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As a contemporary product of Internet of Things, wearable devices that are closely related to consumer health have received widespread attention from various nations and industries. Thus, the use of wearable devices has become a significant scientific problem requiring an urgent solution. In this article, we consider the characteristics of wearable devices, consumers, the market, safety, and the environment to analyze consumer behavior. A multiple attribute decision-making (MADM) model is used in this article, including the decision-making trial and evaluation laboratory (DEMATEL) technique, the DEMATEL-based analytic network process (DANP) method, and the modified Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. Based on expert data and the index system of criteria for consumer adoption of wearable devices, we evaluate the relationship between criteria and their weight of impact to create an influential network relationship map by using the DEMATEL technique, comprehensively analyze the correlation between consumer behaviors and products, clearly identify the relative gaps between criteria and the optimal level, and recommend improvements through the DANP. Finally, we use three specific products as examples to analyze consumer satisfaction for different criteria based on the modified VIKOR method. The results reveal that there are gaps between product performance and the optimal level, and product promotion strategies are proposed that would narrow the performance gaps of these three wearable devices by prioritizing the factors that required improvement.

To reinforce competitiveness, companies must focus on competitive priorities including cost, quality, delivery, and flexibility. The competitiveness also depends on the adherence to environmental requirements and the ability of companies to survive unforeseen disruptions. Lean, agile, resilient, and green (LARG) paradigm is a hybrid supply chain paradigm, which can enhance competitiveness in today's complex markets. In this article, we propose a new conceptual decision-making model to evaluate LARG supply chain performance in medical equipment companies. In this regard, supply chain operations reference model attributes were utilized as performance evaluation criteria. Furthermore, the appropriate practices of LARG supply chain are selected through fuzzy Delphi method. Also, an extension of decision making trial and evaluation laboratory-based analytic network process method is proposed to uncover network relationships and obtain the weights for dimensions and elements. According to the results, the agile paradigm, responsiveness, and quick introduction of new products are determined as the most prioritized elements in their corresponding dimensions, which emphasize the importance of the customer satisfaction. Finally, the extended hesitant fuzzy linguistic VIKOR (Vlsekriterijumska Optimizacija I Kompromisno Resenje) is applied to rank LARG performance of Iranian needles and syringes companies via qualitative group decision-making process. The results of this study are conformed to conventional selection results of needles and syringes suppliers conducted by Iran's Ministry of Health.

This work utilizes an expert-based framework for the hybrid of in-depth interviews, Fuzzy Delphi Method (FDM), Analytic Network Process (ANP), Weighted Linear Combination (WLC) to evaluate cultural tourism strategies for Fucheng Historical District in Tainan City, Taiwan. A set of indicators were proposed through in-depth interviews with experts. Then, FDM was employed to select criteria, sub-criteria. The weight-based computation through ANP demonstrated that historical and cultural value is highly prioritized in criteria, whereas cultural assets and preservation of historical buildings are significant sub-criteria. In addition, we find that “cultural and creative clusters” is a prioritized strategy that should be adopted in historic-route-based cultural tourism. Finally, WLC indicated that Route VI is the most suitable route to organize “cultural and creative clusters”. In order to call for the sustainable implementation of tourism development, the proposed modelling approach is an effective tool that provides a comprehensive understanding to develop tourism strategies.

PurposeTo strengthen the correlation analysis on risk factors of drug production safety and reduce the influence due to fuzzy judgments, a safety risk assessment method based on Grey-Analytic Network Process (G-ANP) is proposed.Design/methodology/approachFirst, an index system evaluating drug production safety risk is constructed according to the “Good Manufacture Practice of Medical Products,” next the influence weight of each risk index is derived by using the Analytic Network Process, then the grey number of each risk index is determined by further utilizing the grey statistical theory, and finally the risk level of drug production is obtained.FindingsAn empirical study is conducted and the results support the feasibility and practicability to use G-ANP method for drug production safety risk evaluation. The results of the case show that it is feasible and practical to use G-ANP method for drug production safety risk evaluation.Originality/valueThe innovation lies in the use of G-ANP method to fully consider the interdependence and interaction between the risk factors of drug production safety, which improves the objectivity in judging the risk level of drug production and provides a scientific basis for pharmaceutical manufacturers to formulate further decisions and management in the case of insufficient quantification of risk factors. Based on the findings, more targeted suggestions are made to reduce the production risk of pharmaceutical enterprises.

Driven by the development of global Energy Internet and the requirement of energy transition, the sharing economy mode in energy sector has turned into a novel inspiration. This paper aims to explore whether the sharing economy model advance China’s energy transition effectively. We describe the system of Energy with Uber-like mode, and introduce the Uber-like Mode into the PV power sector. By using the Analytic Network Process method, a set of comprehensive benefit evaluation system for the Uber-like mode plus renewables project is put forward from aspects of the technical and economic benefit, the social and environmental benefit, the energy benefit and the safety (reliability) benefit. The Uber-like mode plus PV project, as one specific application, is chosen as a case study to verify the effectiveness of the evaluation model. Based on the evaluation results, SWOT is adopted to discuss the internal conditions and the external environment for Renewables Sharing Economy in China. Finally, this article puts forward a series of suggestions to realize the goal of the proper adjustment of energy structure with respect to the renewables’ sharing economy mode.

