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On a global scale, flooding is the most devastating natural hazard with an increasingly negative impact on humans. It is necessary to accurately detect flood-prone areas. This research introduces and evaluates the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) integrated with GIS in the field of flood susceptibility in comparison with two conventional multi-criteria decision analysis (MCDA) methods: analytical hierarchy process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The Spercheios river basin in Greece, which is a highly susceptible area, was selected as a case study. The application of these approaches and the completion of the study requires the creation of a geospatial database consisting of eight flood conditioning factors (elevation, slope, NDVI, TWI, geology, LULC, distance to river network, rainfall) and a flood inventory of flood (564 sites) and non-flood locations for validation. The weighting of the factors is based on the AHP method. The output values were imported into GIS and interpolated to map the flood susceptibility zones. The models were evaluated by area under the curve (AUC) and the statistical metrics of accuracy, root mean squared error (RMSE), and frequency ratio (FR). The PROMETHEE model is proven to be the most efficient with AUC = 97.21%. Statistical metrics confirm the superiority of PROMETHEE with 87.54% accuracy and 0.12 RMSE. The output maps revealed that the regions most prone to flooding are arable land in lowland areas with low gradients and quaternary formations. Very high susceptible zone covers approximately 15.00–19.50% of the total area and have the greatest FR values. The susceptibility maps need to be considered in the preparation of a flood risk management plan and utilized as a tool to mitigate the adverse impacts of floods. [ABSTRACT FROM AUTHOR]

In recent years, the concept of entrepreneurial and innovative universities has gained widespread prominence. Many universities have been paying more attention to being entrepreneurial and innovative by improving their organizational systems, advancing their infrastructure, and increasing financial support. Since numerous criteria with different weights exist, ranking universities based on entrepreneurial and innovative performance can be considered a multi-criteria decision-making (MCDM) problem. This article aims to investigate how different multi-criteria decision-making methods with different criterion weights can affect university rankings and to highlight the reasons that contribute to these differences. In this scope, Grey Relational Analysis (GRA) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) methods were used to rank and compare the universities in Türkiye according to the 2022 Entrepreneur and Innovative University Index (EIUI). In addition to the current weights of each EIUI dimension, entropy-based weights and equal weights were used in MCDM methods. Three ranking approaches with varying weights provided different rankings for universities. The effect of criterion weights was found to be more important in the ranking difference than the method used. The ranks for universities coded U1 and U2 as the most entrepreneurial and innovative universities remained the same. In addition, the performance of each university according to each dimension was evaluated graphically using the GAIA plane to enable them to identify areas for improvement in their rankings. [ABSTRACT FROM AUTHOR]

Climate change is a global phenomenon, which affects in several ways different regions all around the world, beyond the rise in global temperature. Among the different climate change issues, the management of transport infrastructures is crucial. Particularly, their vulnerability against changes in climatic conditions should be assessed. Vulnerability indicators are based on the IPCC concept of vulnerability and can be defined as a function of Climate Exposure, Sensitivity, and Adaptive Capacity. These dimensions need to be addressed during the assessment making and can be modelled as a Multiple Criteria Decision Analysis (MCDA) problem. This study proposes an integrated approach of several MCDA methods as a possible tool for ranking the climate change vulnerability of coastal roads in Malta. The application covers six coastal roads in the islands of Malta, classified by three different MCDA methods. The results indicate that the proposed approach can produce a consistent ranking of the climate change vulnerability of coastal roads. The study provides policy and decision-makers with a definition of a coastal road, an inventory of such roads, a list of climate change impacts, and a mathematical model incorporating climate change vulnerability indicators. The model can be used to prioritize investment and plan climate change adaptation strategies for infrastructural works on coastal roads. [ABSTRACT FROM AUTHOR]

This research carries out a comparative analysis of the Internal Market Orientation (IMO) to a sample of small and medium enterprise companies, which has been divided into nine groups based on their business sectors. Applying the statistical technique of structural equations, the coefficients of four IMO inputs in each business sector are obtained. Next, the PROMETHEE multicriteria technique is applied to obtain a composite indicator of the IMO implementation of each business sector. The value of the composite indicator together with the GAIA plane allow to distinguish the differences of the IMO profiles, their strengths and their weaknesses. The results show the divergences in the IMO inputs according to the sector, which is evidence that the business sector is a predictor of the IMO implementation of a company. [ABSTRACT FROM AUTHOR]

The highly dynamic nature of the real-world environment poses significant challenges for electric bus system operations (EBSOs), which are prone to serious accidents due to their complexity and a wide variety of risk factors. The accidents are often the result of ignoring the most serious risk sources because of a lack of comprehensive risk assessments. Therefore, this paper proposes an improved failure mode and effects analysis (FMEA) multicriteria group decision-making model to ensure the reliability and safety of EBSOs. First, an expert group is invited to evaluate the risk failure modes (FMs) of the EBSOs and transform them into a normal wiggly hesitant fuzzy set (NWHFS) form. Because the risk assessment process involves a large number of team members with different backgrounds, the experts are grouped based on scoring function values using the K-medoids clustering technique. Then, the evaluation values of the expert group are integrated using the normal hesitant fuzzy weighted geometric (NWHFWG) aggregation operator to obtain the final aggregation matrix, and the weights of the three criteria of occurrence (O), severity (S) and detection (D) are determined for each FM via the CCSD method. Finally, considering the cross-correlation between factors within the system, the relationships between FMs are analyzed, and their impact and importance are quantified using the gray correlation-based DEMATEL method, followed by the final ranking of the FMs using regret theory and the PROMETHEE II methodology to achieve a rational allocation of resources. The results are analyzed with sensitivity and comparative analyses to illustrate the superiority of the model. [ABSTRACT FROM AUTHOR]

Sustainable construction becomes a highly key topic in the construction sector owing to its benefits and advantages over economy, environment, and society. This study considers one such application of sustainable construction, rehabilitation of RC beams. Through such rehabilitation, it is possible to extend the life of existing concrete structures. A numerical analysis approach has been framed to study to this phenomenon. Total 10 criteria have been selected to evaluate the rehabilitation material alternatives on RC beams with ultra-high-performance concrete. 9 Alternative materials have been considered for rehabilitation of RC beams. DEMATEL-ANP and PROMETHEE has been used as a methodology in two phases, the first phase used DEMATEL-ANP to analyze the considered criteria and the second phase used PROMETHEE to evaluate the considered alternative materials based on the analyzed criteria. Finally, this study concludes with the effective rehabilitation material for RC beams with ultra-high-performance-concrete. The considered numerical framework can be adopted in various forms of similar or different applications. [ABSTRACT FROM AUTHOR]

