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Multi-criteria recommender systems have been increasingly useful for helping consumers identify the most relevant items based on different dimensions of user experiences and highlighting their most valued features. Therefore, researchers have proposed various multi-criteria models to improve recommendation performance. However, most of the existing methods utilize only multi-criteria ratings explicitly provided by the users. Note that explicit multi-criteria ratings are sparse and have the problem of missing values. User reviews, on the other hand, contain richer information of user experiences and reveal multi-dimensional user preferences. Therefore, we propose to use latent multi-criteria ratings generated from user reviews, as opposed to explicit multi-criteria ratings, to provide recommendations and capture latent complex heterogeneous user preferences. Specifically, we propose two novel models for the latent multi-criteria rating generation process: the one-stage model LatentMC-1S that utilizes document hashing method to directly compute latent ratings and the two-stage model LatentMC-2S that uses GRU and Gumbel-Softmax for indirect rating generation. Extensive experiments show that the proposed latent multi-criteria rating approaches outperform explicit ratings across different datasets and performance measures. We also show that latent multi-criteria ratings could be used for imputing missing explicit multi-criteria ratings and thus further improving multi-criteria recommender systems.

During many decades, the town of Moulay Yacoub underwent an important urban expansion to meet the high demand on housing fuelled by tourism activities, which constitute the backbone of the town’s economy. Unfortunately, the majority of buildings, both private and public, suffer from varying levels of damage related to the lithological, climate, and geomorphic settings of the area. In fact, the town is built on a marly hill prone to various types of mass movements, ranging from shallow soil creep to large slides. In addition, vertical displacement related to swelling/shrinkage behaviour of these Miocene marls is widespread in the area. The present paper presents a multi-disciplinary approach to investigate the activity and the interaction between slow-moving urban landslides and expansive soils within the urban perimeter of Moulay Yacoub. In fact, the severe seasonal contrast characterised by intense rainfall over short periods constitutes the main triggering factor of the instability phenomena in the region. Moreover, the desiccation cracks affecting marly soils are indicators of their expansive behaviour, which is very obvious in geotechnical tests results. The other geotechnical parameters obtained from laboratory tests show that the shallow marl samples are severely weathered compared with those of the compact material extracted at greater depth. The borehole data and seismic noise survey allows the detection of several impedance contrasts corresponding to the weathered layer – bedrock boundary, which in some cases corresponds to the rupture surface of the inventoried landslides. The very slow but perennial activity of the later processes is well documented by the inclinometers, the PS-InSAR monitoring, and building damage assessment surveys. Indeed, this case study highlights the complementarity of techniques used in this multi-disciplinary approach that give a multi-faceted understanding of slope instability processes and should provide a blueprint for future site-specific studies in the region.

This paper explores the spatial aspects of GIS-based multi-criteria evaluation. We provide a systematic account of geographically defined decision criteria based on three classes of spatial relations: location, proximity, and direction. We also discuss whether the evaluation score of a decision alternative should be directly influenced by neighbouring scores and outline a methodology for distance-based adjustment of evaluation scores. A home buyer case study is employed to demonstrate how spatial criteria can be included in a spatial decision support system and to investigate the effect of geographically adjusting the evaluation scores of decision alternatives. The case study demonstrates how spatial criteria can be presented to decision-makers and their effects be observed in the decision outcome. Further, the spatial adjustment of evaluation scores using the performance of neighbouring properties smoothes the distribution of scores across the study area and allows decision-makers to consider a location’s environment. Keywords: Multi-Criteria Evaluation, Residential Real-Estate Choice, Spatial Decision Support Systems, Spatial Relations.