China has embarked on a new round of power market reforms since 2015. Traditional power generation groups (TPGGs), as important practitioners of the electricity market, have taken various actions to adapt to the new market environment during recent years. Trading performance, the most manifestation of enterprise operation is evaluated in this paper, to help TPGGs explore transition strategies to improve business and achieve a sustainable development. Considering multiple factors influencing TPGG trading performance, a hybrid framework based on Multiple-Criteria Decision-Making (MCDM) under fuzzy rules is proposed. Meanwhile, to deal with the ambiguous language in the evaluation process, the fuzzy Delphi method is combined with triangular fuzzy numbers (TFNs). Thus, twelve key indicators are selected, and the integrated evaluation index system is established. Then, a novel method combining Analytic Network Process (ANP) and Matter-Element Extension Cloud model (MEECM) is applied to evaluate the performance, which cover both fuzziness and randomness of the problem. In addition, an optimized cloud entropy algorithm considering the “3En” and “50% relevance” is implemented in MEECM. At further, through empirical analysis of five power generation companies in China, the proposed evaluation framework is verified. The results show that company A is the best, and more attention should be paid to the criteria related to market and support technology research (Z1), as well as trading capability and behavior (Z2). Finally, a series of sensitivity analyses have been conducted to examine the validity and effectiveness of the established evaluation framework and results. Meanwhile, some suggestions are put forward to improve trading performance for TPGGs. The study improves traditional cloud model and MCDM techniques. Therefore, it can help TPGGs track their course of actions during market reforms, and then take steps to enhance their management capabilities. On the other hand, it provides an effective means for market management agencies to evaluate market conditions through physical performance, to achieve a sustainable development of the power industry.

Purpose The purpose of this paper is to prioritize and establish relationships among the barriers that affect green, lean and Six Sigma (GLSS) implementation in the Indian construction sector. Design/methodology/approach A hierarchal model consisting of several levels is generated by the interpretive structural modelling (ISM) methodology. For establishing the priority weights and the ranking of the barriers, the relationships among barriers from the model in ISM are used to provide an output from the analytic network process (ANP). The 12 vital barriers that affect implementation of GLSS adoption were shortlisted from literature and then finalized in consultation with experts belonging from both industry and academia. Findings Based on the ISM model “Lack of awareness for green products, Lack of top management commitment and involvement as well as Lack of funds along with an improper estimation” are at the highest level. Similar results were found while ranking the barriers through ISM–ANP integration. Originality/value This study identified and prioritized the barriers that affect GLSS implementation using ISM–ANP approach, such a study has not been attempted previously for the construction sector. The ISM model and ANP ranks are based on the inputs gathered from experts and academicians so as to ensure practical validity. This approach is assists decision-makers to focus on the key barriers priority basis and enables them to implement GLSS smoothly.

One of the serious supply chain management (SCM) challenges is the effective selection of suppliers and the right allocation of orders to achieve an SCM's suitable financial and technical status. This paper introduced a multi-objective linear programming model to prioritize and weigh suppliers using the modified best–worst method (BWM). The proposed model uses evaluation criteria and used fuzzy variables to determine the right numbers of suppliers as well as order quantity of raw materials each supplier should provide. The model has been made up of four objective functions, and the required constraints have been solved using the goal programming method. The proposed model can take a set of opposing goals into account and prioritize the goals to maximize access to each goal. The uncertain concepts such as fuzzy and rough theories in some criteria enabled the proposed model to use the imprecise information in the best possible manner. This study has solved an example using the proposed model through analytic network process and BWM. Then, the obtained results were compared with our findings for adopting interval-valued fuzzy-rough numbers BWM (IVFRN-BWM) and it was shown that the modified BWM approach generates lower costs and better criteria.

Biometrics has been developing for decades in diverse industries, such as consumer electronics, internet of things, financial industry, etc. The purpose of this research is to build a decision-making model to evaluate and improve the performances of biometrics for administrators to design and make suitable biometric systems. This paper adopts a hybrid multiple criteria decision making (MCDM) model, comprising decision-making trial and evaluation laboratory (DEMATEL), and DEMATEL-based analytic network process (called DANP) to probe into the interrelationship and influential weights among criteria of biometrics. According to DEMATEL technique, the empirical results indicate that criteria of biometrics have self-effect relationships. The dimension of biometrics that administrators of biometrics should enhance first when improving the performances is usability. The criterion of universality with the highest influencing value to systematically affect all other evaluation factors is what administrators of biometrics should comprehensively consider. In the top three criteria for evaluation by DANP, biometric systems with the most influential weight is the criterion that can be improved to have higher recognition rates for increasing the performances of biometrics, followed by biometric conditions and permanence.

The current research intends to design and develop the simulation-based multi-criteria optimization (MCO) of the indirect-expansion solar-assisted heat pump (IX-SAHP) used to supply sustainable warm water for buildings. The developed IX-SAHP is verified and its performance is studied in the temperate climate of Iran every month of the year. The parametric analysis (PA) is performed using one-variable-at-a-time (OVAT) approach to analyze the impacts of input variables of the IX-SAHP on the system performance. Furthermore, the single and multi-objective particle swarm optimizations (MOPSO) are integrated with the designed IX-SAHP to maximize the coefficient of performance (COP) and the solar collector efficiency (SCE), independently and concurrently to study the trade-off in the COP and SCE of the system, to achieve the optimal non-dominated solutions in the form of Prato front, and to define the optimal configuration and operating parameters of the system. Eventually, the final optimum configuration is chosen through the Analytic Network Process (ANP) multiple-criteria decision-making (MCDM) method integrated with the MOPSO algorithm using MatLab coupling functions. The comparison of achieved optimum solutions indicates that the MCO scheme provides better comprehensive performance, more appropriate and reliable solutions in comparison with the single-criterion optimization (SCO) schemes. The results of the MCO process indicate that whereas the SCE relatively lessens from 52% to 47%, the COP enhances considerably from 3 to 7 in comparison with the baseline design so that the total operating hour of the optimized IX-SAHP lessens close to 282 h throughout the year that leads to considerable lessening of the total power consumption of the system. This study demonstrates the importance of MCO, OVAT, and MCDMs in the design stages of energy systems to maximize the performance and minimize power consumption of the system.