The importance of public transportation service quality research is significantly increasing in recent years, it is the key to understanding and analyzing passengers' preferences. Different approaches are utilized to explore users' preferences however, dominantly these apply merely subjective scoring of the attributes and alternatives of the mobility. In this paper, we design a specific model for public transportation mode choice which is capable of integrating subjective scoring with scoring by objective measures such as distance or time. Owing to this purpose, we combine the outranking Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) as a method to evaluate passengers' preferences for tangible and intangible criteria with the fuzzy theory, and the Graphical Analysis for Interactive Aid (GAIA) plane to visualize the interactions between attributes as well as to test the robustness of the results via sensitivity analysis. The contribution of this paper is the constructed integrative method that is less subjective than the well-known models but also keeps the freedom of individual evaluators in expressing their preferences. Moreover, another significant issue of mode choice analysis is the group consideration, which is also refined in the new methodology by taking into account not only the mean of group preferences but also their range. A common characteristic of public surveys, the possible vague responses of the layman pattern is solved with the fuzzy approach to reduce the risk of uncertain scoring. The proposed model acts as a great base for the fuzzy inference system that can facilitate mode choice for passengers within a changing environment. The efficiency of the new methodology is demonstrated through a real-world case study of Budapest city, the obtained results are supporting underground mode service quality and highlighting its impact on citizens' behavior in favor of public transport. [ABSTRACT FROM AUTHOR]

The most common situation encountered in the operating room scheduling is the uncertainty of operation times. This situation may cause the operations to be delayed or canceled. In this study, stochastic operating room scheduling is discussed under the uncertainty of operation times. In real life, variability may vary depending on many factors from operation to operation/patient to patient. These factors are the surgeon's experience, the difficulty of the operation, the patient's weight, age, disease history, etc. In this study, separate coefficients of variability were determined for each operation, taking into account the variability factors. Operations are scheduled, taking into account the operation-specific coefficients of variation. To evaluate the variability factors, analytical network process method was used considering the interaction between them. The level of uncertainty/coefficient of variation of each operation was determined with the PROMETHEE method. Finally, the logical modeling power of constraint programming is used to solve the operating room scheduling problem. In the proposed constraint programming model, the flexibility of the goal programming method was utilized. For the modeling of uncertainties, a chance-constrained approach was used. The case study demonstrates that the proposed approach is a novel and outstanding technique, and the proposed CP model is efficient in solving the problem. As a result of the study, the uncertainty in the operation time of each patient was calculated as the variability according to the factor weights, and the tables were reconstructed according to this situation. The performance and effectiveness of the new schedules obtained under variability are shown. [ABSTRACT FROM AUTHOR]

Failure modes and effects analysis (FMEA) is a systematic framework applied to recognize and eliminate viable failures within objects, services, structures, or designs at the early stage of the production process. The existing traditional FMEA approaches come with inherent deficiencies, e.g., all risk factors having similar weights, inappropriate and crisp risk priority numbers, and neglecting the existence of uncertainty and vagueness in experts' judgment. Our purpose is to identify the drawbacks of the existing models and then improve the risk assessment of failure modes under uncertain environments. For this purpose, we present a novel FMEA technique based on rough numbers and preference ranking organization method for enrichment evaluations (PROMETHEE) in multi-criteria decision-making problems. The presented FMEA model integrates the advantages of the PROMETHEE approach and the modified entropy weight method in coping with the roughness of linguistic assessment information. To illustrate its potential applications, the proposed method is tested to examine the risks of the general anesthesia process in the medical field. In the end, a comparison with the existing techniques is given to show the efficiency and accuracy of the proposed model. [ABSTRACT FROM AUTHOR]

Due to global warming and climate change, power generation from renewable resources, such as solar, wind, fuel cells, and others, is becoming increasingly important. The smart grid's incorporation of renewable energy resources makes Peer-to-Peer (P2P) energy trading through the local energy market possible. Due to its distributed nature, Peer-to-Peer (P2P) energy trading faces many challenges regarding security and privacy in energy trading. Most existing works do not consider data privacy among participants. A P2P energy trading model with Decentralized Multi-Criteria Ranked Prosumers called DeMCRP is introduced to address these privacy and security challenges in smart grid energy trading. The proposed DeMCRP trading model uses Corda-distributed ledgers, consensus, and smart contracts for energy trading between energy buyers and sellers, also termed prosumers. In the DeMCRP model, the mapping procedure between prosumers is done based on priority values for such prosumers determined using a multi-criteria decision-making method, namely the Preference Ranking for Organization Method for Enrichment Evaluation II (PROMETHEE II). Furthermore, an energy-trading smart contract performs energy trading between buyers and sellers. The transactions' validation, verification, and uniqueness process is acheived through consensus of the distributed ledger by notaries. The Distributed Service Operator (DSO) selects 51% of sellers as notaries according to the rank of sellers. The rank of sellers is determined using a multi-criteria decision-making method called PROMETHEE II by considering various parameters such as energy surplus, trust factor, and the number of times the seller is selected as notary. Moreover, the performance analysis of the proposed scheme has been carried out in terms of network deployment time and memory utilization. The proposed DeMCRP technique works in polynomial time if the number of participants increase. Furthermore, security analysis has been carried out for various security features like data sharing privacy, users' privacy, non-repudiation, integrity, and preventing double-spending. The proposed method protects the privacy of participants' data and stops double spending, which is not taken into account by many existing methods. [ABSTRACT FROM AUTHOR]

Multi-criteria decision making is a useful tool for economic applications, especially in the last decade. We investigate and compare the performance of Italian public high schools. We consider three outputs provided by average students' school and academic performance, and school characteristics. Our sample includes 263 high schools (HS) in all Italian Regions, grouped into 6 types and 3 geographic areas. Employing specific weights for the outputs, we apply PROMETHEE to derive the ranking of different types of schools by geographic area. Then we apply ELECTRE III to the same problem and compare the results with those obtained using PROMETHEE. Our results show that: (a) there are significant differences between HS according to school and academic performance; (b) Classic and Scientific Lyceums are the best types of school regardless of geographic area; (c) by simultaneously considering type of school and geographic area, southern HS seem to have better performances than northern ones. [ABSTRACT FROM AUTHOR]

A country that relies on developing industrialization and GDP requires a lot of energy. Biomass is emerging as one of the possible renewable energy resources that may be used to generate energy. Through the proper channels, such as chemical, biochemical, and thermochemical processes, it can be turned into electricity. In the context of India, the potential sources of biomass can be broken down into agricultural waste, tanning waste, sewage, vegetable waste, food, meat waste, and liquor waste. Each form of biomass energy so extracted has advantages and downsides, so determining which one is best is crucial to reaping the most benefits. The selection of biomass conversion methods is especially significant since it requires a careful study of multiple factors, which can be aided by fuzzy multi-criteria decision-making (MCDM) models. This paper proposes the normal wiggly interval-valued hesitant fuzzy-based decision-making trial and evaluation laboratory model (DEMATEL) and the Preference Ranking Organization METHod for Enrichment of Evaluations II (PROMETHEE) for assessing the problem of determining a workable biomass production technique. The proposed framework is used to assess the production processes under consideration based on parameters such as fuel cost, technical cost, environmental safety, and C O 2 emission levels. Bioethanol has been developed as a viable industrial option due to its low carbon footprint and environmental viability. Furthermore, the superiority of the suggested model is demonstrated by comparing the results to other current methodologies. According to comparative study, the suggested framework might be developed to handle complex scenarios with many variables. [ABSTRACT FROM AUTHOR]