Roundabout design has been evolving to include versatile types that target specific characteristics of each considered location. They started addressing maneuverability issues and directional splits, among other factors. Therefore, research is required to investigate the functionality and performance gains associated with each new design element. This study was conducted to provide a comparative assessment of three roundabout types (modern, elliptical, and turbo) using microsimulation. The multi-criteria assessment considered three evaluation criteria, namely, safety, mobility, and environmental impacts, for different traffic and pedestrian volumes. The findings indicated that the three roundabout types provided substantial safety and environmental gains when compared to a signalized intersection, while the mobility gains were less significant. Among the three roundabout types, the turbo roundabout outperformed the other two types at low and medium traffic flow levels (≤3000 pcu/h) in terms of safety and mobility, while the elliptical roundabout performed better at high congestion scenarios (>3000 pcu/h). On the other hand, the elliptical roundabout had the worst environmental performance at all traffic volumes. In-depth safety assessment that was conducted using simulated traffic conflicts revealed that 75% of conflicts on roundabouts occurred between approaching vehicles, while only 25% occurred between circulating vehicles. Still, circulating conflicts were considerably more severe. The three roundabouts were especially effective in reducing pedestrian-vehicle conflicts, they also reduced rear end conflicts but to a lesser degree. Moreover, the elliptical roundabout experienced higher severity of conflicts, even when the conflict frequency was lower.

Consensus reaching is a key issue in group decision-making, because conflicts of interest among groups are common. Democratic consensus refers to achieve a soft consensus among collective as well as ensure the effective participation and satisfaction of individuals. Multi-person multi-criteria large scale decision making (MpMcLSDM) usually involves a huge number of decision makers (DMs/participants), and different DMs usually have different interests. Thus, how to effectively manage individuals to promote democratic consensus is a current research challenge. To do that, this research develops a democratic consensus reaching process (DCRP) for MpMcLSDM problems. In the proposed approach, a clustering method that considers both the opinion similarity and individual concern similarity of DM is firstly given to decrease the complexity of MpMcLSDM issues. Subsequently, we propose to assign equal initial weight to each cluster to protect the interests of minorities. Meanwhile, a consensus contribution-based dynamic interactive weight updating method is implemented in the DCRPs to promote a high level of democratic consensus. Besides, a compromise degree-based consensus feedback strategy is developed to improve the efficiency of the DCRPs. The proposed feedback mechanism effectively considers the individual concern and adjustment willingness of DMs in the DCRPs. Finally, a case study and some comparisons are given to show the effectiveness and innovation of this research.

In recent times, there have been developments in the maritime industry that underscore the need to optimise operations to yield maximum productivity. Apart from this, stakeholders in this industry have also advocated improvements in seaport operations' critical areas. However, there is no known study in which the relationship between performance criteria and seaport operation measures is investigated. This study proposes a framework for selecting operation measures for the maritime industry. It uses stakeholders' expectations for operational criteria and fuzzy logic to design the framework. Nine criteria were considered in the framework, while Fuzzy VIKOR (VIsekriterijumska optimizacija I KOmpromisno Resenje) and fuzzy Shannon entropy were incorporated into it. The framework's applicability was tested using information that was obtained from Tin can port, Lagos, Nigeria. During this process, hinterland traffic diversion (A1), congestion pricing (A2), off-dock container yards (A3), Fast rail shuttles (A4), expanded rail connections (A5) were considered as alternatives for seaport operational measures. When the developed framework was used to analyse the collected information from Tin Can port, Lagos, Nigeria, the fuzzy VIKOR index ranked the alternatives as A1 » A2 » A3 » A5 » A4. Therefore, this study's insights show that mathematical models can be used to make informed seaports decisions.

Building carbon emission prediction plays an irreplaceable role in low-carbon economy development, public health protection and environmental sustainability. It is significant to identify influential factors mainly contributed to building emission and predict emission accurately in order to harness the growth from the source. In this paper, 11 influencing factors of building carbon emission are identified and a support vector regression (SVR) prediction model is proposed to forecast building carbon emission considering improvement the prediction accuracy, generalization, and robustness. In the SVR model, parameters are optimized by particle swarm optimization (PSO) algorithm with the aim to improve performance. Cases in Shanghai’s building sector are adopted to demonstrate practical applications of the proposed PSO-SVR prediction model. The results indicate that the presented prediction system has an outstanding performance in forecasting building carbon emission under multi-criteria evaluation. Furthermore, compared to the results from other four prediction models (e.g., linear regression, decision tree), it is shown that PSO-SVR model can achieve higher accuracy (e.g., improvement average of 1.01% R2 under training subset), better generalization (e.g., improvement average of 19.89% R2 under testing subset), and better robustness (e.g., improvement average of 18.93% R2 under different levels of noise intensity).