Purpose This paper investigates the interview examination applied during the admission process of air traffic controller (ATCO) candidates. This paper aims to select the most appropriate candidates by minimizing any shortcomings of the process. Design/methodology/approach ATCOs have a very important role in the air traffic system. They are responsible for ensuring the safe, regular and rapid flow of aircraft traffic. They carry out this challenging task by monitoring the aircraft under their responsibility and by giving instructions to the pilots when necessary. So the selection of ATCO candidates is of critical importance. This process is usually conducted through multistage examinations. It is a critical issue to use correct methods in the selection process to identify the most suitable candidates. Besides, the application of subjective examination in a standard way and standardization of criteria can assist in selecting the right candidates. Within this context, the analytic network process (ANP) and the rating method are used. The weights of the selection criteria are calculated by the ANP, and the candidates are evaluated by the rating method according to the defined criteria. Findings 39 candidates were ranked using the ANP, and the rankings obtained by the ANP and the current system were compared. The ANP rankings were also compared with the results of a previous study conducted by analytic hierarchy process (AHP). The results indicate that using the ANP yields consistent results with those obtained from the AHP in terms of the rankings. Research limitations/implications ATCOs are one of the most important operators in air transport system. They undertake critical tasks where even a small error may cause serious consequences. Therefore, selecting the appropriate candidate is an important first step in training qualified staff. The authors believe that this study will contribute to the training of more qualified ATCOs. On the other hand, the approach about the use of ANP method on the interview examination and the standardization of the criteria can provide insight into improving interview process in different area. Originality/value To the best of the authors’ knowledge, this is the first study that presents an ANP-based methodology for the selection process of ATCOs and the interview process.

The indivisibility principle of the 2030 Agenda is considered key for the implementation of policies in pursuit of the Sustainable Development Goals (SDGs). Therefore, science is not only asked to develop new methods for assessing SDG target interactions but also to translate findings of methodological insights into policy advice for easy take-up by policymakers. The present paper demonstrates how to adopt the multi-criteria analysis technique Analytic Network Process (ANP) for prioritizing SDG targets in considering all positive and possible indirect SDG target interactions at once. The application of the ANP is linked to a multi-method setting embracing positive scores derived from the analytical methods Nilsson-scale, a cross-impact matrix, and network analysis techniques. This supports the prioritization of SDG targets when considering n-order neighbours in a network with respect to their synergies. The ANP allows evaluating the synergistic potential and progress controllability of SDG target rankings calculated by CI-matrix metrics and thus provides conclusions on the importance of n-order interactions of SDG targets in a network for the final ranking. We showed that the application of a combination of different analytical methods improves the overall quality of the formulated policy advice regarding its scope and methodological profoundness. In this context, we compared the analytical methods involved with respect to their ability to formulate policy advice and finally presented a framing how to translate methodological results into concrete and applicable policy advice.

Purpose Resilient hospitals have the vital role in reducing mortality, severity of injuries by providing required emergency services during accidents and disasters. This study aims to identify and prioritize key indicators on hospital resilience. Design/methodology/approach This cross-sectional study was conducted in 2019. The draft of the indicators obtained from the systematic review of the previous study was finalized, with three expert panel sessions and 14 experts in resilience fields. The outputs of these sessions were divided into three domains including constructive resilience, infrastructural resilience and administrative resilience, 17 sub-domains and 71 indicators. Then fuzzy analytic network process method was used to weight and prioritize the final indicators of hospital disaster resilience. Findings Administrative resilience, logistic and financial management and strategic outsourcing agreement allocated the highest weight as domain, sub-domains and indicators, respectively. The weight of each sub-domain and indicator was also determined. Originality/value Investigating the weight of domains, sub-domains and indicators shows the importance of managerial and operational issues in hospital resilience. By using the indicators and relative weights, a tool for measuring hospital disaster resilience can be created in further studies. The output of these assessments is effective in promoting safety and increasing awareness of hospital managers and health policymakers.

With the widespread use of computers, in the design world, rule-based design methods, which can work in harmony with computer logic and can be easily adapted to the computer environment, have come to the fore. It is possible to analyze the logic of a design made with the rule-based design method. It is even possible to change the design which is placed on the rule base by removing the rules, in the computer environment, or creating new designs with the same design logic. In this paper, a new hybrid model is proposed based on Interval Type-2 Fuzzy Analytic Network Process (IT2 FANP) and Interval Type-2 Fuzzy TOPSIS (IT2 Fuzzy TOPSIS) for the evaluation of store plan alternatives produced with rule-based design method. The IT2 FANP method is used to determine the weight of criteria determined by experts in the selection of store plan alternatives. Then, the IT2 Fuzzy TOPSIS method is used to obtain the ranking of alternatives with Interval Type-2 trapezoidal fuzzy numbers. The originality of the paper comes from the first-time usage of a hybrid approach based on IT2 FANP and IT2 Fuzzy TOPSIS methodology in the prioritizing of store plan alternatives produced with rule-based design method.

