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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 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.

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.

The number of public-private partnership (PPP) projects has gone up especially in developing countries. The risk assessment of PPP projects is essential in ensuring project success. The objective of this study is to develop an Analytic Network Process (ANP) based risk assessment model for hydropower investments, and a tool to facilitate quantification of risk ratings based on this model. The results show that the three most important risk factors that affect the overall risk rating of a PPP hydropower investment are legal risks, contractor/subcontractor risks, and operator risks. In addition, the three most important risk clusters were identified as stakeholders, government requirements, and resources, whereas market was the least important cluster. The tool that measures the risk rating of a PPP of hydropower project was tested on ten real cases, and satisfactory results were obtained in terms of its predictive capability. The contributions of this research include (1) identification of the risk factors and clusters of factors associated with PPP hydropower investments; (2) determination of the priority of each risk factor and cluster; (3) development a tool that guides the investors through the risk assessment of PPP hydropower investments.

PurposeThis study initially aims to identify the barriers to the big data analytics (BDA) initiative and further evaluates the barriers for knowing their interrelations and priority in improving the performance of manufacturing firms.Design/methodology/approachA total of 15 barriers to BDA adoption were identified through literature review and expert opinions. Data were collected from three types of industries: automotive, machine tools and electronics manufacturers in India. The grey-decision-making trial and evaluation laboratory (DEMATEL) method was employed to explore the cause–effect relationship amongst barriers. Further, the barrier's influences were outranked and cross-validated through analytic network process (ANP).FindingsThe results showed that “lack of data storage facility”, “lack of IT infrastructure”, “lack of organisational strategy” and “uncertain about benefits and long terms usage” were most common barriers to adopt BDA practices in all three industries.Practical implicationsThe findings of the study can assist service providers, industrial managers and government organisations in understanding the barriers and subsequently evaluating interrelationships and ranks of barriers in the successful adoption of BDA in a manufacturing organisation context.Originality/valueThe paper is one of the initial efforts in evaluating the barriers to BDA in improving the performance of manufacturing firms in India.

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.

Factors like intensive industrial and socio-economic development can lead to the deterioration of the water environment. At the same time, economic, social, and population sustainability rely on good water conditions in the river. Therefore, it is important to calculate the water environment capacity and allocate it accordingly. Our research selects the economic, social, and environmental factors to construct a water environment capacity allocation model using the analytic network process. Allocation results are as follows: there are Liaozhong County, Tiexi District, and Liaoyang County, where the water environment capacity allocation value exceeds 10 000 t/a at 18 336.25, 12 743.19, and 10 585.53 t/a, respectively. Dashiqiao City’s water environmental capacity allocation is the least, at only 78.30 t/a. In the paper, the goal to allocate water environment capacity from the three major rivers to the administrative area is achieved in Liaoning Province, thus making the allocation result more comprehensive and fairer. It provides a scientific basis for controlling the water environment in Liaoning Province.

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.

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.

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.

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.

PurposeThe revitalization of big data has gained attention in the public sector. However, such open government data (OGD) is facing major challenges with respect to data quality and limited use. To solve this problem, this study analyzes the factors driving the use of OGD from the perspective of data providers in the public sector.Design/methodology/approachUsing the analytic hierarchy process and analytic network process methodologies, the importance of the factors driving the use of big data in the public sector was ranked. In addition, the different characteristics of tasks among the departments in a public agency were compared based on expert interviews.FindingsThe factors driving OGD use are not only political environment or the technological environment. The importance of the institutional culture within the organization increases with the motivation of the data provider. The priorities of the OGD factors also depend on the objectives of the department involved.Originality/valueThis study provides implications for improving the publication of open data by analyzing the priorities of the factors driving its use from the perspective of big data providers. It focuses on different perceptions of the factors valued by public officials in charge of data in institutions. The results suggest the need to explore officials' perceptions of value creation in big data fields.

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.

Delays in the implementation and commissioning of rail transportation projects can cause economic dam-ages to project stakeholders and the cities linked by such rail networks. Hence, the current study aims toinvestigate the key delay factors and provides salient recommendations. In this study, multi-criteria deci-sion-making–the Analytic Network Process (ANP) and the DEMATEL (Decision Making trial and evalu-ation laboratory) method was employed to help decision-makers in prioritizing these delay factors andevaluating their interactions, respectively. A Delphi approach was used to validate the study’s findings viaexpert questionnaire surveys–based on a rail transportation development project for the MobarakehSteel Complex (MSC). The study’s findings revealed the management factors as the most important delayfactors, followed closely by the financial, design, and implementation factors. Other potent issues includethe existence of numerous decision-making stations, lack of central role, and sufficient authority for theproject managers are responsible for project delays. Overall, the results show that resolving problemswith the‘management’domain can significantly avoid or alleviate the extent of delays in rail transporta-tion development projects. The study’s findings and recommendations can serve as a policy and consulta-tive instrument for the relevant stakeholders in the railway industry.

