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Digital supply chains (DSCs) can be useful in unlocking supply chains to gain competitive advantage as a driver of growth, while enabling rapid response, supporting novel technology-driven approaches, and creating innovative products and services. Partner selection (PS) is one of the crucial tasks in the successful digitalization of supply chain. However, the selection of a suitable partner is not an easygoing process and is mostly associated with complexity. Various criteria are considered during the partner evaluation process. This paper delivers an efficient evaluation process to assess alternative DSC partners. The proposed approach integrates the Pythagorean fuzzy sets (PFSs), analytic hierarchy process, and complex proportional assessment under a group decision making environment for the first time in the literature. PFSs can depict experts’ evaluations with a richer structure, allowing for a more representative decision making. A case study from Turkey is conducted to validate proposed approach. This study contributes to the existing literature by developing a new evaluation model to improve DSC PS process and by proposing a new hybrid PFSs-based framework. This paper can be useful to practitioners and researchers to better understand DSC PS problem and designing effective DSC partner evaluation systems. [ABSTRACT FROM AUTHOR]

While formal and rigorous procedures are adopted to ensure the reliability of measurement results, the decision-making activities based on the information returned by measurement are often poorly structured and based on vague assumptions and reasoning, thus negatively impacting the reliability of the achieved conclusion. To overcome this problem, a structured multicriteria decision-making approach based on the analytic hierarchy process was recently proposed in the literature to properly support decision-making activities by exploiting the information provided by measurement results related to heterogeneous properties and its application was demonstrated in a case study. Using the same case study, this paper analyses the sensitivity of the obtained conclusion to various uncertainty sources that are embedded in that approach. In particular, the impact of uncertainties on both measurement and pairwise comparisons of the criteria is analyzed and their combined effect is evaluated by considering a worst case situation. Moreover, the effect of the formula adopted to aggregate the information returned by the measurement of heterogeneous properties on the final decision is considered. The performed analysis contributes to support a disciplined and methodologically sound implementation of decision-making activities, significantly improving both the repeatability and reproducibility of the whole evaluation process, as well as the confidence in the achieved conclusions. [ABSTRACT FROM AUTHOR]

This paper presents data-driven approaches to improve the active power output of wind turbines based on estimating their health condition. The main procedure includes estimations of fault degree and health condition level, and optimal power dispatch control. The proposed method can adjust the active power output of individual turbines according to their health condition and can thus optimize the total energy output of a wind farm. In the paper, extreme learning machine algorithm and Bonferroni interval are applied to estimate fault degree, while an analytic hierarchy process is used to estimate the health condition level. A scheme for power dispatch control is formulated based on the estimated health condition. Models have been identified from supervisory control and data acquisition data acquired from an operational wind farm, which contains temperature data of gearbox bearing and generator winding. The results show that the proposed method can maximize the operation efficiency of the wind farm while significantly reducing the fatigue loading on the faulty wind turbines. [ABSTRACT FROM AUTHOR]

Public–private partnership (PPP) is effective in delivering infrastructure projects. In China, PPP has been widely adopted in project finance ever since the government has permitted the private sector to get involved in the investment and operation of urban infrastructure. However, there seem to be many spurious PPP projects emerging in the market, which may bring about losses to the stakeholders and lead PPP away from orderly and standardized development. This paper attempts to address this issue by applying the fuzzy analytic hierarchy process to investigate two infrastructure PPP projects, and analyze the characteristics of genuine and spurious PPP projects in China. Recommendations for mass implementation of PPP and ways of identifying spurious PPP projects are summarized to guide the regulation of infrastructure PPP projects. The paper also provides a reference for project selection and policy-making in the future. [ABSTRACT FROM AUTHOR]

In this paper, we report on the fabrication and performance analysis of multiplexed carbon nanotube (CNT) network-based biosensors for detecting real-time deoxyribonucleic acid (DNA) hybridization. We have presented a tailor-made application of analytic hierarchy process (AHP) that matches the risk assessment of such CNT network biosensors. In this respect, we are introducing AHP in the biosensor area for the first time predicting at risk severity assessment for various analyte (DNA) concentrations. Most importantly, we are presenting AHP that allows for an analysis based on multi-factors (multi-criteria) which is augmented intuition along with real-time experimental data and expert proficiency. [ABSTRACT FROM PUBLISHER]

Using the lamination process in the high-functionality textile industry, this paper investigates the development of an approach for technology and supplier selection based on 12 factors affecting manufacturing technology selection with respect to the supply chain. In many manufacturing industries, technology selection still represents a challenging and not fully understood area, especially when it comes to choosing between competing technologies with similar levels of performance. The methodology employed identified two competing lamination technologies with high levels of development and mechanization: 1) full lamination/solvent type; and 2) dot lamination/solvent free. This was followed by the identification of multiple factors affecting manufacturing technology selection with respect to the supply chain, the use of analytical hierarchy process techniques, and a case study involving site visits and interviews with the senior management of a company operating in the high-functionality textiles industry. The analysis of empirical data gathered from the case study revealed how supply chain related factors are more important than those directly related to the technical merit of the technology such as low-cost manufacturing or automation. The proposed approach has the potential to be transferable to other industries using lamination processes and/or advanced fiber and fabric technology. [ABSTRACT FROM AUTHOR]