The probabilistic linguistic term set (PLTS) shows great superiority in expressing decision-makers' opinions. The multi-attribute decision-making (MADM) problem under a PLTS environment has gained attention from numerous scholars. However, the majority of current studies are not precise enough in capturing information on PLTS. To address this problem, this paper presents a preference ranking organization method for enrichment of evaluations (PROMETHEE) based on the redefined PLTS and novel score function to solve MADM problems under a PLTS environment. First, an asymmetric normalized PLTS based on prospect theory (ANPLTSPT) is developed. Compared with the PLTS, ANPLTSPT offers a more realistic portrayal of decision-makers' psychological state while ensuring the superiority of the PLTS. Second, regarding the structural complexity of ANPLTSPT, this paper attempts to simplify the computational process through a score function that can embody the characteristics of ANPLTSPT. Inspired by previously formulated score functions, a novel score function called Score-InInHe is developed, the corresponding definitions are given, and some further properties are discussed. With the support of the proposed Score-InInHe, the total score entropy is defined and an objective method to determine the attribute weights is proposed. Finally, the proposed approach is applied to the selection of a green supplier and the determination of air quality. The validity and realistic applicability of the proposed approach are demonstrated through comparative analyses and discussions. [ABSTRACT FROM AUTHOR]

Variable refrigerant flow (VRF) air conditioning systems have become highly preferred in the air conditioning sector has enabled many new companies to enter the industries of VRF Air Conditioning Systems manufacturer and applicant. It has become difficult for decision-makers to select the best applicant company among the alternatives. In this article, the applicant company selection model is developed for heat-pump VRF air condition systems to meet the heating and cooling needs of buildings. The operation and structure of the VRF systems and their selection criteria are determined for the applicant company. Using the Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) method, one of the multi-criteria decision-making methods, an application company selection model has been presented for three different buildings according to the building characteristics and climate conditions. [ABSTRACT FROM AUTHOR]

Sea level rise is one of the serious aftermaths of global warming on the hydrosphere. The scientific community often depends on global climate models (GCMs) for projection of future sea levels. Numerous GCMs are available; thus, selecting the most appropriate GCM/GCMs is a critical task to be performed prior to downscaling. In this study, multi-criteria decision-making (MCDM) techniques, namely, Preference Ranking Organisation Method of Enrichment Evaluation (PROMETHEE-II), Elimination Et Choice Translating Reality (ELECTRE-II), and compromise programming, were used to identify appropriate GCMs whose projections can be used to downscale sea level projections at Ernakulam, Kerala, India. Support vector machine was employed to statistically downscale the sea level projections from the projections of GCMs. Five statistical metrics, namely, correlation coefficient (r ), normalized root mean square error, absolute normalized average bias, mean absolute relative error, and skill score, were adopted in this study as the performance criteria. The weightage of each criterion was computed using the entropy method. Six GCMs (GISS-E2-H, CanESM2, ACCESS1-0, CNRM-CM5, GFDL-CM3, and CMCC-CM) were considered for the analysis based on the availability of predictors. GISS-E2-H, CanESM2, and ACCESS1-0 occupied the first three positions respectively in all three MCDM techniques. [ABSTRACT FROM AUTHOR]

The well-planned investment in a robust Information System (IS) is essential for the sustainability of a firm's competitive advantage. The careful selection of a suitable adoption plan for the IS investment is vital, especially in the early preparedness stage of a system development life cycle (SDLC), as this has a long-lasting impact on the SDLC. The selection process involves a complex, multiple criteria decision making process. The adoption of a multiple criteria decision tool, the Primitive Cognitive Network Process (PCNP), an alternative of the Analytic Hierarchy Process (AHP), can be challenging due to the minor differences among objects which are not appropriately evaluated by multiplication or ratio. This commonly results in rating judgement that occurs during the selection of alternatives. To address the challenges with IS planning, this paper proposes the use of the PCNP in various decision models. Three established studies of IS projects using the AHP are revisited using the proposed PCNP to demonstrate the feasibility and usability of the PCNP. The paper discusses data conversion from the AHP to the PCNP, its merits, and limitations. The proposed method can be a applied as an alternative decision tool for IS planning for various projects including Artificial Intelligence adoption projects, cloud sourcing planning projects, and mobile deployment projects. [ABSTRACT FROM AUTHOR]

This case study proposes a novel methodology for hospital management to plan and implement short to medium-term improvement initiatives by integrating data-driven decision-making with Multi-Criteria Decision-Making/Analysis (MCDM/A). Historical data on 165 patients operated upon in eye surgery department was first analysed (using Tableau software) to provide overall insights supported by process mining (using Celonis software) to identify the process bottlenecks that require immediate attention. The bottlenecks led to the identification of issues and their potential solutions. These potential solutions were taken as alternatives and run through Visual PROMETHEE software that incorporates the PROMETHEE II method, an MCDM/A method. By adopting a visual approach, the hospital management could arrive at a quick consensus regarding the actual situation and bottleneck, potential solutions to issues identified and their comparative ranking in an interactive environment. While insights from data analysis bring a consensus on the issues requiring a resolution, the solutions to these issue(s) can be compared and ranked by utilising PROMETHEE II. Hence, this paper proposes a unique methodology that facilitates both short-term and medium-term decision-making by utilising visual means for understanding current reality and developing/exploring potential solutions to identified issues. [ABSTRACT FROM AUTHOR]

PROMETHEE method is a very popular quantitative method of decision-making with many benefits. However, the evaluation of alternatives in the original PROMETHEE method is derived only from differences in values, i.e., regardless the performance values themselves. In some situations, ignoring these values can distort the final results. This paper brings several examples of such situations, for which the original PROMETHEE fails and does not bring reliable results. Ishizaka and Resce (Soft Comput 22:7325–7338, 2018) have recently introduced the modification of PROMETHEE which considers the performance values, but also changed substantially the logic of the ranking algorithm. The aim of this paper is to modify the original PROMETHEE method to make it possible to include the performance values, without losing any main benefit of the original method and with keeping the original logic of the algorithm based on pair-wise comparisons. Two particular preference functions' types are proposed for the proposed extension (Gaussian function and strictly concave function), whose choice depends on the performance of the worst-performing alternative under consideration. In addition, the new algorithm is provided also in the fuzzy environment, which is useful if the decision-maker is not able to set the input parameters of the preference function precisely. Both the deterministic and fuzzy extensions are demonstrated using numerical examples. The results show that the final ranking can be strongly influenced by the level of performance. Moreover, the fuzzy extension brings richer information through the natural interpretation provided by possibility and necessity measures if the parameters of the preference functions are imprecise. [ABSTRACT FROM AUTHOR]

