning (ML) models to evaluate the severity of factors causing road accidents without a detailed severity distribution. Further, most of these available models require significant pre-data processing and have certain data-centric limitations. However, the multi criteria decision-making (MCDM) techniques have the potential to combine expert opinions for robust analysis without any pre-data processing calculations. Thus, this study uses a hybrid analytic hierarchy process (AHP) and the preference ranking organisation method for enrichment evaluation (PROMETHEE) approach to analyse the severity of factors and characteristics that influence road accidents within the Gujarat state, using injury types as criteria and minor road accident influencing factors as alternatives. These 82 minor factors have been further characterised into 18 characteristics and 4 major factors. Further, AHP integrated 40 expert inputs to determine criterion weights, while PROMETHEE ranked all minor variables. Then, after applying k-mean clustering, each ranked factor has been classified as very severe, moderately severe, or severe. The result clearly highlights that overspeeding, male gender, and clear weather conditions have been concluded to be the highly severe factors for Gujarat state. Thus, by providing a clear severity analysis and distribution of factors influencing road accidents, the proposed research may help government stakeholders, researchers, and politicians build severity-based road safety reforms and strategies with clarity.

A Model Proposal for Measuring Performance in Occupational Health and Safety in Forest Fires

This study endeavors to prioritize occupational health and safety (OHS) accomplishments across ten forest management directorates in a specified province of Turkey, utilizing multi-criteria decision-making techniques (MCDMT). In a rigorous evaluation across four applied methodologies, the alternative P5 (amount of risk before measures) consistently exhibited superior performance. A notable emphasis of this research lies in the employment of a sophisticated OHS performance model, developed herein, to critically assess the OHS performance within these forest management directorates. In our evaluations, static variables such as land use and land cover, slope, vegetation type, soil characteristics, and distance from highways and human settlements were considered. Similarly, dynamic variables including temperature, wind speed and direction, and humidity were also factored in. Our findings corroborate the substantial ramifications, both tangible and intangible, of workplace accidents, hence underscoring the imperativeness of extensive, inter-industrial research initiatives geared towards effective accident prevention. This research is particularly focused on fortifying accident prevention measures in the arena of occupational health and safety during forest fires—an area with substantial economic and social implications, and of escalating importance in light of the expanding prominence of clean energy sources. Specifics pertaining to the forest fire rate within the studied directorates, as well as the criteria bearing the most and least impact as determined by this study, will be explicated in the full text, accompanied by their corresponding percentage values. Through the employment of Analytical Hierarchy Process (AHP), Fuzzy Analytical Hierarchy Process (F-AHP), and the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) method, this study demonstrates the efficacy of MCDMTs in assessing and categorizing OHS performance, hence contributing valuable insights to managers, OHS experts, and future research undertakings. In conclusion, this research concentrates on the formulation of employee-centric occupational health and safety systems, with a specific emphasis on forest fires. The proposed OHS performance index model represents a tool of exceptional potential in the evaluation of performance measurement, while addressing uncertainties across diverse sectors. This underscores the urgency of addressing health and sustainability issues within economic, social, and ecological domains through the rigorous examination and implementation of effective OHS practices.

A hybrid integrated multi-criteria decision-making approach for risk assessment: a study of automotive parts industry

PurposeThis paper aims to enable the analysts of reliability and safety systems to evaluate the risk and prioritize failure modes ideally to prefer measures for reducing the risk of undesired events.Design/methodology/approachTo address the constraints considered in the conventional failure mode and effects analysis (FMEA) method for criticality assessment, the authors propose a new hybrid model combining different multi-criteria decision-making (MCDM) methods. The analytical hierarchy process (AHP) is used to construct a criticality matrix and calculate the weights of different criteria based on five criticalities: personnel, equipment, time, cost and quality. In addition, a preference ranking organization method for enrichment evaluation (PROMETHEE) method is used to improve the prioritization of the failure modes. A comparative work in which the robust data envelopment analysis (RDEA)-FMEA approach was used to evaluate the validity and effectiveness of the suggested approach and simplify the comparative analysis.FindingsThis work aims to highlight the real case study of the automotive parts industry. Using this analysis enables assessing the risk efficiently and gives an alternative ranking to that acquired by the traditional FMEA method. The obtained findings offer that combining of two multi-criteria decision approaches and integrating their outcomes allow for instilling confidence in decision-makers concerning the risk assessment and the ranking of the different failure modes.Originality/valueThis research gives encouraging outcomes concerning the risk assessment and failure modes ranking in order to reduce the frequency of occurrence and gravity of the undesired events by handling different forms of uncertainty and divergent judgments of experts.