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 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.

Although a lot of research has focused on energy recovery and recycling of valuable materials from wastes to reduce loads on landfills, landfilling is still an inseparable part of solid waste management. Since landfills potentially pose a risk to human health and the surrounding environment, landfill site selection is an important endeavor that has to be carried out with great care. Choosing a suitable location for landfills depends on several factors and is a complicated procedure. In this paper, a comprehensive review of landfill site selection methods, with an emphasis on multi criteria decision making (multiple criteria decision analysis) has been presented. Assessments indicate that the most frequently used methods are weighted linear combination (WLC), ordered weighted averaging (OWA), analytic hierarchy process (AHP), fuzzy analytic hierarchy process (F-AHP), TODIM, fuzzy TODIM, analytic network process (ANP), fuzzy-analytic network process (F-ANP), and grey systems theory, each of which is discussed. The most widely used factors for selecting a landfill site are groundwater depth, surface water vicinity, elevation, land slope, soil permeability, soil stability, flooding susceptibility, lithology and stratification, faults, land use type, nearby settlements and urbanization, cultural and protected site vicinity, wind direction, roads, railways, proximity to building materials, pipelines and powerlines, and proximity to airports.

Despite the great importance of designing innovative business models, a validated methodology that helps related process is still not available. To fill this gap, this research introduces business model management concepts that consider business models as individual entities that can be systematically developed and managed by utilizing science based methodology. This study mainly focuses on proposing business model evaluation and deployment framework by applying analytic network process (ANP), a multi multiple criteria decision-making method and portfolio matrix strategy. The proposed evaluation method is designed to consider both internal and external perspectives where internal perspective is measured through ANP method by employees, while external perspective is measured through customer survey by in-depth interview. The obtained values from ANP and customer survey are then used to construct the business model portfolio matrix for continuous business model management. To this end, this study identifies 12 criteria within three different categories employed in ANP analysis and business model portfolio matrix, respectively. The proposed framework for managing business models provides new insights to both practitioners and academics. It regards business models as individual entities and introduces a way to practically apply the business model concept. It also provides managers with guidelines to manage business model alternatives in terms of evaluation, selection, and continuous management. Finally, because this study is, to the best of our knowledge, one of the first attempts to employ the portfolio management concept in business model research, it can function as the foundation for future applied business model research.

A reconfigurable product serves as multiple products to deliver different functions through reconfiguration processes to change between product configurations. An optimization method was developed in our previous research to identify both the optimal design and the optimal reconfiguration processes. Because the generic design or process considering different candidates was modeled by an AND–OR tree or graph, importance weights were assigned to nodes with an AND or OR relation, such that the less-important nodes were pruned to improve the optimization efficiency. In this research, an extended analytic network process method is introduced to further improve the quality of the optimal reconfigurable product design approach when dependency relations among descriptions of design/process candidates and evaluation criteria are considered. In this method, the initial weights of the design/process nodes in the AND–OR tree or graph are adjusted based on the dependency relations such that the weights which truly reflect their contributions to the solutions are achieved. In addition, multiple evaluation criteria similar to the evaluation measures used in optimization are selected to identify the weights of the design/process nodes. A case study has been implemented to demonstrate effectiveness of the extended analytic network process for improving the quality of optimal reconfigurable product design.

PurposeConstruction projects involve with various risks during all phases of project lifecycle. Failure mode and effective analysis (FMEA) is a useful tool for identifying and eliminating possible risk of failure modes (FMs) and improving the reliability and safety of systems in a broad range of industries. The traditional FMEA method applies risk priority number method (RPN) to calculate risk of FMs. RPN method cannot consider the direct and indirect interdependencies between the FMs and is not appropriate for complex system with numerous components. The purpose of this study is to propose an approach to consider interdependencies between FMs and also using fuzzy theory to consider uncertainties in experts' judgments.Design/methodology/approachThe proposed approach consist of three stages: the first stage of hybrid model used fuzzy FMEA method to identify the failure mode risks and derive the RPN values. The second stage applied Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) method to determine the interdependencies between the FMs which are defined through fuzzy FMEA. Then, analytic network process (ANP) is applied in the third stage to calculate the weights of FMs based on the interdependencies that are generated through FDEMATEL method. Finally, weight of FMs through fuzzy FMEA and FDEMATEL–ANP are multiplied to generate the final weights for prioritization. Afterward, a case study for a commercial building project is introduced to illustrate proficiency of model.FindingsThe results showed that the suggested approach could reveal the important FMs and specify the interdependencies between them successfully. Overall, the suggested model can be considered as an efficient hybrid FMEA approach for risk prioritization.Originality/valueThe originality of approach comes from its ability to consider interdependencies between FMs and uncertainties of experts' judgments.