All multi-criteria decision making (MCDM) techniques used with discrete event simulation (DES) in previous literature in the analysis of healthcare systems assume that performance measures are independent, while in fact many are correlated. In this study, correlations between performance measures are taken into consideration using modified TOPSIS. A framework is developed in which DES is used to model multiple experiments and factor levels to estimate various performance measures. Design of experiments is used to study the effects of factors and their interactions. Experiments are input to modified TOPSIS as alternatives to be compared based on multiple correlated performance measures. A case study is presented at Educational Dentistry Clinics in Jordan. This study was designed to improve overall clinic performance. Results show that when using appropriate appointment scheduling rules and orthodontist working schedules, and exploiting the resources effectively, the best scenario obtained shows an effective decrease in orthodontic patient waiting times and length of stay (LOS), and primary diagnosis patients LOS by 29.44%, 37.74% and 7.12%, respectively. It also shows an increase in orthodontist and receptionist utilizations, and number of patients served by 11.22%, 65.82% and 69.39%, respectively. The results obtained from modified TOPSIS were more accurate as compared to traditional TOPSIS. As a future work, other appointment rules can be tested and other MCDM techniques that take criteria correlations into consideration can be used to rank the experiments such as the analytic network process.

In recent years, cloud computing is becoming an attractive research topic for its emerging issues and challenges. Not only in research but also the enterprises are rapidly adopting cloud computing because of its numerous profitable services. Cloud computing provides a variety of quality of services (QoSs) and allows its users to access these services in the form of infrastructure, platform and software on a subscription basis. However, due to its flexible nature and huge benefits, the demand for cloud computing is rising day by day. As a circumstance, many cloud service providers (CSPs) have been providing services in the cloud market. Therefore, it becomes significantly cumbersome for cloud users to select an appropriate CSP, especially considering various QoS criteria. This paper presents a hybrid multi-criteria decision-making (H-MCDM) algorithm to find a solution by considering different conflicting QoS criteria. The proposed algorithm takes advantage of two well-known MCDM algorithms, namely analytic network process (ANP) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), to select the best CSP or alternative. Here, ANP is used to categorize the criteria into subnets and finds the local rank of the CSPs in each subnet, followed by VIKOR, to find the global rank of the CSPs. H-MCDM considers both beneficial and non-beneficial criteria and finds the CSP that holds the maximum and minimum values of these criteria, respectively. We demonstrate the performance of H-MCDM using a real-life test case (case study) and compare the results to show the efficacy. Finally, we perform a sensitivity analysis to show the robustness and stability of our algorithm.

All parties are aware that teacher performance is directly proportional to improving the quality of education. Not a few teachers work under predetermined work standards, conditions like this are caused by low work enthusiasm which results in decreased performance. If we observer the passion of work in the form of sine graph which one day will meet a saturation point if there are no preventive and curative efforts eithers form himself or guidance form his superior. One of the efforts taken is to impose teacher performance assessment to ensure a quality learning process at levels of education. Teacher performance appraisal needs to be carried out so that the functions and duties in the functional teacher positions and duties in the functional teacher positions are carried out by the applicable rules and code of ethics. On that basis, a decision support system was created using the Analytical Network Process method which can determine teacher performance improvement strategies, based on objective performance appraisals and make decisions that become more efficient.

Wetlands play an important role in the life of the planet. In order to preserve and revive the wetlands, decision-makers need to employ appropriate approaches and tools to identify the main driving forces and this could be an important step toward adopting effective strategies and making critical decisions. The current study used the combined analytic network process with driving forces-pressures-state-impacts-responses framework in mulino decision support system. The Parishan wetland, located in Iran, has been selected as the case study. After an initial study of the conditions of Parishan wetland and identifying the stakeholders, the opinions of experts and local residents were gathered to identify the most important parameters influencing the conditions of the wetland. The analytic network process model was used to improve and reduce the number of input parameters to mulino decision support system. Then, the most important and the best responses associated with the management of Parishan wetland were identified through questionnaires and imported into mulino decision support system along with other components. Finally, the mulino decision support system decision algorithm was run. The findings revealed that reduced precipitation, land-use change, competition of farmers in the wetland to use water resources, and increased temperature were the main driving forces. Moreover, water transfer from the Nargesi Dam was chosen as the most effective response. The results suggest that the driving forces-pressures-state-impacts-responses framework in mulino decision support system algorithm combined with analytic network process model can be used as an integrated tool to study and manage wetland socio-ecological ecosystems.

Driven by the development of global Energy Internet and the requirement of energy transition, the sharing economy mode in energy sector has turned into a novel inspiration. This study aims to analyze an innovative form as a new attempt of sharing economy in China’s renewables. The Analytic Network Process method is used as an exploratory approach. A feasibility evaluation system for the Uber-like mode plus renewables project is established from the aspects of technical-economic feasibility, social-environmental feasibility, energy feasibility and reliability feasibility. The Uber-like mode plus wind project, as one specific application, is chosen as a case study to verify the effectiveness of the evaluation model. Based on the evaluation results, SWOT is adopted to discuss the internal conditions and the external environment for Renewables’ Sharing Economy in China. The results show that: (1) The Energy with Uber-like Mode is theoretically feasible, and it can be a prime example of the “Energy Internet and Sharing Economy” scheme. (2) The feasibility evaluation system can be applied to regulate the key problems in the Renewables’ Sharing Economy. (3) Analyzed by the SWOT, the unfavourable factors should be paid more attention to the Uber-like mode plus renewables project than the favorable factors.

Finding a boarding house is usually done manually or by visiting the boarding house in person. Several choices of boarding houses make boarding house seekers have to make choices according to the desired criteria, so it takes quite a long time. A decision support system is a system that can be used to help make decisions based on existing criteria for determining several alternatives to be selected. The methods used in this research are the Analytic Network Process (ANP) and the Fuzzy Logic method. This study employed several criteria in providing recommendations, including distance, price, facilities, security, number of spaces, parking space and convenience. The weighting of these criteria used the fuzzy logic method based on the priority scale determined by the boarding house seekers. This system has provided a recommendation for boarding houses based on the results of the calculation process using the ANP method and weighting using fuzzy logic. The result of calculations shows that the highest value was obtained by Munawar kos (boarding house) with a value of 6.55% and followed by Diding kos with a value of 6.52%.