With the evolution of web computing, Cloud computing and 6G, the data transmission speed has become one of the most imperative subject to be addressed especially for smart cities. The OBS (optical burst switching) networks are emerging as a fast and robust infrastructure for seamless connectivity and data transmission over internet in smart cities. The network traffic is generally heterogeneous in nature and OBS networks are responsible for transmission of network traffic to the intended nodes with fast speed. An appropriate channel allocation to the network traffic is a major challenge for OBS networks due to enormous traffic on optical medium. In order to minimize the consumption of bandwidth, to minimize the latency and to enhance the network throughput, we are proposing PROMETHEE-II based multi criteria channel selection approach in this paper. We have devised client server based approach where OBS central controller controls the core optical switches and channelize the network traffic on suitable channels. The criteria for selecting the appropriate channel is based on network bandwidth, hop count, channel waiting time, channel response time, traffic load on channel, expected traffic, wavelength, and link capacity. We have used AHP method to attain relative importance of each criterion, then applied PROMETHEE method to make pairwise comparison of all the alternative channels that are capable to transmit the data and finally, the channels are ranked to determine the most suitable channel for forwarding the network traffic. The results obtained from the proposed research work helps to enhance the network throughput, to reduce the latency and to enhance the traffic transmission rate. [ABSTRACT FROM AUTHOR]

The success of any company depends significantly on the realization of new products, which in turn depends on the selection of the best initial concept. Concept evaluations and selections during product development are critical decision-making steps and involve multiple-criteria decision-making (MCDM). This work proposes a concept evaluation and selection model which considers not only customer requirements (CR) but also the quality of the product features, defined in terms of feature quality levels (FQLs) and manufacturer's ability (MA) to fabricate the product, i.e. product manufacturability. The work evaluates quantitative weights assigned to CR (i.e. w c ) and FQL with the help of the analytical hierarchy process (AHP) so that rather than random weights considered by the designers, the system takes into account the structured weights. The work also considers MA during concept selection in the form of weights ( w m ). The work proposes an extended PROMETHEE technique, where the weights of CR ( w c) and MA ( w m) are applied concurrently at the conceptual design stage itself into the PROMETHEE to select the most appropriate concept that leads to product development by including the criteria from customers as well as manufacturers' perspectives. [ABSTRACT FROM AUTHOR]

Energy is one of the most important components in the socio-economic development processes of countries. The share of the manufacturing industry in total energy production in the world is 37 and 30% of this is used in steam production in industrial steam boilers. Therefore, industrial steam boilers should be one of the industrial processes considered first in the manufacturing industry to increase the energy efficiency and cleaner production implementations. In addition, selecting the most suitable one among the many practices is very effective in increasing the savings and benefits. At this point, energy efficiency practices (EEPs) that can be applied in industrial steam boilers need to be evaluated by using an analytical decision-making model. In this study, EEPs commonly applied in industrial steam boilers were investigated. These EEPs were evaluated by using PROMETHEE decision model, one of the multi-criteria decision-making models (MCDM) according to total eight criteria such as potential energy saving opportunities, initial investment and operating costs, applicability, technology accessibility, economic savings potential, environmental benefit, cross-media effects, and payback period. As a result, some EEPs such as reuse of condensate, maintenance and repair of steam traps, insulation of steam boilers, and distribution systems were found as priority EEPs. Surprisingly, it was concluded that heat recovery from waste flue gases, economizer installation, and heat recovery from blow down have lower priority. The results showed that prevention-oriented practices were higher priority than pollution control and recovery practices. [ABSTRACT FROM AUTHOR]

Assessing alternative solutions that have uncertain evaluations in conflicting multiple criteria is not straightforward. Probabilistic models such as Bayesian networks (BNs) can effectively model and represent the uncertainty in such problems, but they do not include built-in mechanisms to guide different decision makers (DMs) with varying preferences toward the final decision. We propose a systematic approach to combine outranking methods with BNs to provide decision support for solutions with multiple and conflicting criteria under uncertainty. The proposed approach is based on Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), and it offers different levels of precision and flexibility to the DMs in assessing the solutions. Our approach includes graphical tools and summary metrics to enhance the presentation of its results to the DMs. We test our approach with a case study on supplier selection where the uncertainty in supplier performances is modeled with a BN. We demonstrate that the proposed approach can enable the joint use of multiple criteria techniques with probabilistic modeling techniques like BNs to provide decision support in complex environments including uncertainty. [ABSTRACT FROM AUTHOR]

Recovery and recycling end-of-life tires (ELT) incur a significant cost of collection and transportation. Improperly conducted ELT collection contributes to excessive vehicle exhaust emissions and high transportation costs. This study investigates the collection and transportation costs of ELT including preliminary processing of waste tires like cutting, baling and packing tires to reduce the volume of each shipment. Waste collection vehicles exhaust emissions were examined in the collection scenarios. In this study a novel approach of multi-criteria decision support – coupled analytical hierarchy process (AHP) and Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) methods have been applied for selecting the best scenario, considering costs and environmental impacts in the collection of ELT. The results show the most profitable method of the ELT collection by routing of heavy truck and pickup of waste tires from a local network after preprocessing. Packing the tires in the collection points reduces the total cost between 20-30% and vehicles' exhaust emissions up to 40 %, compared to other methods in the Polish case study. The proposed decision support method is useful for evaluating environmental and cost factors, especially in regions where transport costs due to distances constitute the largest share of costs. This method and the results are useful for the ELT collection companies in promoting sustainable methods of transportation of waste including emission and economic factors. The study encourages using the compacting of ELT at the collection points for more efficient transportation. [ABSTRACT FROM AUTHOR]

Pythagorean fuzzy sets, a generalization of intuitionistic fuzzy sets, can effectively handle uncertain, incomplete and inconsistent information involved in real-life multi-criteria decision making (MCDM) problems. Preference ranking organization method for enrichment of evaluation (PROMETHEE) is one of the popular methods for solving MCDM problem. In this paper, we extend the PROMETHEE method with Pythagorean fuzzy sets. We illustrate the proposed model with a numerical example and compare the method with some existing Pythagorean fuzzy sets-based methods. We also solve a medical diagnosis problem using the proposed Pythagorean fuzzy PROMETHEE method and highlight some advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Efficiency evaluation is a desirable kind of a decision making analysis for experts in various fields because if something can be measured, it can also be improved more easily. Measuring efficiency has been a topic of many research studies, and many quantitative methods to deal with this problem under various assumptions have already been established. However, most methods struggle with barriers limiting their use in practice. The aim of this paper is to establish a method for efficiency evaluation which is as traceable as possible, provides a graphical representation of the results, and yields results that are easily interpretable for problems with uncertain input data expressed by fuzzy evaluations. In particular, a new hybrid method for efficiency evaluation is presented. This method is based on a combination of the PROMETHEE (i.e. outranking multi-criteria decision making method) and data envelopment analysis (DEA) under uncertainty, similar to the study published by Ishizaka et al. (Soft Comput 22(22):7325-7338, 2018) who, however, worked only with deterministic data. The PROMETHEE method allows a computationally easy and traceable evaluation of alternatives. The DEA method is currently the most popular method for efficiency evaluation. However, its original version provides a graphical representation only for very simple models. In addition, the results are usually not easy to interpret because it mixes scale-dependent and scale-independent data together. Fuzziness in the DEA model is handled using measures of possibility and necessity, which provide easily interpretable results for a decision maker. In particular, the results reveal to what extent each unit under evaluation can possibly be, or certainly is, efficient. The proposed algorithm is applied to one artificial and one real-life numerical example, and the results are compared with the pure DEA model. [ABSTRACT FROM AUTHOR]