Software packages that meets the requirements of an organization should be appropriately investigated and evaluated. Picking up a wrong software package may adversely influence the business process and working function of an organization. Inappropriate software selection can turn out to be costly and it is a time-consuming decision-making process. This paper aims to provide a base for selecting the open source software packages based on analytic hierarchy process and technique for order preference similarity to ideal solution methodologies. In addition, the priority weights are generated and optimized by using teaching–learning based optimization approach. A well-organized algorithmic procedure is given in detail and a numerical example is examined to illustrate the validity and practicability of our proposed methodologies. [ABSTRACT FROM AUTHOR]

The influences of network reconfiguration and distributed generations (DGs) on distribution system state estimation (DSSE) and measurement placement should not be ignored in active distribution systems. In this paper, considering network reconfiguration and the output uncertainties of DGs, a robust measurement placement method for active distribution systems is proposed based on measurement saturation analysis and heuristic algorithm. First, the saturation number determined by measurement saturation characteristic is chosen as the measurement number. Then, the impacts of different network topologies are represented by various weights in the robust measurement placement model, which are computed by Markov chain and analytic hierarchy process. In addition, Gaussian mixture model is applied to approximate the power fluctuations of DGs. Uncertainties caused by measurements are considered by Monte Carlo simulations. The accuracy of DSSE in different network topologies can be guaranteed by the proposed robust measurement placement method. Simulation results based on the IEEE 33-bus and 119-bus distribution systems demonstrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Until now, multi-hop device-to-device (MD2D) routing in mobile wireless networks has been designed to operate over a single-protocol routing framework. Consequently, to the best of our knowledge, no framework has been developed to enable the dynamic deployment and switching of multiple MD2D routing protocols under one framework. This paper proposes a multi-protocol framework to introduce the idea of clustering a cell and deploying different MD2D routing protocols for each cluster based on the cluster requirements. To this end, four clusters are developed with varying network conditions (i.e., node density, mobility rate, and the number of flows). Then, the performance of our two previously designed multi-hop routing protocols, namely hybrid SDN architecture for wireless distributed networks (HSAW) and source-based virtual ad hoc routing protocol (VARP-S), are investigated in each cluster in terms of energy consumption, end-to-end (E2E) delay, packet loss, and cellular-band overhead. Based on the achieved simulation results, a multiple-criteria decision-making (MCDM) approach based on the analytic hierarchy process (AHP) is developed to choose the most suitable protocol for each cluster to provide the best performance. The simulation results indicate that our proposed multi-protocol framework provides better performance compared to traditional single-protocol architectures. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) with a static sink suffer from concentrated data traffic in the vicinity of the sink, which increases the burden on the nodes surrounding the sink, and impels them to deplete their batteries faster than other nodes in the network. Mobile sinks solve this corollary by providing a more balanced traffic dispersion, by shifting the traffic concentration with the mobility of the sink. However, it brings about a new expenditure to the network, where prior to delivering data, nodes are obligated to procure the sink's current position. This paper proposes Tuft, a novel hierarchical tree structure that is able to avert the overhead cost from delivering the fresh sink's position while maintaining a uniform dispersion of data traffic concentration. Tuft appropriates the mobility of the sink to its advantage, to increase the uniformity of energy consumption throughout the network. Moreover, we propose Tuft-Cells, a distributed dissemination protocol that models data routing as a multi-criteria decision making (MCDM) in three steps. To begin with, each criterion constitutes a random variable defined by a mass function. Each of these cirterion serves a proportionately distinguishable alternative, and hence, may conflict. Therefore, the analytic hierarchy process (AHP) quantifies the relationship between criteria. Finally, the final forwarding decision is derived by a weighted aggregation. Tuft is compared with state-of-the-art protocols, and the performance evaluation illustrates that our protocol adheres to the requirements of WSNs, in terms of energy consumption, and success ratio, considering the additional overhead cost brought by the mobility of the sink. [ABSTRACT FROM AUTHOR]

Wars of all degrees and intensities have the potential to intensely affect the economic, social, political, and institutional values of a nation. Among air power (AP) , land power, and sea power, AP plays the most decisive role in the outcome of any war. Formation of any air combat unit ( ACU) to encounter hostility is faced with twofold problems. The first one lies in raising the unit taking into account the configuration of various categories of weapons the unit should hold for its functional role and subject to various constraints. Second, any combat unit is interested in selecting the right vendor who can supply the required category of weapons based on budget allocation. In this paper, the selection of alternatives for the procurement of weapons is initially discussed with the help of analytic network process approach. This paper then discusses a hierarchical weapon systems valuation model framework and applies analytic hierarchy process in establishing two major indices used to evaluate AP effectiveness and AP potential. They are termed as air power index ( API) and air power value (APV). Using API and APV as tools, an optimization model for raising an ACU considering several representative constraints, which the unit may come across, is developed. All models are demonstrated with the help of illustrative examples. [ABSTRACT FROM PUBLISHER]