The lack of road safety associated with cycling is still one of the main barriers to its greater use as a transport mode. The implementation of bikeways arises as an alternative way to provide a safer environment for bicycle traffic. In this context, this article proposes a cycle-lane safety-level inspection tool to support the decision making of opportunities for safety improvements. A multi-criteria inspection tool was developed based on a weighted index that takes into account the perceptions of users, managers, and researchers regarding safety on cycle lanes, and the real local assessment of multiple attributes, not in comparison to an ideal situation. The results show that 40% of the weight of influence on safety in cycle lanes was accumulated in 20% of the attributes: users’ attitudes, geometry of the cycle lane design, relative speeds between vehicles and obstacles present in the bikeways, suggesting that prioritizing the treatment of these attributes can lead to significant safety gains. Conversely, attributes related to drainage conditions, presence of heavy vehicles, bicycle maintenance and use of auxiliary accessories were the least important for the perception of road safety. Finally, the tool was tested in a complex case study of three cycle lanes in the city of Porto Alegre, Brazil.

One of the major challenges that populations have to face is vulnerability to water: lack of water, flooding, pollution, hazard sensitivity and coping capacity. Owing to economic, social and environmental inequalities, this paper focuses on water-related vulnerabilities in the Metropolitan Region of Santiago (RMS) in Chile. Our main objective is to understand, through mapping, the multiscalar logics of water vulnerability. This study is carried out at two scales: at the regional level (RMS), we proceed with open access municipal statistical data and maps, while at the local level, a more detailed analysis focusing on the Chacabuco Province is based on the same type of data but with either a higher spatial resolution (census districts) or a spatially more intensive data processing in order to take into account intra-municipal differences. In this way, we put into perspective the discourse developed in the Chilean media and by the inhabitants of Chacabuco Province regarding the province as an environmental ‘sacrifice zone’ for the RMS. The vulnerability maps are carried out at different scales in a simple and reproducible way by multi-criteria evaluation (MCE). The results confirm the hypothesis of a sacrifice zone and show that high-resolution data and adequate data processing give, on average, lower vulnerability scores than using only statistical data on the municipal level. The results provide a cartographic decision support for stakeholders. Limitations of the study are discussed and required further research is pointed out.

This paper aims to develop a Fermatean fuzzy ELECTRE method for solving multi-criteria group decision-making problems with unknown weights of decision makers and incomplete weights of criteria. First, a new distance measure between Fermatean fuzzy sets is proposed based on the Jensen–Shannon divergence. The cross entropy for Fermatean fuzzy sets is defined. Three kinds of dominance relationships for Fermatean fuzzy sets are proposed. Then, two optimization models are constructed to obtain positive ideal decision-making information and negative ideal decision-making information, respectively. Accordingly, the credibility degree of each decision maker is calculated. Decision makers’ dynamic weights are determined by their credibility degrees. Besides, to obtain the weights of criteria, an optimization model is constructed based on grey relational analysis for Fermatean fuzzy numbers. Finally, the strong, medium and weak Fermatean fuzzy concordance and discordance sets are identified to construct the Fermatean fuzzy concordance and discordance matrices, respectively. A practical case study is carried out to illustrate the feasibility and applicability of the proposed ELECTRE method. Comparative analyses are performed to demonstrate the superiority and effectiveness of the proposed ELECTRE method.

Work objective. The article considers the process of optimizing transport and logistics system in various areas of work and represents the task of solving a complex multi-criteria system. Determining the most optimal and accurate way to solve this problem will lead to a significant stimulation of the management processes of transport and logistics complexes, in particular trucking. Research methods. The research was carried out by analyzing the solution of multi-criteria mathematical problems. Novelty of the work. The novelty of the work is due to the need to solve the main problems of optimizing the operation of the transport and logistics system. The solution of this task will stimulate the country's economy by increasing the volume of annual shipping goods, as this indicator is one of the key road transport complex of our country. Research results and novelty. According to the results of the study, the main ways of solving the problem of optimizing the transport and logistics system are determined. Conclusions: The main criteria for solving a multi-criteria task in optimizing the planning, organization and management of terminal and warehouse complexes of work are determined.