The growing number of web services (WSs) and cloud services, which can meet the users' functional and non-functional requirements, have inspired researchers to provide more effective approaches for ranking the available services, regarding different QoS factors and selecting the best of them. In this context, several service selection frameworks using the fuzzy multicriteria decision making (MCDM) techniques are introduced in the literature. This paper focuses on such schemes, and firstly provides the required background knowledge about service selection and MCDM methods. Then, it puts forward a taxonomy of the service selection schemes, regarding their utilized fuzzy MCDM methods, and describes how the fuzzy MCDM methods are adapted to handle the fuzziness of the users' preferences and QoS properties. Furthermore, the main features of these schemes are compared, and their contributions and possible shortcomings are discussed. Finally, the concluding remarks are provided, and directions for future studies are illuminated. [ABSTRACT FROM AUTHOR]

To determine the critical component in an industry is one of the most important tasks performed by maintenance personnel to choose the best maintenance policy. Therefore, the purpose of the current paper is to develop a methodology based on integrated cloud model and extended preference ranking organization method for enrichment evaluation (PROMETHEE) method for finding the most critical component of the framework by ranking the failure causes of the system from multiple decision maker perspective. For this purpose, ranking of failure causes is performed by taking into account five factors namely chances of occurrence of failure (F0), non-detection probability (Nd), downtime duration (Dd), spare part criticality (Spc) and safety risk (Sr). In this paper, first the primary and secondary weight of decision makers are calculated based on the uncertainty degree and divergence degree, respectively, to determine overall weight using cloud model theory by converting the uncertain linguistic evaluation matrix into interval cloud matrix, and then ranking of the steam handling subunit of paper making unit in a paper mill using extended PROMETHEE. The effectiveness of the proposed methodology is explained by considering steam handling subunit of paper making unit to find the critical component. [ABSTRACT FROM AUTHOR]

This paper develops a multi-objective conflict resolution simulation-optimization model based on a leader-follower game to resolve conflicts between different water users while optimizing water quality in the river through selective depth water withdrawal from the reservoir. Iran Water Resources Management Company (IWRMC), given the nature of the power distribution in this region, is selected as leader, and agricultural, domestic, and industrial water users are selected as followers. Nash-Harsanyi bargaining theory is used as a nested model in this general framework to model competition between followers. The proposed selective withdrawal approach considers four reservoir outlets, located at 120, 145, 163, and 181 m above sea level. Water withdrawal from multiple outlets addresses reservoir thermal stratification and water quality. Temperature and water quality are simulated based on different possible scenarios of reservoir inflow and release using a calibrated CE-QUAL-W2 model. Five artificial neural network (ANN) surrogate/meta models are then trained and validated based on CE-QUAL-W2 model results for each water quality variable. Subsequently, these validated surrogate models are coupled with the NSGA-II optimization model, which along with the utility functions of different stakeholders, constitute the building blocks of our conflict resolution multi-objective optimization model. Finally, three decision-making methods, namely AHP, PROMETHEE, and TOPSIS, are utilized to choose the superior compromise solution. Our results show that water withdrawal from multiple reservoir outlets ensures optimal water allocation to different stakeholders while satisfying the desired water quality criteria. In this study, the top outlet (181 m) has desirable quality, and the IRWQISC water quality criterion at the top and deepest outlets are highest and lowest, respectively. [ABSTRACT FROM AUTHOR]

Besides accuracy, diversity of recommendation list is also important for users. Hence, the optimization of the recommendation system can be abstracted as a multi-objective problem because accuracy and diversity are contradictory goals. Available multi-objective optimization based recommendation schemes return the Pareto set for the target users. However, the scale of Pareto solutions is uncontrollable. If a Pareto set contains too many solutions, it will not be quite useful for users to make the final decision. In this paper, multi-objective immune algorithm is used to improve recommendation accuracy and diversity, then we can get the pareto set. Further, we introduce PROMETHEE into the recommendation system to get a more precise evaluation of Pareto solutions. By combining PROMETHEE with Pareto, we redefine recommendation as Top-n PROMETHEE Pareto optimization problem and a multi-objective immune optimization and decision-making algorithm is presented. The experimental results show that the proposed algorithm, compared with other existing algorithms, can generate more diverse and accurate recommendation list and provide more precise decision-making for the user. [ABSTRACT FROM AUTHOR]

Natural disasters, such as flood, earthquake or hurricane, can cause environmental damage, infrastructural destruction, economic disruption and result in the loss of human lives. The goal of this paper is to assess vulnerability of the interdependent sectors and to determine the impact of a disaster. As part of our investigation, we explore the inoperability input–output model (IIM) to obtain ballpark estimate of losses. IIM can quantify the effect on production level and measure ripple effect of perturbation on the interdependent system by calculating the economic loss and inoperability level. Furthermore, PROMETHEE (preference ranking organization method for enrichment evaluations) is integrated with IIM, incorporating the criteria of economic system linkages, economic losses and percentage of the inoperability. It will help to analyze the effect of disaster and rank the most vulnerable sectors—based on the defined criterion. A case study is performed in Pakistan (an Asian developing country) which was hit by floods in 2011–2012, perturbing the demand in various sectors. To analyze the perturbation level and system linkages, an interdependent input–output matrix—based on the Pakistan's economy—is constructed. Further research analysis gives perspicacity in terms of describing criticality and sensitivity of the independent economic system. It is concluded that the agriculture and service sectors have suffered with the highest inoperability level. The results also identify significant sectors of the economy after flood strikes; these sectors can be prioritized for policymaking activities to reduce the sector-specific impact in the aftermath of a catastrophic event. [ABSTRACT FROM AUTHOR]