PurposeDecisions taken during the early design of adaptive façades involving kinetic, active and responsive envelope for complex commercial buildings have a substantial effect on inclusive building functioning and the comfort level of inhabitants. This study aims to present the application of an analytic network process (ANP) model indicating the order of priority for high performance criteria that must be taken into account in the assessment of the performance of adaptive façade systems for complex commercial buildings.Design/methodology/approachThe nominal group technique (NGT) stimulating and refining group judgments are used to find and categorize relevant high performance attributes of the adaptive façade systems and their relative pair-wise significance scores. An ANP model is applied to prioritize these high performance objectives and criteria for the adaptive façade systems.FindingsEmbodied energy and CO2 emission, sustainability, energy saving, daylight and operation maintenance were as the most likely and crucial high performance criteria. The criteria and the weights presented in this study could be used as guidelines for evaluating the performance of adaptive façade systems for commercial buildings in planning and design phases.Practical implicationsThis research primarily provides the required actions and evaluations for design managers in accomplishing a high performance adaptive façade system, with the support of an ANP method. Before beginning the adaptive façade system of a building design process, the design manager must determine the significance of each of these attributes as high performance primacies will affect the results all through the entire design process.Originality/valueIn this research, a relatively innovative, systematic and practical approach is proposed to sustain the decision-making procedure for evaluation of the high performance criteria of adaptive façade systems in complex commercial buildings.

Hazards and disasters have always negative impacts on the way of life. Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout the world. The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin, Slovakia. In this regard, the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process (FDEMATEL-ANP), Naive Bayes (NB) classifier, and random forest (RF) classifier were considered. Initially, a landslide inventory map was produced with 2000 landslide and non-landslide points by randomly divided with a ratio of 70%:30% for training and testing, respectively. The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical, hydrological, lithological, and land cover factors. The ReliefF method was considered for determining the significance of selected conditioning factors and inclusion in the model building. Consequently, the landslide susceptibility maps (LSMs) were generated using the FDEMATEL-ANP, Naive Bayes (NB) classifier, and random forest (RF) classifier models. Finally, the area under curve (AUC) and different arithmetic evaluation were used for validating and comparing the results and models. The results revealed that random forest (RF) classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve (AUC = 0.954), lower value of mean absolute error (MAE = 0.1238) and root mean square error (RMSE = 0.2555), and higher value of Kappa index (K = 0.8435) and overall accuracy (OAC = 92.2%).

Purpose The purpose of this study is to provide a model for evaluating, prioritizing and allocating orders to suppliers in the supply chain for mega-projects. Design/methodology/approach By using an integrated model (based on fuzzy analytic network process), suppliers are selected and the appropriate amounts are allocated to them in mega-projects. Initially, a hierarchical model of the research method was introduced. Then, the results on reliability and validity analysis of research measurement tools were presented. Finally, prioritization and allocation of orders to suppliers, with a case study of Iran Mall project, was carried out using Decision Making Trial and Evaluation Laboratory (DEMATEL) and analytic network process (ANP). T-test was used to evaluate the research hypotheses. Findings The findings were examined against conventional numerical analysis techniques. Finally, implication and recommendations for future work were presented. Originality/value The originality of this work is about using multi-criteria decision-making techniques for evaluating suppliers in mega-projects.

This paper presents a method that supports a process for the generation of performance indicators for manufacturing areas within companies. The existing literature in the area of performance manage...

A permit to work (PTW) system is a formal procedure designed to control non-routine and hazardous works. However, this system by itself does not prevent incidents, and various factors contribute to its efficiency. This study aims to prioritize the indicators influencing system efficiency. To do this, indicators of the system were identified and scored by 15 safety experts. Next, priority weights of the indicators were analyzed by an analytic network process and Super Decisions version windows 2.10. Accordingly, the nine main indicators and 43 sub-indicators influencing the PTW system were ranked. The main indicators from high to low were preventive actions, training, safe procedures, emergency system, control and corrective measures, coordination, monitoring, details of the permit form and documentation, respectively. The present work helps identify the involved indicators in PTW system efficiency. Thereby, the experts can prioritize and perform measures to prevent failures in the system and decrease accidents.

The big data revolution has created data center sustainability problems, whose solutions require the consideration of environmental factors. The purpose of this study is to establish a big data center sustainability evaluation index and provide guidance for sustainable data center construction. This research formulated a big data center sustainability evaluation model that integrates multiple-criteria decision-making methods based on the analytic network process and fuzzy technique for order preference by similarity to an ideal solution (TOPSIS). Furthermore, a case study was used to examine the proposed model. The refrigeration system, layout and ventilation, data center location, data volume, and server power consumption are the five most crucial factors in determining the sustainability level of a big data center. The areas that require further development are the balancing of tasks on different IT equipment, renewable energy use, and waste heat utilization. This research provides a method or guide that can be used by managers when they build new big data centers or upgrade and optimize existing big data centers to make them more sustainable. This study is the first to assess the sustainability of a big data center according to multiple criteria decision-making methods, in which fuzzy theory is applied to evaluate the imprecise and subjective judgments of decision-makers. This study provides a systematic evaluation framework that is based on qualitative and quantitative criteria and comprises the four factors of big data level, equipment level, room level, and data center level. Big data is new oil, but it is not clean oil. It is both a vital driver of economic growth and a source of environmental damage. We need to ensure that big data centers are run in a sustainable way.