The paper aims to develop a site selection procedure for solar-diesel hybrid systems using a multi-criteria performance analysis of site options. The site selection process using this multi-criteria approach was carried out on the example of Kungurtug rural settlement (Tyva Republic). The area surrounding this settlement was analyzed, revealing four possible sites for a solar-diesel system. For evaluating the performance of these site options, the following criteria were adopted: ease of installation and maintenance of the solar-diesel hybrid system; surface topography and soil quality; convenience of the photovoltaic cell layout; environmental impact of the solar-diesel hybrid system; opportunities for further expansion of the system; orientation potential of the photovoltaic cell. In order to assess the significance of the concordance coefficient, the distribution quantile was determined, amounting to 16.2. For 5 degrees of freedom and a significance level of 0.05, the table value of the concordance coefficient amounted to 16.2. Since the distribution quantile is greater than the table value, the concordance coefficient can be considered significant (95% confidence level), indicating agreement between expert opinions. Experts ranked the site options to obtain relative performance estimates for the criteria; numeric indicators were converted into the relative estimates using linear transformation formulas. The multi-criteria performance estimates of the possible options were calculated for arithmetic mean and harmonic convolutions. After comparing the site options for the solar-diesel system, the second variant characterized by the highest criterion scores was selected for Kungurtug settlement. As a result, a site selection procedure for the elements of solar-diesel hybrid systems was developed using the theory of multi-criteria optimization and the method of expert evaluations, allowing a set of technical, economic, climatic, and environmental criteria to be taken into account.

The type 2 intuitionistic fuzzy sets (T2IFS) have a greater expressive power in representing the uncertainty involved in the information given by the experts. A very few ranking methods have been proposed to compare the T2IFS based on distance measure. This paper proposes a new distance measure called Jaccard distance for type 2 intuitionistic fuzzy set. This method ranks the T2IFS based on the dissimilarity of the given T2IFS to the ideal set. The properties of distance measure have been proved. The efficiency of proposed method is validate by comparing with existing methods. It is observed that the proposed ranking can rank the T2IFS more effectively when compared to existing methods in some tested contexts. The proposed ranking method is applied in solving a multi criteria group decision making method and the results are analyzed. From the analysis it is observed that the ordering the alternatives using proposed method agrees with the human intuition.

PurposeThe paper aims to build a customized hybrid multi-criteria model to identify the top three utilities of drones at both personal and community levels for two use cases: firefighting in high-rise buildings and logistic support.Design/methodology/approachA hybrid multi-criterion model that integrates fuzzy analytical hierarchy process (AHP), Best Worst, fuzzy analytical network process (ANP), fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) is used to compute the criteria weights. The weights are validated by a novel ensemble ranking technique further whetted by experts at the community and personal levels to two use cases.FindingsDrones' fire handling and disaster recovery utilities are the most important to fight fire in high-rise buildings at both personal and community levels. Similarly, drones' urban planning, municipal works and infrastructure inspection utilities are the most important for providing logistics support at personal and community levels.Originality/valueThe paper presents a novel multi-criteria approach, i.e. ensemble ranking, by combining the criteria ranking of individual methods – fuzzy AHP, Best-Worst, fuzzy ANP and fuzzy DEMATEL – in the ratio of optimal weights to each technique to generate the consolidated ranking. Domain experts also validate this ranking for robustness. This paper demonstrates a viable methodology to quantify the utilities of drones and their capabilities. The proposed model can be recalibrated for different use case scenarios of drones.

This paper introduces an interactive approach to support multi-criteria decision analysis of project portfolios. In high-scale strategic decision domains, scientific studies suggest that the Decision Maker (DM) can find help by using many-objective optimisation methods, which are supposed to provide values in the decision variables that generate high-quality solutions. Even so, DMs usually wish to explore the possibility of reaching some levels of benefits in some objectives. Consequently, they should repeatedly run the optimisation method. However, this approach cannot perform well – in an interactive way – for large instances under the presence of many objective functions. We present a mathematical model that is based on compromise programming and fuzzy outranking to aid DMs in analysing multi-criteria project portfolios on the fly. This approach allows relaxing the problem of rapidly optimising portfolios while preserving the beneficial properties of the DM’s preferences expressed by outranking relations. Our model supports the decision analysis on two instance benchmarks: for the first one, a better compromise solution was generated 84% of the runs; for the second one, this ranged from 93% to 97%. Our model was also applied to a real-world problem involving social projects, obtaining satisfactory results.