Three-way decisions play an important role in rough sets and decision theory. As a representative model, decision-theoretic rough sets (DTRSs) provide a sound interpretation of thresholds used in three-way decisions. This problem is associated with the determination of the loss function of DTRSs. In this paper, we investigate a novel way of determining the loss functions of DTRSs with relative values. More specifically, with the aid of analytic hierarchy process (AHP) method, the determination of loss functions is realized in the context of DTRSs. First, a hierarchical structure of DTRSs is constructed. Second, along the hierarchical structure, pairwise comparison matrices are analyzed in a top-down fashion. In light of the generic condition imposed on the loss functions of DTRSs, some constraints on relative values between loss functions are introduced. The relative ratios at each level are computed. Considering the consistency ratio (CR) of the reciprocal matrices, two mathematical programming approaches are developed by exploiting the flexibility of information granularity. Then, we design a decision procedure for the determination of loss functions and deduce three-way decisions. The loss functions are calculated by aggregating the relative ratios being available at each level. With regard to the loss functions, we finally compare the existing studies with the AHP method. We demonstrate that the relative value with AHP improves the restriction of the existing studies and exhibits a certain level of tolerance to inconsistency. [ABSTRACT FROM PUBLISHER]

This paper presents an innovative approach to formulate a customer-oriented demand response (DR) program in the electricity market using the analytic hierarchy process (AHP). AHP could represent the mutual importance degrees of load reduction criteria from the various viewpoints of load-holders. Thus, the proposed DR program (DRP) could provide the reasonable decision-support process for load-holders, because the participation or nonparticipation is determined by the incentive payments corresponding to the specific load reduction conditions. The proposed DRP has the objective to maintain the system reliability at the emergency period, and it is designed as an incentive-based program (IBP) considering the real-time and by-directional operation. The modified IEEE 24-bus system for the case study is used to demonstrate the usefulness and applicability of the proposed approach, and the load profiles with the proposed DRP and the required incentive payments are obtained. The results show not only the enhancement to the system reliability but also the economic benefits within Pareto-improvement. [ABSTRACT FROM PUBLISHER]

A traction power-supply system (TPSS) is the only power source for electric locomotives; therefore, its safe and consistent operation is of critical importance to reliable, high-speed operation. Electrical faults can disrupt train scheduling and jeopardize the safety and security of high-speed railways. Thus, it is essential to quantitatively evaluate possible risk factors in TPSSs. To do so, based on 24-h train timetable, this paper defines indices to describe the failure risk to traction power-supply equipment, and a separate set of indices present the risk from various power-quality problems. Combining both the equipment and power-quality risks yields a comprehensive risk-assessment for the TPSS, by means of a weighted evaluation. The proposed risk assessment method can ensure informed personnel by communicating the system's risk state in real-time to identify high-risk operation, thereby avoiding the potential for severe accidents and informing maintenance and operational plans. [ABSTRACT FROM AUTHOR]

As the typical clean and renewable energy, wind energy has witnessed a continuous annual increase in the last few decades. Due to the random and intermittent characteristics, the wind producers in the electric market meet serious financial losses caused by the deviation between the actual power output and forecasting result. It is difficult to increase the accuracy of forecasting in a short time. How to improve the financial income has become a major task to wind producers and academia. Being the backbone network of the offshore wind farms (OWFs), voltage source converter-based multi-terminal HVdc system (VSC-MTdc) has been regarded as one of the effective solutions to transport wind power. In this paper, an optimal distribution method is proposed for VSC-MTdc system integrating OWFs to reduce the financial loss due to wind power output deviation. The proposed method could be divided into two optimizing functions. The first optimizing function of the proposed method is to analyze the onshore external system according to the historical system operation data and adjust the droop coefficient with the analytic hierarchy process. The second optimizing function of the proposed method is to do further adjusting with the regulating price. With the case study, the proposed optimal distribution method has proved that it can bring more benefit to wind producers. [ABSTRACT FROM AUTHOR]

Many fault detection and identification methods have been developed in recent years; each method works under its own assumption, which means a method that works well under one condition may not provide a satisfactory performance under another condition. In this paper, several data-based process monitoring methods are used in order to provide an effective monitoring scheme for processes under various conditions, and then the analytic hierarchy process approach is introduced for model prioritization. Compared with conventional ensemble systems, the proposed method is able to provide different priorities for different models in monitoring different process faults. Furthermore, a new fuzzy decision fusion system is designed for the purpose of online process monitoring. The effectiveness of the developed method is verified through using the Tennessee Eastman benchmark process. [ABSTRACT FROM AUTHOR]

In recent years, in order to provide a better quality of service (QoS) to Internet of Things (IoT) devices, the cloud computing paradigm has shifted toward the edge. However, the resource capacity (e.g., bandwidth) in fog network technology is limited and it is essential to efficiently bind the IoT applications with stringent QoS requirements with the available network infrastructure. In this paper, we formulate a joint user association and resource allocation problem in the downlink of the fog network, considering the evergrowing demand of QoS requirements imposed by the ultra-reliable low latency communications and enhanced mobile broadband services. First, we determine the priority of different QoS requirements of heterogeneous IoT applications at the fog network by enforcing the analytical framework using an analytic hierarchy process (AHP). Using the AHP, we then formulate a two-sided matching game to initiate stable association between the fog network infrastructure (i.e., fog devices) and IoT devices. Subsequently, we consider the externalities in the matching game that occurs due to job delay and solve the network resource allocation problem by applying the “best-fit” resource allocation strategy during matching. The simulation results illustrate the stability of the user association and efficiency of resource allocation with higher utility gain. [ABSTRACT FROM AUTHOR]