This study addresses to rank the environmental performance of manufacturing sectors using Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method which is a widely used multi-criteria decision making method for environmental management. The study focuses on 24 manufacturing sectors and 5 criteria—water usage, environmental expenditure, environmental employment, waste recover and waste density—to rank the environmental performance of the manufacturing sectors. Rankings of the manufacturing sectors are made using PROMETHEE II, which is a developed version of PROMETHEE, with three scenarios (base scenario, environmental scenario, economic scenario) obtained by different weightings of the criteria. In the analysis stage, manufacturing sectors are ranked and then environmental performance of the sectors is compared in terms of four technology levels (low technology, medium–low technology, medium–high technology and high technology) proposed by OECD. According to the results, manufacture of tobacco products is the best performing sector and manufacture of chemicals and chemical products is the worst performing sector in each scenario. Furthermore, high-technology and medium–high-technology sectors have better environmental performance than medium–low-technology and low-technology sectors in each scenario. After that, Geometrical Analysis for Interactive Aid planes were used to understand the weak and strong points of the manufacturing sectors. Better performing sectors are good at water usage, waste recover and waste density criteria, but bad at environmental expenditure and environmental employment criteria. This study closes the gap as the first study on the environmental performance of Turkish manufacturing sectors using a multi-criteria decision making method, and we hope that this study will be beneficial for the researchers in this area. [ABSTRACT FROM AUTHOR]

Due to the increasing complexity and uncertainty in green supplier selection, there would be some hesitations for decision makers (DMs) to provide evaluation information of suppliers. Hesitant fuzzy set is a suitable tool to model such hesitations. This paper develops a hesitant fuzzy Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) for multi-criteria group decision-making and applies to green supplier selection. First, a new hesitancy index of hesitant fuzzy element (HFE) is defined. Then, a generalized hesitant fuzzy Hausdorff distance is proposed considering the individual deviation of membership values and the hesitancy index simultaneously. A combined hesitant fuzzy entropy is presented integrating the defined fuzziness entropy and hesitancy entropy of HFEs. Subsequently, a linear programming model is established to derive DMs' weights objectively. To determine the criteria weights for each DM, a nonlinear programming model is built through minimizing the relative entropy. The PROMETHEE is employed to obtain individual ranking of alternatives for each DM. To obtain the collective ranking of alternatives, a multi-objective assignment model is constructed and transformed into a single-objective assignment model for resolution. Thereby, a hesitant fuzzy PROMETHEE method is presented. A green supplier selection example is demonstrated to validate the proposed method. [ABSTRACT FROM AUTHOR]

Nowadays, in the coal mining industry, achieve to goals such as high production rates and efficiency with increasing safety and reducing production costs needs to using mechanized mining methods and advanced equipment. As case studies, Takht coal mine and Tazareh coal mine in north-eastern of Iran and Tabas coal mine in mid-Eastern Iran were selected. In total, 17 coal seams with the potential for mechanization from three mentioned mines were studied. In this present study in order to evaluating these coal mines potential of mechanization, important and influential factors such as seam inclination, seam thickness, roof strength, seam uniformity condition, seam extension, bearing capacity and water at the working face were investigated. In this study, it was tried to select the best seams between studied seams with potential for mechanization based on the mentioned factors, by using Preference Ranking Organization Method for Enrich Evaluation method. Finally, Tabas and K11 seam from Takht mine were selected as the top options for mechanization. [ABSTRACT FROM AUTHOR]

A well-organized sustainable risk management in an organization often generates environmental and economic advantages. Addressing “sustainability and risk” simultaneously, an organization is more capable of enduring challenges that produce environmental and operational stability in management. In an industrial organization, these primary areas of concern involve social responsibility and a focus on occupants’ health and well-being; both areas address environmental and climate change, with an end result of increasing competitiveness and profitability. The key challenge lies in exploring sustainable risks associated with the industry so that they are addressed strategically. This research work is one such attempt to find sustainable risks in the manufacturing sector. This research is the outcome of a case study conducted in three leading surgical cotton manufacturing companies in the southern part of India. A hybrid multi criteria decision making based fuzzy decision making trial and evaluation laboratory and analytic network process with preference ranking organization method for enrichment evaluations (FDANP with PROMETHEE) methodologies is used to derive the results. The final outcome of this paper presents the identified critical sustainable risks from the case study, and also serves as a model for risk managers in manufacturing sectors. By identifying sustainable risks at an early stage, a company may avert the occurrence of undesirable incidents while, at the same time, may enhance their production capacity. [ABSTRACT FROM AUTHOR]

Multiple-criteria decision-making is common in our daily life. The probabilistic linguistic term set is an effective tool to represent both simple and cognitive complex linguistic expressions given by individuals and groups completely. In this paper, the PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluations) is enhanced by integrating with a conflict analysis to solve general multiple-criteria decision-making problems with both quantitative and qualitative criteria. Firstly, to capture the inherent uncertainty of evaluations, interval numbers are used to expresses the values of quantitative criteria while probability linguistic term sets are used to scale the qualitative criteria. Then, a preference function for both quantitative and qualitative criteria is proposed. In addition, a conflict analysis is presented and added to the PROMETHEE, which can derive the preference, indifference, and incomparability (PIR) relations of alternatives. A reference point is given to select the thresholds for the PIR relations. Finally, the improved PROMETHEE is highlighted by a case study concerning site selection of the wind power plant. [ABSTRACT FROM AUTHOR]

In spite of proliferation of maintenance work-force research, the maintenance community has deficient insight into the technicians' selection process. Building on studies focusing on technical crew selection and uncertainties, this paper offers a framework to potentially remedy this lack. First, the establishment of a fuzzy-rooted structure and connection of it with step-wise weight assessment ratio analysis (SWARA) and complex proportional assessment of alternatives (COPRAS) can enlarge the research community's comprehension of the competent selection process and could be initiated and completed by selection teams for maintenance technicians. Second, the put forward structure confronts the widespread description of where to obtain technicians, directing attention from the steps-to-selection to mere sources of obtaining technicians. Thus, this paper establishes the manner, in a particular context that the integration of fuzzy-SWARA and COPRAS could add to the research community's perception of tackling uncertainties all around the maintenance technicians, while building on ideas from the human resource management literature as well as the technical engineering literature. [ABSTRACT FROM AUTHOR]

In most cases, PROMETHEE method just applies to traditional multicriteria decision making (MCDM) problems with independent criteria. However, there exist more or less interdependences among criteria in actual situations. A special case is MCDM with prioritizations among criteria, called prioritized MCDM. In recent years, how to deal with MCDM problems in the environment of prioritized criteria becomes hot topic increasingly. Lots of existing methods, especially some methods based on aggregated operators, are modified for the prioritized MCDM. However, up to now, PROMETHEE methods are not very mature when used into prioritized MCDM problems. Therefore, our purpose is to modify traditional PROMETHEE methods according to prioritized MCDM after considering the characteristics of both PROMETHEE methods and prioritized criteria. Firstly, preference information is not static any longer for prioritized criteria, so we design an approach to weight the criteria dynamically based on a new concept—preference expectations. Furthermore, an ordered weighted averaging operator is used to generate pseudo-criteria for the situation of weakly ordered prioritizations. In such a case, the situation of weakly ordered prioritizations is transformed into that of strictly ones. After quantifying preference information properly, we can then calculate aggregated preference indices which are important intermediate outcomes for PROMETHEE to rank alternatives. An example, for assessing the strategic status of islands and reefs, is taken to illustrate the practicability and feasibility of our method. [ABSTRACT FROM AUTHOR]