The Urban growth in Trikomo (Yeni İskele) region in Cyprus has dramatically increased recently. The unorganized and uncontrolled development process has started to consume land resources; loss of landcover, valuable agricultural lands, and change of wetlands of stream beds or ponds occurred. In addition, partial and fragmented housing development projects bring only housing and second housing to the coastal region. As a result, environmental and economic problems occurred in sustainable urban growth (SUG) in the Trikomo (Yeni İskele) region. Due to the lack of planning instruments in Trikomo, urban expansion policies and alternatives have been ignored. In this regard, this research tries to investigate spatial SUG and expansion alternatives by using Multi-Criteria Evaluation (MCE) and fuzzy logic within geographical information systems (GIS). Compact growth, environmental protection, and equal accessibility to local services were used for multi-criteria analysis to construct spatial SUG problems. Then they were converted to spatial layers within the (GIS) environment. Results show that; 6 percent of the study area is in a shallow suitability zone. Forty-four percent of it has very low and low suitability for SUG. Also, 41 percent of the area is suitable. Only 12 percent of the area has high and very high suitability values. These findings showed that approximately 118 square kilometers (56 percent) of the city is within the same level appropriate for urban development.

Recent developments related to the widespread utilization of electric vehicles (EV) have required the building of sound and reliable public charging networks. In the literature, this task has usual...

Predictive maintenance has evolved considerably over the past two decades making this strategy an effective way to monitor the operation of industrial systems, thereby predicting its future states and remaining lifespan. It is therefore developed through a process that begins with the collection of information from the industrial system, the objective of which is its diagnosis or / and its prognosis. This article presents an analysis of single-model and multi-model approaches to the effect of diagnostic and prognostic tasks. This analysis is based on a multi-criteria comparison of the different models in order to provide a clear vision to choose the appropriate approach for predictive maintenance. The relevance of the comparative study is argued by the development of criteria directly impacting performance, reliability, efficiency and mutual cooperation between models. Conclusions are then drawn, in order to identify the appropriate diagnostic and prognostic approach for predictive maintenance.

Based on the DEA method, an approach has been developed for the multivariate analysis of the road bitumen production processes, allowing obtaining integral comparative assessments that ensure the ranking of processes according to various heterogeneous criteria. The main quantitative characteristics, qualitative indicators, and technological parameters of the oxidation processes are selected to form target functions when solving mathematical programming problems. Based on the CCR and Super Efficiency models of the DEA method, the problems of multivariate analysis of the efficiency of road bitumen production processes for the actual values ​​of the characteristics of raw materials and parameters of technological processes were formulated and solved, a comparative analysis of the estimates obtained for 64 bitumen samples was carried out. The results of the studies carried out make it possible to significantly expand the scope of the DEA method application and create on its basis a software package for multivariate analysis and optimization of bitumen production processes by improving the quality of the final product, reducing the resources for its production and reducing the negative impact on the environment.

The design-build project delivery method allows owners to deal with a single source of responsibility for the design and construction phases of a facility. Due to its inherent complexity, there is ...

Our goal is to address the complicated problem of strategic supplier selection with interrelated and insufficient data. To achieve this goal, we proposed our Strategic Supplier Selection Methodology (SSSM). First, SSSM formulates the enterprise strategies and evaluation criteria. Then, we developed a novel method called Grey Principal Component Analysis-Data Envelopment Analysis (GPCA-DEA) to evaluate suppliers in SSSM. GPCA-DEA overcomes the major disadvantages and limitations of former methodologies (e.g., DEA) while dealing with insufficient and interrelated data. Finally, SSSM applies Multiple Attribute Decision-Making (MADM) methods to select suppliers based on the ranking score. The application of SSSM is illustrated in the payment industry to select Payment Initiation Service Providers (PISP). For the first time, we considered the payment industry-specific criteria in compliance with the latest regulation (PSD2). The Spearman rank correlation statistical test showed that our method (GPCA-DEA used in SSSM) yields more reliable results than a former version of DEA.