In order to deal with a complex decision making problem, a group of experts are commonly invited to express their opinions and reach a final decision. For the purpose of building consensus among the members of the group, it is requisite to include iterative mechanisms of brain storming. The particle swarm optimization (PSO) method can be used to model the interactive process of forming decisions. In this paper, we propose a modified consensus model of group decision making augmented by an allocation of information granularity. Under a level of information granularity, it is found that the consistency indexes of randomly created multiplicative reciprocal matrices in the analytic hierarchy process may be bigger than unity. To alleviate this limitation, a modified objective function is proposed, and it is optimized by using the modified PSO method. The information granularity is allocated by considering the reciprocity of preference relations. Some comparative studies are carried out to illustrate the proposed consensus model through numerical examples. The observations reveal that a more consistent decision can be achieved by the proposed approach. [ABSTRACT FROM AUTHOR]

This paper proposes a new and comprehensive approach for optimal sizing and placement of a wind farm (WF) in a radial distribution network (RDN) while considering several factors associated with its reliability, operation, economics and environment impact. The consideration of the reliability aspect is essential to correctly assess the impacts of forced outages of wind turbines in the field. In this context, a sequential Monte Carlo simulation-based technique has been formulated to evaluate the performance of the WF with varying capacities at different locations in the RDN. Additionally, the capability of the WF to perform Volt-Var and Volt-Watt control has been capitalized on to provide voltage support to the grid in compliance with international standards (IEEE 1547-2018). Finally, a combination of Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution has been used to assess and determine the optimal size and location of the WF, based on a selected set of performance criteria, namely cumulative violations of the steady-state voltage range, cumulative losses in RDN, cumulative violations of the line thermal rating, levelized cost of energy, and reduction in carbon emission. The proposed approach has been validated considering different modes of voltage control on the standard 33-bus RDN system. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal design method of modular multilevel converter (MMC) levels to approach the reasonable compromise between accuracy and computational efficiency. The entire procedure is benchmarked by a Thévenin equivalent 451-level MMC model. First, the calculation method of the critical maximum simulation time step is proposed to faithfully represent each voltage level of the staircase waveforms. Second, the previously developed improved analytic hierarchy process is adopted to integrate the relative errors of all the main electrical parameters into one comprehensive relative error at each MMC level. Third, the steady state, ac, and dc transient state relative error results as well as the central-processing unit times of all models are normalized and curve fitted based on the benchmark model. Finally, the optimal MMC level is obtained by the plotted curves and the given subjective weights considering the accuracy and efficiency. And the effectiveness and robustness of the proposed method are validated by various simulation scenarios on PSCAD/EMTDC. [ABSTRACT FROM PUBLISHER]

Large-scale data centers have been widely used for cloud services, and the stability of various cloud services has received additional attention from users. Although service disruptions are not as catastrophic as they once were, their impact might be more extensive than before. These outages may trigger the migration of virtual machines (VMs) located in the failure node. However, the access time of each VM is random, unlike the accident time, which can be predicted. This means that traditional migration caused by service interruptions may result in a large number of unwanted migrations, regardless of the user’s downtime experience. Migration is an expensive process in terms of the resources needed as well as the degradation of application performance during migration. A balance between the recovery time of the service (to minimize the migration resulting from a given placement) and the downtime experience of the users (to minimize the impact of access interruptions) is needed. In this paper, we propose HMGOWM, a hybrid decision-making mechanism for automating the migration of VMs. Our proposed mechanism extends the original VM migration performance cost model, greatly reducing the downtime experience of the users. To achieve high performance and a good load balance, a multi-objective monitoring system for both VMs and physical machine nodes and an adaptive VM migration-scheduling scheme for the OpenStack cloud platform are proposed. Extensive experiment results indicate that the downtime experienced by users can be efficiently reduced and that the implementation of HMGOWM outperforms the original scheduling of the OpenStack cloud platform. [ABSTRACT FROM AUTHOR]

In order to solve the problem of poor grid connected consumption capacity of renewable energy in power system, a multi-source coordinated optimal scheduling method of wind-PV-hydro-thermal-nuclear-storage is proposed in this paper. The optimal scheduling method comprehensively considers the constraints of system power balance, capacity and climbing rate of various power generation modes, establishes the objective function based on the system power purchase cost and renewable energy consumption, obtains the weight relationship between the objective functions by analytic hierarchy process. An adaptive improved genetic algorithm is proposed, and the objective function is solved and the results are compared by this algorithm and three algorithms such as adaptive improved particle swarm algorithm, respectively. The results show that the improved genetic algorithm can eliminate the local optimum phenomenon by normalizing the crossover and combining variances, and is more suitable for the multi-source coordinated optimization scheduling model compared with the other algorithms. After the multi-source coordination and optimal dispatch, the system renewable energy disposal is reduced from 657MWh to 208MWh, and the power purchase cost of the system load is reduced from ${\$}$ 30.077 to ${\$}$ 30.052M. It can be seen that the selected optimization method can enhance the system’s renewable energy consumption capacity while reducing the system’s power purchase cost, which reflects the effectiveness of the optimization method used in this paper. By changing the scaling parameters of the hierarchical analysis method, the scheduling scheme can be flexibly selected for different regions, reflecting the universal applicability of the optimization method used in this paper. To provide an efficient and accurate solution for multi-energy dispatching problems in power systems.