Today’s market demands the manufacturing industries to be more agile in production to produce large volumes as well as a large variety of products. The companies must be quickly responsive in order to take the competitive lead. Decision making strategies used by the companies dealt with different criteria considering that the criteria are independent of each other, whereas in real time cases, most of the criteria are inter-relational. Multi Criteria Decision Making (MCDM) methods tend to be useful in making decisions based on preferences, during circumstances of more than one contradictory criterion and thus help in approaching the ideal choice amongst options. Approaching a decision is not only to select a best alternative, but also to rank all the alternatives for resources apportionment or to pool the strengths of criteria to produce a collective significance. Analytic Network Process (ANP) does not necessitate independence among elements, unlike its competitors, making it an effective tool to prioritize criteria and make decisions among the more weighted alternatives. The paper deals with ANP as an MCDM gizmo for prioritizing key criteria for augmenting the production agility levels under four major factors Technology, Workers skills, Manufacturing Strategies and Management decisions. A structured methodology for ANP has been constructed based on Satty’s scale and pairwise comparisons performed using criteria weightages, before estimating and surfacing the sub criteria namely, Flexibility of the production process, Learning capability of workers, waste elimination and type of maintenance applied as the most pertinent factors of agility.

Offshore wind power has been an important force to promote energy transformation. To build an advanced offshore wind farm, wind turbine selection requires the decision maker to explore new relevant criteria and evaluate alternatives with respect to decision criteria with assigning importance weightings to the criteria. In this paper, we devise a novel hybrid multi-criteria decision-making (MCDM) approach for offshore wind turbine selection by reconstructing analytic network process (ANP) and entropy-weight method (EWM). Based on the assigned weightings for ANP and EWM, the best alternative can be selected. For the decision-making model, five main criteria are specifically subdivided into 22 sub-criteria. The method is then applied to deal with the quantitative and qualitative inputs and the interrelation of criteria as arising from decision-making process. The results show that the proposed method can effectively select the optimal one from four different types of alternative offshore wind turbines.

Tapai is one of the products that become the flagship and image of Bondowoso district. So, the existence of cassava is very supportive to the continuity agroindustry based on Tapai. The main purpose of this research is determining methods that can be used to select the agro-industrial development strategies. One of the decision-making methods that can be used for the selection of agro-industrial development strategies is the Analytic Network Process (ANP) method. In the formulation of strategies is also used SWOT analysis. Based on the research, a priority strategy is obtained, namely the WO strategy, namely strengthening capital for the Tapai agroindustry business and increasing promotion by conducting training.

Facing the current situation of aging society, the health of the elderly has once again become an important issue related to people's livelihood. Aiming at the health problems of the elderly, with the improvement of science and technology, a series of wearable health monitoring products targeted at the elderly have gradually appeared. In order to accurately provide the elderly with a good experience in the interaction of wearable health monitoring products, this article obtains the needs of the elderly according to the research content of ergonomics, and addresses the needs of the elderly from their physical, psychological and Three angles of interaction were classified; then based on the demand, a Quality Function Deployment (QFD) method was used to build a house of quality model, which transformed the needs of the elderly into design requirements, and combined with Analytic Network Process (ANP) to build a model evaluation index system. Combining the house of quality model with the network analysis method, so that the advantages of the ANP method can be fully utilized, the shortcomings of the QFD method can be overcome, and the degree of interaction between factors in the index system can be better reflected, so that the attribute weights given are more reasonable. Finally, a fuzzy QFDANP method model for the selection and modification of such product design schemes is constructed, which not only avoids the subjective and unreasonable factors of the initial requirements acquisition of the product design, but also can target the applicability requirements of the product functions and structure Modifications provide clear goals for the design and selection of design options for such products. The validity and practicability of the method are verified by examples.

PurposeSupply chain efficiency can be enhanced by integrating the activities in supply chain through digitalization. Advancements in digital technologies has facilitated in designing robust and dynamic supply chain by bringing in efficiency, transparency and reduction in lead times. This research tries to identify and prioritize the customer requirements and design requirements for effective integration of supply chain through digitalization.Design/methodology/approachThe key nine customer requirements and 16 design requirements applicable for an electronics company were shortlisted in consultation with the experts from the company and academia. An integrated analytic network process (ANP) and quality function deployment (QFD) methodology has been applied for prioritizing the customer and design requirements. The relative importance and interdependence of these requirements were identified and a House of Quality (HOQ) is constructed.FindingsThe HOQ constructed has prioritized and identified interrelationships among customer requirements and design requirements for effective supply chain digitalization. These findings could be effectively used by managers for planning the objectives on long-term, medium-term and short-term basis.Originality/valueThis study tries to bridge the gap of identifying and prioritizing the design and customer requirements for effective supply chain integration through digitalization. The results could aid practicing managers and academicians in decision-making on supply chain digitalization process.

Higher Education is a level of education after High School which selects new students based on achievement, report cards, and tests. Admission selection was based on report cards. Number of indicators and who register make it difficult for determine which students are accepted in education. Multi criteria Group Decision Making (MCGDM) is decision-making method to determine best alternative from a number of alternatives based on certain criteria. In this study, MCGDM used is Analytic Network Process (ANP) and Elimination and Choice Expression Reality (ELECTRE). ANP model is a development of AHP and requires linkages between criteria using a network. ELECTRE is method based concept of ranking through pairwise comparisons between alternatives on the appropriate criteria. Contribution is integration ANP and ELECTRE methods based on group, by determining decisions based on consistency ratio. The results of testing level consistency ratio, group-based ANP-ELECTRE can be applied to assessment selection at Electrical Engineering with highest accuracy of 86.36%.