The selection of a best alternative method to minimize air pollution and energy consumption for mine sites is a critical task because it encompasses evaluation of different techniques. The aim of this paper is to select most suitable technology for mining system which helps in reducing air pollution and carbon footprints by implementing the multicriteria decision analysis (MCDA) method. The existing methods or frameworks in the mining sector, which have been used in the past to select the sustainable solution, are lacking aid of MCDA, and there is a need to contribute more in this field with a promising decision system. The MCDA method is applied as a probabilistic integrated approach for a mine site in Canada. The analysis involves processing inputs to the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method which assists in identifying the alternatives, defining the criteria, and thus outranking of the final choice. Moreover, criteria weighting has been determined using analytical hierarchical process (AHP) method. Three categories: reduction of dust/fugitive emission control strategies, reduction in fuel consumption to minimize carbon footprint, and cyanide destruction methods are selected. The probability distributions of criteria weights and output flows are defined by performing uncertainty analysis using the Monte Carlo simulation (MCS). The sensitivity analysis is conducted using Spearman's rank correlation method and walking criteria weights. The results indicate that the integrated framework provides a reliable way of selecting air pollution control solutions and help in quantifying the impact of different criteria for the selected alternatives. [ABSTRACT FROM AUTHOR]

Investment and policy decisions in the context of sustainable development are classic application areas for multi-criteria decision analysis. Ranking various pathways, i.e. conversion routes, for biomass use in the energy sector is particularly challenging. Depending on how ecological, economic, and social criteria are weighed, a multi-criteria decision analysis can lead to significantly contrasting recommendations. In this paper, we present a decision support for eleven energy pathways using decision criteria drawn from all three sustainability dimensions—ecological, economic, and social. For the graphical presentation of the relatively large number of pathways and criteria weightings, we introduce a novel visualization approach that combines the results of both PROMETHEE I and II. This visualization approach permits stakeholders to quickly and intuitively gather insights about the result structure and the consequences of different input parameters, for instance different criteria weightings. [ABSTRACT FROM AUTHOR]

In this work, the process parameters optimization problems of abrasive waterjet machining process are solved using a recently proposed metaheuristic optimization algorithm named as Jaya algorithm and its posteriori version named as multi-objective Jaya (MO-Jaya) algorithm. The results of Jaya and MO-Jaya algorithms are compared with the results obtained by other well-known optimization algorithms such as simulated annealing, particle swam optimization, firefly algorithm, cuckoo search algorithm, blackhole algorithm and bio-geography based optimization. A hypervolume performance metric is used to compare the results of MO-Jaya algorithm with the results of non-dominated sorting genetic algorithm and non-dominated sorting teaching–learning-based optimization algorithm. The results of Jaya and MO-Jaya algorithms are found to be better as compared to the other optimization algorithms. In addition, a multi-objective decision making method named PROMETHEE method is applied in this work in order to select a particular solution out-of the multiple Pareto-optimal solutions provided by MO-Jaya algorithm which best suits the requirements of the process planer. [ABSTRACT FROM AUTHOR]

The design of stepped spillways is a multi-objective optimization problem in which the uncertainty of parameters plays a key role in the stepped spillways' design. A Fuzzy Transformation Method (FTM), as a first attempt, was employed to address the uncertainty through a multi-objective optimization of the stepped spillway. The proposed methodology comprises two parts. A numerical model is developed by FLOW3D® and the Multi-Layer Perceptron artificial neural network (MLP-ANN) meta-model was trained and validated to substitute the numerical model. Then, the meta-model is coupled with a multi-objective optimization model to find the optimal design scenarios in each α-cut level. Finally, two multi-criteria decision-making (MCDM) models are applied to achieve the lower and upper bounds of optimum solutions. The application of proposed methodology in Jarreh dam, Iran, shows its capability in satisfying the objectives and also addressing the uncertainty in stepped spillways' design. [ABSTRACT FROM AUTHOR]

Purpose: Due to efforts to reduce dependence on limited fossil energy reserves and increasing GHG emissions related to fossil fuel extraction and use in transportation vehicles, renewable fuel use is growing rapidly. By adding renewable oxygenated fuel additives such as oxymethylene ether (OME) to conventional diesel, combustion GHG emissions can be reduced significantly without modifications to vehicle engines. However, life cycle sustainability assessments (LCSA) of OME production and its use with diesel are scarce. The objective of this paper is to develop an LCSA model of OME production from forest biomass to be used in vehicles as a diesel additive. Methods: This study conducts an LCSA of OME production from two types of forest biomass as feedstock, whole tree and forest residue. A framework was developed to assess environmental, economic, and social impacts of unit operations along the life cycle for a functional unit of 1 MJ of heat produced from OME. Then, PROMITHEE (Preference Ranking Organization Method for Enrichment and Evaluation) was used to rank and select the best sustainable pathway for OME production and the most sustainable OME-diesel blend based on a number of indicators. Results and discussion: Based on the sustainability assessments results, the forest residue pathway is found to be more sustainable than the whole tree pathway. In addition, the environmental, economic, and social impact results for different OME-diesel blends show that a blend of 10% OME in 90% diesel is the most sustainable fuel mix. Assuming that the GHG emissions from biofuel combustion are offset by CO2 sequestered during plant growth, the biomass production operation contributes the highest global GHG emissions in the OME life cycle; this is due to the high energy intensity of harvesting operations for both pathways (13 gCO2eq/MJ for whole tree and 7.13 gCO2eq/MJ for forest residue). OME production costs are higher for the whole tree (1.92 $/L) than the forest residue pathway (1.71 $/L). All the social indicators (i.e., employment potential and employee wages and benefits) are more favorable in the forest residue pathway. Conclusions: We conducted sensitivity analyses by varying parameters such as sustainability impact weights, threshold values, and indicator impact values. We then determined the parameters' impacts on overall ranking to verify the robustness of the model. This model can be used to assess and rank other energy technologies that integrate environmental, economic, and social sustainability impacts and thereby contribute to policy-making for the energy industry. [ABSTRACT FROM AUTHOR]