Multifunctional grid-tied inverters (MFGTIs) have been paid much attention to handle the commonly concerned power quality issues of the microgrids. An MFGTI can not only interface the renewable energy resource into the utility grid, but also can compensate the harmonic and reactive current in the microgrid as an auxiliary service. However, the apparent capacity of an MFGTI for power quality compensation is limited. Therefore, how to enhance the power quality of the microgrid by optimal utilization of the limited and valuable capacity becomes a technical challenge. In this paper, two optimal control objectives of MFGTIs are presented based on a comprehensive power quality evaluation algorithm by means of analytic hierarchy process theory. One objective aims to obtain the expected power quality using minimal apparent capacity of the MFGTI. Another objective focuses on enhancing the power quality as well as possible in the given available apparent capacity condition. The two proposed strategies are compared in performance, and the paper also discusses how to use them in practice for the best performance. Experimental results performed on a microgrid in the laboratory confirm the validation and feasibility of the proposed optimal control strategies. [ABSTRACT FROM AUTHOR]

Passive ultrahigh frequency (UHF) radio frequency identification (RFID) is a powerful automatic contactless identification technique that has numerous applications. The performance of passive UHF RFID system is affected seriously by the application environments. In this paper, we propose a metrical method of application environment impact for passive UHF RFID system based on fuzzy comprehensive evaluation. The metric of application environment impact is graded by the degradation of reading distance, reading rate, and reading speed of passive UHF RFID system. Analytic hierarchy process is used to calculate the weight of factors, which reduces the subjectivity of evaluation results. For avoiding the loss of valid information, weighted average operator is employed to replace traditional max min operator. The end user can know the real performance of product selected by the grade of application environment impact visually and easily use the method proposed. Experimental results indicate that the proposed method is simple, valid, fast, accurate, and practical. [ABSTRACT FROM PUBLISHER]

The green design of electromechanical products is a pivotal link to manufacturing industry. The question on how to design green products must be answered by excellent designers using both advanced design methods and effective assessment techniques of design alternatives. Making an objective and precise assessment of green designs is of increasing importance to ensure sustainable development. This work proposes a framework based on the combination of analytical hierarchy process (AHP), gray correlation (GC), and technique for order performance by similarity to ideal solution (TOPSIS) to evaluate the performance of design alternatives. AHP is used to determine the weights of performance indices and a nonlinear programming model with constraints is proposed to obtain the integrated closeness index based on the similarity closeness index from GC and distance closeness index from TOPSIS. A case study, i.e., three kinds of refrigerators, is illustrated to verify the proposed method. By comparing with existing methods, i.e., AHP-TOPSIS and AHP-GC, the effectiveness of the proposed method has been confirmed. In addition, sensitivity analysis is also provided in order to assess the robustness of the proposed method. Also, the following implication can be obtained from our results: 1) chloro–fluoro–carbons (mg/ $\text{m}^{3}$ ) (C1), production noise (dB) (C6), and environmental cost (C16) have a large impact on refrigerator’s green design since these factors carry relatively larger weights. Results of the sensitivity analysis from different cases demonstrate that the best alternative may change when different weights are assigned to the evaluation criteria. This finding means the importance of establishing a qualified group of experts/designers in design evaluation and 2) the selection of design alternatives to produce a green product is critical to product development. The main contribution of this paper is the definition and development of an effective evaluation framework to guide managers to assess product design alternatives. The results show that it overcomes the one-sidedness of AHP-TOPSIS and AHP-GC, and makes the evaluation results more objective and realistic. It provides an accurate, effective, and systematic decision support tool for green performance evaluation of product design alternatives. [ABSTRACT FROM AUTHOR]

Measurement is customarily performed to acquire information useful to support decisions. More and more frequently, the complexity of analyzed systems and problems requires to measure various heterogeneous properties and to process the returned results in order to obtain information directly exploitable to support decision-making activities. However, despite a high relevance for many engineering applications, methods for rigorously processing data about heterogeneous empirical properties have not been properly analyzed yet. As a result, decision makers attain their conclusions by subjectively fusing together the available measurement results without following a formal procedure, which has a negative impact on the verifiability and the reliability of the whole evaluation process. This paper proposes a simple and effective multicriteria decision-making method that can be applied whenever measurement results are available for the considered decision criteria. Moreover, unlike most of the scientific literature on decision making, the proposed method provides not only the most desirable decision outcome, but it also returns information on the outcome reliability. This is achieved by leveraging on measurement fundamentals. In particular, by expressing measurement uncertainty using interval values, the related interval priorities of the considered decision alternatives can be determined. The proposed methodology is finally applied to a dependability-benchmarking case study. [ABSTRACT FROM AUTHOR]