Due to a lack of assets and high improbability, developing an information system for every disaster situation is a challenging task. Consequently, building an information system for such a situation is essential, and the latest research direction has emphasized on development of such a system which varies due to the irregular nature of environments. This work emphasizes the data mining technique based on available disaster data or the earlier prediction of such occurrences to combat damages. The data mining technique is applied to the clustering of data for smooth processes of the obtained data. K-means clustering and analytic network process (ANP) are implemented as unsupervised learning for initial data and to find groups in the data, clustered based on feature similarity. The proposed approach implies an effective tool for predicting impacts in terms of hazards and this paper also evaluates its effectiveness. This study offers important insights into the disaster recovery practitioner to select the best disaster recovery solution and prioritize them for their enterprise.

PurposeThe purpose of the paper is to visualize the priorities of important factors towards the status of Industry 4.0 implementation in Indian manufacturing industries by utilizing the analytic hierarchy process (AHP) and analytic network process (ANP) techniques.Design/methodology/approachBased on a review of relevant literature, the authors recognized four criteria and 16 critical factors that have been validated by academic and industry experts. AHP and ANP models are then developed to evaluate the requirements and essential elements of the Industry 4.0 implementation. The outcomes were validated with the aid of sensitivity analysis.FindingsThe above models offer the industry a clear and straightforward way to understand the critical factors in the status of Industry 4.0 implementation. The two techniques have emerged to be influential in deciding the status of Industry 4.0 implementation. The two methods yielded almost identical outcomes. Both methods take into account the industry's specific needs based on their available capacity.Practical implicationsDecision-makers and specialists will use the strategies discussed in this paper to effectively include the implementation status of Industry 4.0 in their industries, based on their ability to make arrangements for proper implementation of Industry 4.0 and to concentrate on top priority factors when implementing Industry 4.0 at their workplace.Originality/valueThe contribution of this research is that it is the first to be carried out with a view to both AHP and ANP to analyze important factors regarding the implementation status of Industry 4.0 and authentication through sensitivity analysis methods; this is a recent initiative in Industry 4.0.

The hesitant fuzzy preference relation (HFPR) and the hesitant multiplicative preference relation (HMPR) are emerging techniques in decision-making research field. Clearly, an intuitionistic idea to improve the above preference relations is to synthesize the HFPR and the HMPR, which is also a primary contribution of this paper. Based on which, we propose the generalized HMPR (GHMPR) with full desired properties using transformation formulas that combine HFPR with HMPR and convert the HMPR into the GHMPR. Then, the GHMPR not only considers different risk appetites of a decision maker, but also preserves the advantages of both. Moreover, we design the corresponding consistency check algorithms, weight calculation methods and convergence algorithms for the GHMPR. Based on these new methods, we further propose a qualitative decision-making method under the GHMPR environment named as the analytic risk-network process (ARNP). Saaty’s well-known analytic network process case is then utilized as an illustrated example to verify the feasibility of the proposed methods via comparisons. The simulation results show our proposed methods are effective and useful.

Water pipelines are exposed to severe aging and deterioration challenges. The weights of importance of deterioration factors shall be estimated to predict the condition of underground water pipelines. Analytic network process (ANP) is one of the most common multiple criteria decision making techniques. The fuzzy set theory has been employed to tackle the vagueness and imprecision in human judgments. Accordingly, the overarching aim of this research is computing the weights of importance of these factors using fuzzy ANP (FANP). Chang’s extent analysis method is one of the most frequently used algorithms in FANP. For group decision making, two aggregation approaches are considered, namely geometric mean (GEO) and minimum-maximum (MIN-MAX). The performance of the modified extent analysis method is evaluated against the original method using four evaluation measures. These measures are satisfactory index, group minimum violations, group Euclidean distance, and distance between weights. Most of the evaluation measures indicate that the modified extent analysis (MIN-MAX) method exhibits better results than other methods. A water distribution network in Shaker Al-Bahery, Egypt is used to demonstrate the application of the proposed model.

Multi-criteria decision-making is one of the techniques in determining a choice of several alternative options. There are several methods in making decisions, one of which is ANP (Analytic Network Process). ANP is a development of AHP (Analytic Hierarchy Process) method. AHP is a hierarchical method in which any existing criteria will be compared first which will produce an ordinary matrix. While ANP is a network method that compares all things including criteria and alternatives that exist so as to obtain a super matrix is very complex. Currently ANP method is not only calculated but already there is software support one of them is Super Decisions. The software can be used to generate a decision by the ANP method. Every software must have advantages and disadvantages. That's why there is a need for software development that has been there before to appear new software better.

PurposeHumanitarian supply chain (HSC) has been constantly challenged with multiple issues due to the complex dynamics of the disaster. These issues are inevitable and interrelated. The issues produce undesirable cascading effects that make performance measurement complicated. This paper aims to identify and model the critical barriers in the HSC.Design/methodology/approachThe study identifies 17 barriers through the relevant literature and interviews with the stakeholders related to humanitarian organization (HO) in the developing economy. The barriers have been identified from strategic, technological, organizational, economic and operational point of view. Further, the barriers were ranked based on the degree of influence using the grey analytic network process (ANP) approach. The interrelationship among the influential barriers is established through modified total interpretive structural modelling (mTISM). To analyse and demonstrate the iterative consensus among the stakeholders, kappa statistics was adopted.FindingsThe barriers have been ranked to determine their extent of influence and modelled to reveal the interrelationship among them. The issues concerned with skills of personnel are the most influential barrier. Other three critical barriers identified are: chaotic operative environment, conflicting objectives of HO and lack of funding for information technology (IT) infrastructure. Appropriate funds need to be allocated towards IT infrastructure and personnel training.Originality/valueBoth quantitative and qualitative performance measurement frameworks had been proposed earlier for HSC. However, limited literature addresses the implementation issues with the available frameworks. This study advances the knowledge on performance measurement barriers of HSC and develops a functional description to identify the critical role of performance measurement in HOs. The study proposes a new version of the interpretive structure modelling, using mTISM technique, to determine the contextual interactions between various HSC performance measurement barriers.