Due to the momentous advantages of composite materials, recent years many studies focused on reinforcing different new materials to the existing ones to improve their conventional strength and life time within the concern of application status. In the row, reinforcements on Al6061 become a fancy topic among researchers due to its wide applications including automobiles, yachts, electrical fittings and so on. This study continues this innovation by reinforcing three different reinforcement materials including zirconia (ZrO2), zirconia + aluminium oxide (ZrO2 +Al2O3) and fused zirconia aluminum (40FZA). These three reinforcing materials are included with the proposition of varying particle reinforcements as 5, 10 and 15%. The testing specimens were experimented to explore its mechanical, wear and corrosion behavior. Further the experimental results are given as inputs to the numerical analysis, PROMETHEE. By combining the experimental and numerical methodologies the reliability of the results were improved. However, from this study it can be evident that inclusion of 15% particle reinforcement of zirconia fused alumina in Al6061 provides greater strength, toughness, high resistance to wear and corrosion on both experimental and numerical analysis. There is ample room that this proposed material inclusion be a better option for the reinforcement of Al6061 among available alternatives for sustainable development. [ABSTRACT FROM AUTHOR]

The PROMETHEE method, one of the most widely used and best known methods, takes advantage of the outranking principle to rank potential alternatives. The probability multi-valued neutrosophic sets (PMVNSs) have the power to describe complex uncertain information more comprehensively. Thus, in order to integrate the merits of PROMETHEE method and PMVNSs, this paper extends the PROMETHEE method to PMVNSs environment. Firstly, some basic preliminaries are reviewed, such as multi-valued neutrosophic sets (MVNSs), PMVNSs and classical PROMETHEE method. Then, we propose the operational laws of PMVNSs based on the operational rules of the MVNSs and probability distribution. Meanwhile, the score function and accuracy function of PMVNSs are given to simplify the comparison of any two probability multi-valued neutrosophic numbers (PMVNNs). Further, we develop a new distance measure for PMVNNs with unequal length, and then based on the distance measure and deviation maximization method, the attribute weights are determined; an extended PROMETHEE method for multi-criteria group decision-making with the information of PMVNSs is established to achieve the process for optimal alternative selection. In the end, a practical example concerning third party logistics providers is used to highlight the feasibility and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

With the increasing number of Web services published on the Web, many of services provide the same functionality with different quality of service. Ranking similar web services based on QoS is then an important issue. This paper proposes a hybrid approach to rank-order Skyline Web services, which mixes several methods borrowed from Multi-Criteria Decision Making field. The Skyline method is used to reduce the decision space and focusing only on interesting Web services that are not dominated by any other service. For weighting QoS criteria, we aggregate objective and subjective weights. The objective Entropy weights are extracted directly from invocation history data, however, the subjective weights are calculated using Fuzzy AHP from user opinions. Promethee method is leveraged to rank Skyline Web services, by taking advantage of the outranking relationships between Skyline Web services and generating positive, negative and Net flows. An efficient algorithm to rank-order Skyline Web services on the basis of Net flow is developed. A case study is presented to illustrate the different steps of our approach. The experimental evaluation conducted on real-world datasets demonstrates that our approach can better capture the user preferences and retrieve the best ranked Skyline Web services. [ABSTRACT FROM AUTHOR]

Multi-criteria decision analysis has been used to solve many problems. Here we describe an adaptation of PROMETHEE for measuring productivity. The results of PROMETHEE productivity analysis are displayed graphically, permitting the user to distinguish easily between four types of action: efficient, effective, frugal and inefficient actions. The productivity graph can be used for visual management and provides a simple, effective means of improving information communication within an organisation. It enhances transparency and promotes continuous improvement. Steps can be taken to improve ineffective actions using peer(s) on the frontier as example. To illustrate the use of the method we analysed the productivity of British universities. Only two old and two of the most recent universities were found to be on the frontier. Almost all of the most recent universities were classed as frugal and post-92 universities tended to be inefficient. Large old universities were generally classed as effective. [ABSTRACT FROM AUTHOR]

Increasing human activities have not only substantially altered the natural material cycle but also created new synthetic chemicals flows. Some of these chemicals, which are described as micropollutants (MPs), may result in adverse effects on human health, aquatic organisms, and ecosystems. MPs can be transported to the environment and water resources in a variety ways including domestic and industrial wastewater. Unfortunately, most MPs are only partially removed in existing conventional treatment plants. Therefore, conventional treatment plants should be modernized by advanced treatment technologies to protect the environment and human health. However, there are various mysteries about best treatment techniques, evaluation criteria, and decision-making methods. In this study, it was aimed to determine the best treatment alternatives for triclosan (TCS) which is one of the priority MPs. A total of 18 evaluation criteria were identified and prioritized by employing analytical hierarchy process (AHP) and entropy methods. Treatment alternatives were identified and their performance was assessed through a comprehensive literature investigation. In decision-making processes of determining these alternatives, “technique for order preference by similarity to ideal solution (TOPSIS),” “preference ranking organization method for enrichment evaluation (PROMETHEE),” and “Višekriterijumsko kompromisno rangiranje (VIKOR)” analytical decision-making methods were employed, and priority rankings were determined according to each decision method. The final priority ranking was found as adsorption > membrane filtration > hybrid processes > advanced oxidation processes > constructed wetlands > conventional treatment processes > biological treatment > other treatment processes. Although the obtained results are specific to TCS, the employed analytical decision methods can be also used to decide the best treatment alternatives for other MPs. [ABSTRACT FROM AUTHOR]

In this article, the effect of randomly oriented polypropylene fiber inclusion in enhancing the various geotechnical properties (unconfined compression strength, compressibility index, swell index, hydraulic conductivity, linear shrinkage, California bearing ratio) of a typical expansive semiarid soil is studied. Since the practicing engineer typically has difficulty in selecting the right mix of fiber type, dosage, and lime content to meet the optimum requirements of this targeted group of geotechnical properties, PROMETHEE is here adopted in order to assist in the decision-making process. The primary objective of this study was to evaluate different geotechnical properties for possible alternatives, the first being untreated soil itself and the second treating the soil with 6% lime. The remaining alternatives vary with fiber type, fiber size, and fiber dosage with or without lime. A further objective was to study the combined influence of factors, viz. lime addition, fiber type, fiber dosage, and fiber length at their respective levels of interactions for the targeted soil properties. The results of the study led to the identification of groups of criteria expressing similar preferences. Sensitivity analysis was carried out in order to better understand the conflicts to be solved in order to make this decision. [ABSTRACT FROM AUTHOR]

Hesitant fuzzy linguistic term sets (HFLTSs) have attracted a lot of attention recently due to their distinguished power and efficiency for dealing with the situations in which the decision makers may hesitate among several linguistic terms to assess alternatives. To extend the applicability of HFLTSs, this paper firstly analyses the existing comparison methods for HFLTSs, and then develops some new different possibility degree formulas which are convenience and easy of calculation. After that, two novel methods of hesitant fuzzy linguistic multi-criteria decision making are developed based on the priority method of fuzzy complementary preference relation and the idea of PROMETHEE, in which multiple steps may be integrated into one formula to simplify the computational process. Especially, the two proposed methods are designed to reflect the degree of one alternative to be superior or inferior to another one properly. Finally, an experiment is designed to test and compare the proposed methods and other existing ones. The results show that the proposed methods provide us with a useful way for decision making in fuzzy environments. [ABSTRACT FROM AUTHOR]