The intuitionistic fuzzy set has shown definite advantages in handling vagueness and uncertainty over a fuzzy set. Taking the powerfulness of the analytic hierarchy process (AHP) and the fuzzy AHP (FAHP) into account when tackling comprehensive multi-criteria decision-making problems, in this paper, we extend the classic AHP and the FAHP into the intuitionistic fuzzy AHP (IFAHP) in which the preferences are represented by intuitionistic fuzzy values. The IFAHP can be used to handle more complex problems, where the decision maker has some uncertainty in assigning preference values to the objects considered. The paper proposes a new way to check the consistency of an intuitionistic preference relation and then introduces an automatic procedure to repair the inconsistent one. It is worth pointing out that our proposed method can improve the inconsistent intuitionistic preference relation without the participation of the decision maker, and thus, it can save much time and show some advantages over the AHP and the FAHP. This paper also develops a novel normalizing rank summation method to derive the priority vector of an intuitionistic preference relation, on which the priorities of the hierarchy in the IFAHP are derived. The procedure of the IFAHP is given in detail, and an example concerning global supplier development is used to demonstrate our results. [ABSTRACT FROM AUTHOR]

In a previous paper in this journal named The TOC-Based Algorithm for Solving Multiple Constraint Resources, Ray considered an integrated heuristic approach named AHP/TOC comprising the analytic hierarchy process (AHP) and theory of constraints (TOC) in a multiple constraints resource product mix problem. This paper gives three typical examples and points out that the proposed approach would not generate the optimal solution. Furthermore, the reasons are analyzed in terms of the ranking approach of product priority using AHP and the adjustment approach of product mix after a new bottleneck has been identified. We clarify the cases under which the AHP/TOC method can and cannot output the optimal solution. Finally, some possible improvements are given. [ABSTRACT FROM AUTHOR]

In the 5G ultra-dense wireless heterogeneous network system, it is a crucial issue to implement an effective network selection strategy to satisfy the demands of massive edge users and novel 5G services. In this paper, we model the network selection problem of edge users requesting different services as a bipartite graph, and propose a network selection algorithm based on weighted bipartite graph matching for 5G ultra-dense heterogeneous networks, named BGMNS. The proposed algorithm combines Analytic Hierarchy Process (AHP) and Grey Relation Analysis (GRA) to analyze the preferences of multiple services for different network attributes and obtain the Quality of Experience (QoE) of different edge users for each network. Moreover, in order to realize the fair allocation of network resources, we comprehensively consider the importance of the requested services and the obtained QoE by edge users to construct system fairness index. The BGMNS algorithm can optimize the overall QoE of users under the premise of ensuring the system fairness. Simulation results show that compared to the existing network selection algorithms, the proposed BGMNS algorithm can not only provide stable access to users when network status fluctuates randomly, but also effectively reduce user blocking probability as well as total packet loss rate, and significantly improve user average energy efficiency. [ABSTRACT FROM AUTHOR]

Driving safety has been a hot topic in recent vehicular research. However, research on active control strategy, by which an accident might be avoided before it really happens, is still lacking, especially those appealing to machine learning methods with real traffic data. In addition, previous works constructed models with only one or a few factors considered, while the impact of multiple factors on a collision probability is overlooked. In this paper, based on machine learning methods with an actual traffic dataset, we propose a multi-level active safety control strategy taking the Multi-source, Multi-parameter, and Multi-purpose (3M) properties of an accident into consideration. First, by analyzing the impact of different conditions on an accident with the AHP (Analytic Hierarchy Process)-Ridge regression and bisecting K-means clustering model, the safety inter-vehicle distance is derived by learning from an actual traffic dataset. Besides, ELM(Extreme Learning Machines) is adopted as a verification scheme for safety distance calculation. Subsequently, we design a three-level active safety control scheme using the LQG (Linear Quadratic Gaussian) optimal-control model based on the obtained safety inter-vehicle distance. Numerical results show that by comparing with some classical braking and car-following models, our strategy can always keep the distance of two followed vehicles at a safety state. To further explore the impact of the time complexity on the rear-end collisions, we also implemented a road-test and verified that our model can timely respond to the risks and keep two cars always in safety. [ABSTRACT FROM AUTHOR]

The number of electric vehicles (EVs) is expected to increase significantly in the future to combat air pollution and reduce reliance on fossil fuels. This will impact the power system. However, appropriate charging and discharging of EVs through vehicle-to-grid (V2G) operations could also provide support for the power system and benefits for EV owners. This raises the questions of when and how EV battery storage should be dispatched, taking into account both vehicle users' and power system's requirements and priorities, as well as the constraints of the battery system. This paper proposes a novel decentralized dispatch strategy based on the analytic hierarchy process (AHP), taking into account the relative importance of the different criteria, including cost, battery state of charge (SOC), power system contingency, and load leveling. The proposed AHP-based dispatch strategy was tested on an IEEE Reliability Test System with different EV numbers and capacities to investigate the efficacy of such an approach. The simulation results demonstrate the feasibility and benefits of this dispatch strategy. [ABSTRACT FROM PUBLISHER]