With quality function deployment (QFD) as its basis, the product design team is instrumental in promoting sustainability by incorporating relevant requirements in the early stages of the design process. The domain literature, however, contains significant gaps, especially for food products. First, the current literature fails to consider the requirements of all relevant stakeholders, which are crucial to sustainability. Second, while some offer various models of fuzzy QFD – multiple-attribute decision-making (QFD-MADM), they fail to comprehensively address the underlying interdependencies of decision parameters in the QFD. Furthermore, the majority of the works on QFD-MADM limit themselves to product planning while losing control over other subsequent phases of product development. Thus, this work attempts to advance these gaps by proposing an integrated multiphase fuzzy QFD-MADM framework that combines QFD, analytic hierarchy process (AHP), decision-making trial and evaluation laboratory (DEMATEL), and analytic network process (ANP) along with fuzzy set theory. A case study in a Philippine meat processing industry was implemented to demonstrate the proposed approach. The results of the case study show the crucial decision parameters for all phases, which would serve as inputs to design teams. Unlike previous models, the proposed framework preserves the transition of the priorities flow along with all four phases of product development. Thus, the stakeholder requirements are integrated into all product development stages, which is a strong indication that these requirements are addressed in each phase. Also, the proposed framework ensures that the uncertainty and the underlying complexities of interdependencies among decision parameters of the four phases of product design and development are addressed. The proposed framework contributes to sustainable product design literature in a manner that is comprehensive and analytical.

Internet of Things (IoT) played a significant role in Healthcare. One of the major challenges in IoT health is security. For this reason, regarding the past studies, there is a lack of a comprehensive security framework that encompasses all layers of IoT. This article offers a security framework with four layers of sensor, network, service, and application. These layers help to develop security mechanisms within the proposed framework by providing security solutions. Also, security improvement has been influenced by the Multi Criteria Decision Making (MCDM) technique. To evaluate this framework, a conceptual model was defined with six critical criteria including security, network, services, interoperability, privacy, and reliability. The validity and reliability of this conceptual model were analyzed using SPSS 24 and AMOS 18. After proving the important criteria in the proposed model, we used the MCDM process of fuzzy analytic network process to prioritize the important criteria. The evaluation results of the conceptual model in the proposed framework, after analyzing the routes, show the validity of the proposed criteria. Therefore, in this model, 8 out of 12 communication routes between the criteria have a positive and significant relationship and 4 routes do not. The results of prioritizing important criteria in the research model, using analytic network process method, show that the network criterion, and authentication and validation sub-criteria have the highest priority among the proposed criteria. Thus, it can be decided that security solutions in the network layer and authentication and validation sub-criteria help to develop the proposed security framework.

To make a reasonable reliability improvement strategy for CNC equipment, it is necessary to clearly understand the fault relation characteristics and the weak links of reliability. While the fault complexity of CNC equipment makes the fault relation characteristics unclear, it is very difficult to determine the weak link of reliability. Therefore, a fault analysis method for CNC equipment based on decision-making and trial evaluation laboratory (DEMATEL)/interpretive structure modeling (ISM)/analytic network process (ANP) is proposed in this study to describe the relationship between CNC equipment faults accurately and identify reliability weaknesses. First, the fault mechanism was analyzed by the fault phenomenon, and the fault factors of the CNC equipment were identified in view of the system. Second, DEMATEL/ISM/ANP method was adopted to integrate the interaction direction and intensity between the failure factors, hierarchical structure of the factors, and their relative importance, and a hierarchical network structure model for CNC equipment fault factors was built. Finally, according to the analysis results of the aforementioned model, the fault factors of the CNC equipment were divided into drive, link, dependence, and independent fault factors, and the identification of the weak link of reliability was completed. Results show that the proposed method can quantify the fault factors of the CNC equipment and clearly identify the main direction of its reliability improvement.

The concept of efficiency has always been and will continue to be important for competitive business environments where limited resources exist. Owing to the growing complexity of organizations and economy in general, this trend is expected to continue to remain a high priority for organizations. Continuous performance evaluations that utilize both qualitative and quantitative information play a significant role in sustaining efficient and effective business processes. Therefore, the literature offers a wide range of performance evaluation methodologies to assess the operational efficiency in various industries. Majority of these models, however, focus solely on quantitative criteria, avoiding the interrelations and dependencies between qualitative and quantitative measurements. Furthermore, these methodologies tend to utilize discrete and contemporary information eliminating historical performance data. With these motivations, this paper proposes an integrated approach combining fuzzy decision-making trial and evaluation laboratory (DEMATEL), analytic network process (ANP), and artificial neural network (ANN) methodologies for performance evaluation. In the proposed model, DEMATEL and ANP methodologies are utilized in a group decision-making concept to obtain priorities of the evaluation criteria. Following this, an ANN model is designed and trained with historical performance data collected from the organization and the results of the fuzzy DEMATEL-ANP model. The outcomes include the relational data among the criteria and alternatives used in the model in addition to their relative rankings. A food industry case study is presented to demonstrate the steps of the proposed model.

The analytic network process framework is investigated to solve multi-criteria decision making problems in interval-valued intuitionistic fuzzy environments. First, a new approximate multiplicative consistent interval-valued intuitionistic fuzzy preference relation is defined, and the equivalent condition of approximate consistency is given by using the chance-constraint programming model of the Me measure. Second, a new method to derive the priorities from the interval-valued intuitionistic fuzzy preference relation is proposed, and the corresponding algorithm flow of the interval-valued intuitionistic fuzzy analytic network process is established. Finally, the feasibility and effectiveness of the interval-valued intuitionistic fuzzy analytic network process are verified by using a numerical example.