In this paper, we are concerned with the use of simulation optimization to handle collaborative design problems where more than one decision-maker is involved. We assume that the designers cannot enumerate all their considerations in closed-form, precise mathematical expressions but they can examine the merits of solutions with respect to their preferences and can compare candidate solutions with one another. We propose a three-stage framework to take the decision-makers’ such considerations into account. The first step is to obtain a diverse set of designs that can all be considered efficient in terms of a performance metric (i.e., the objective function values of the simulation optimization model). These solutions are then passed on to the decision-makers to be analyzed in terms of their preferences that could not have been previously considered. Finally, the most appropriate solution is chosen. We address the problem encountered in the first step as a multimodal optimization problem. We address the second and the third subproblems as a preference aggregation problem in the social choice theory. We also illustrate the effectiveness of the proposed approach through a supply chain design problem inspired from the literature. We use the crowding clustering genetic algorithm as an example to demonstrate the first step. We use a multiplicative variant of the popular analytic hierarchy process to illustrate how the second and the third steps can be handled. [ABSTRACT FROM PUBLISHER]

This paper presents a positive approach in an optimized design of a combinatory unified power-quality conditioner (UPQC) and superconducting fault current limiters (SFCLs). This is based on a normalized simulated annealing algorithm compared with analytic hierarchy process objective optimization. The optimization algorithm for simultaneous optimization computes and produces three-dimensional alignments in Pareto front at the end of the optimization run. The results show that the SFCL can reduce the volt-ampere rating of the UPQC by limiting the fault current, thereby reducing the cost of UPQC installation. The aforementioned algorithm requires advanced numerical techniques for simulation studies by PSCAD/EMTDC on a sample distribution system for determining a global optimal combination of UPQC and SFCL by considering individual parameters and accounting for the constraints, which is the main motivation of this paper. This will result in a more thorough knowledge of “scaling factors” and the system quality mechanism which will enable the most efficient systems from the viewpoints of cost cutting, energy savings, and downsizing. [ABSTRACT FROM AUTHOR]

We assess the impact of accounting for nonlinearity of selected value functions to improve the multiattribute utility theory approach for transportation planning applications. Research findings were obtained by conducting a case study for investigating and improving the project evaluation and selection with the collaboration of a state-level transportation agency. A methodology based on the analytic hierarchy process has been used for elicitation of the nonlinear value functions. It was found that employing nonlinear functions, as opposed to the commonly assumed linear scaling, even for only a subset of performance measures, had a significant impact in calculating the projects’ index final scores, with changes ranging from 3% to 26%, possibly overturning the project selection. This paper highlights the importance that needs to be given to construct the value functions in decision-making problems and shows how the process could be improved by employing available tools properly. [ABSTRACT FROM PUBLISHER]

This paper presents a novel framework for improving project outcomes by ensuring that systems requirements can be verified and validated against project objectives and constraints. The multiattribute process ensures that each requirement makes a measurable contribution toward meeting a system's intended use while considering the impact of known constraints. This new framework aims to increase the likelihood of program success by identifying and valuing requirements in the early stages of a system's development. This research very explicitly extends previous research and aims to overcome complications that have persisted within the literature with regard to selecting and prioritizing system requirements. The authors present the first demonstration of applying analytic hierarchy process to prioritize requirements for a United States Navy Phased Array Radar and use a novel methodology for applying AHP's ratings approach to requirements prioritization. [ABSTRACT FROM AUTHOR]

Due to ubiquitous Internet connectivity, widely available cloud services, and popular mobile devices, mobile networks have become service delivery and consumption platforms for many industries worldwide. To recommend optimal mobile Web services with trustworthy Quality-of-Service (QoS) and dynamic user preferences, this paper proposes a novel service recommendation model based on Fuzzy Analytic Hierarchy Process (FAHP) and ordinal utility function. First, a Multi-QoS vector is defined, and to take into account the trustworthiness of QoS, the fidelity of QoS is modeled as one component of the Multi-QoS vector. Then, a fuzzy hierarchy including dual attributes of QoS (objective attribute and subjective evaluation) is established to fully consider the objective and subjective attributes’ impact on optimal service recommendation. Furthermore, a FAHP-based weighting mode is developed, in which the resolution ratio of weight can be adjusted dynamically by decision-maker according to user preferences. Finally, the optimal service is obtained through the calculation of ordinal utility function of candidate service. Experimental results and method comparison illuminate the feasibility and efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

Mega-engineering projects in the construction industry are generally carried out by public–private partnership models with the participation of different stakeholders. This paper aims to identify the effect of stakeholder-associated risks on the success of mega-engineering projects. Therefore, the risks were determined and then validated by focus discussions. Afterwards, an illustrative case study was presented to better understand the effects. Finally, a fuzzy analytical hierarchy process method was used to prioritize the identified risks. The prominent risks were found to include “improper partner selection,” “inadequate relationships with employer,” and “incompetent contractor selection.” Engineering managers need to work in close coordination with all members of the project. To better manage projects, engineering managers are expected to be familiar with the concepts of project risk management, because effective project risk management has a direct and positive effect on project management performance. Professionals in the field of engineering management can benefit greatly from the findings of this study by using it as a baseline for coordinating the relationships in multistakeholder organizational structures and analyzing stakeholder-associated risks throughout their projects. [ABSTRACT FROM AUTHOR]

Cybersecurity is one of the major issues to be considered while designing a communication network for Smart Grid (SGCN). However, due to the openness and unpredictable nature of the wireless network, it is often prone to different adversarial attacks that exploit vulnerabilities to launch cross-layer attacks during data transmission. To address this issue, we proposed a novel trust-based routing framework with Bayesian Inference to calculate direct trust and Dempster-Shafer (D-S) theory is used to compute the indirect trust by combining the evidence collected from reliable neighbours. The analytical hierarchy process (AHP) calculates the node’s creditability trust by taking cross-layer metrics like transmission rate, buffering capacity and received signal strength. Further, the fuzzy theory is combined with BDS-AHP to incorporate fairness in utilizing cross-layer metrics for calculating the link trust to enable reliable routing. Extensive experiments are carried to evaluate the performance of the proposed fuzzy-based trusted routing algorithm (FBDS-AHP) with the presence of malicious nodes launching packet-dropping, bad-mouthing, on-off and Sybil attacks. Simulation result proves that the proposed trust evaluation algorithm achieves promising results in defending against these attacks thereby providing improved reliability and ensures secured routing in SGCN. [ABSTRACT FROM AUTHOR]

The polarization and depolarization current (PDC) measurement has been widely used to assess the aging degree of cross-linked polyethylene (XLPE) cables. However, the aging type identification of XLPE cables has not been fully considered. In this study we conduct PDC measurement on thermal-aged and water tree-aged XLPE cable samples respectively, upon which a graphical approach to assess the insulation condition and distinguish the aging type of XLPE cables is proposed. First, to designate the evaluation indexes of the insulation condition, multiple PDC measurement based dielectric parameters are considered. Then, to reflect the sensitivity of each evaluation index to the condition of XLPE cable, a combined weighting method based on the group fuzzy analytic hierarchy process (GFAHP) and entropy method (EM) is introduced. Furthermore, the improved radar chart method (RCM) based graphical approach is proposed to comprehensively and intuitively characterize the evaluation indexes. Finally, the aging degree of the XLPE cable samples is quantitatively assessed by calculating the area of the polygon in the chart. Simultaneously, utilizing the position of the areal coordinate and the shape characteristic parameter of the polygon, the thermal-aged and water tree-aged samples are effectively distinguished. [ABSTRACT FROM AUTHOR]

The analytic hierarchy process (AHP) has been widely applied to multicriteria decision making problems. The AHP aids decision makers to determine the priorities of multiple criteria, and make reasonable decisions. In the AHP, evaluating candidate alternatives requires multiple experts’ evaluations to avoid personal subjectivity. Although having more experts can improve selection quality, inviting more experts also results in higher recruitment cost and longer evaluation time. In this paper, we introduce the idea of optimal computing budget allocation (OCBA) and propose a method, named the AHP_OCBA method, to improve the efficiency of expert allocation. The proposed method optimizes the allocation of experts to maximize the probability of correctly selecting the best alternative in the AHP. This method also can minimize the required number of experts to meet the probability of correct selection. An illustrative example is provided to indicate the implementation of the AHP_OCBA method. We show the improvement of the proposed method compared to proportional and equal allocation rules in the numerical result. [ABSTRACT FROM PUBLISHER]

This paper proposes a modification to the analytic hierarchy process (AHP) to select the most informative genes that serve as inputs to an interval type-2 fuzzy logic system (IT2FLS) for cancer classification. Unlike the conventional AHP, the modified AHP allows us to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test, and signal-to-noise ratio. The IT2FLS is introduced for the classification task due to its great ability for handling nonlinear, noisy, and outlier data, which are common problems in cancer microarray gene expression profiles. An unsupervised learning strategy using the fuzzy c-means clustering is employed to initialize parameters of the IT2FLS. Other classifiers such as multilayer perceptron network, support vector machine, and fuzzy ARTMAP are also implemented for comparisons. Experiments are carried out on three well-known microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, and prostate. Rather than the traditional cross validation, leave-one-out cross-validation strategy is applied for the experiments. Results demonstrate the performance dominance of the IT2FLS against the competing classifiers. More noticeably, the modified AHP improves the classification performance not only of the IT2FLS but of all other classifiers as well. Accordingly, the proposed combination between the modified AHP and IT2FLS is a powerful tool for cancer classification and can be implemented as a real clinical decision support system that is useful for medical practitioners. [ABSTRACT FROM PUBLISHER]

This paper presents a bilevel model for describing the interdependence of decisions between a retailer/aggregator and the distributed resources owners that the former serves and represents. Decision making is performed in a hierarchical fashion. The retailer performs the energy management of various resources indirectly; price signals are used as incentives for inducing the entities owning the distributed resources in a behavior that optimizes the operation and economic profit of the microgrid that they compose. The two-level interaction is formulated mathematically as a mathematical programming problem with complementarity constraints, while nonlinearities are replaced by equivalent linear expressions. The results of a characteristic case study indicate the efficiency and scalability of the proposed model. [ABSTRACT FROM PUBLISHER]