Failure of software systems as a result of software testing is very much rampant as modern software systems are large and complex. Software testing which is an integral part of the software development life cycle (SDLC), consumes both human and capital resources. As such, software defect prediction (SDP) mechanisms are deployed to strengthen the software testing phase in SDLC by predicting defect prone modules or components in software systems. Machine learning models are used for developing the SDP models with great successes achieved. Moreover, some studies have highlighted that a combination of machine learning models as a form of an ensemble is better than single SDP models in terms of prediction accuracy. However, the efficiency of machine learning models can change with diverse predictive evaluation metrics. Thus, more studies are needed to establish the effectiveness of ensemble SDP models over single SDP models. This study proposes the deployment of Multi-Criteria Decision Method (MCDM) techniques to rank machine learning models. Analytic Network Process (ANP) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) which are types of MCDM techniques are deployed on 9 machine learning models with 11 performance evaluation metrics and 11 software defects datasets. The experimental results showed that ensemble SDP models are best appropriate SDP models as Boosted SMO and Boosted PART ranked highest for each of the MCDM techniques. Besides, the experimental results also validated the stand of not considering accuracy as the only performance evaluation metrics for SDP models. Conclusively, more performance metrics other than predictive accuracy should be considered when ranking and evaluating machine learning models. Keywords: Ensemble; Multi-Criteria Decision Method; Software Defect Prediction

PurposeIn the automotive industry, the high process complexity becomes an important issue because of the increased number of product and process variants demanded by the customers. To avoid quality defects in assembly and losses in such a complex manufacturing environment, new predictive support systems are required. This study aims to develop a multiple attribute decision support system (DSS) for the prediction and quantification of the risk of failures on the workstations of a leading Turkish automotive manufacturing company.Design/methodology/approachInitially, the factors affecting the failures in workstations and the attributes to evaluate the factors are identified. Subsequently, the relations among the attributes are specified and priorities of them are calculated. Finally, the risk of failures is calculated and tested in a pilot study and validated with real production data.FindingsTo the best of authors’ knowledge, this is a unique study that computes the risk scores on the workstations via DSS. The DSS has various advantages for improvements of the manufacturing quality: the risk of failures can be detectable and comparable, the effect of changes in the design of new workstations can be observed. Stations that have medium or high complexity scores demonstrated strong correlation with failure rates. A sensitivity analysis is conducted to predict the effect of improvement actions on the riskiness of the workstations.Originality/valueHigh level of production complexity becomes a crucial issue for companies that use various production processes. Considering this fact, it is a requirement for companies to observe and monitor the risk factors, especially in the assembly lines to be able to eliminate failures derived from complexity. Accordingly, to measure risk scores of the workstations in the assembly lines, a decision support for companies aids executives to manage the complexity level in a reliable and effective way. In this study, the authors develop such a DSS for TOFAS, a leading Turkish automotive company. The proposed DSS is verified and applied through a pilot study on a specific basic production unit. A sensitivity analysis is also conducted to see the effects of potential improvements on the risk scores. Additionally, the trend of risk scores for the stations can also give valuable information for tracing the changes in the time horizon. The proposed DSS also enables an opportunity for the executives in their decision of design processes of new production lines by allocating limited resources in an appropriate way based on the risk scores of possible workstations. The proposed DSS is the first and unique proactive failure prevention model developed in a Fiat Chrysler Automobiles (FCA) plant across the world. TOFAS executives also plan to introduce and enlarge the usage of the model to other FCA plants. It may also be possible to apply the model to other assembly lines in any sector. Another plan of the executives of TOFAS is developing a software, which manages each parameter, to constitute data to the DSS to run this system more instantly and effectively. Moreover, they can take integration actions of the software with world-class manufacturing problem management system that is currently in use in TOFAS.

This study aims to develop a strategy for developing Earthenware Craft Business Group in Kasongan Tourism Village. This research method uses mix methods, used namely primary and secondary data. The analytical method used is the Analytic Network Process (ANP) analysis. The results showed that the priority order of the first criteria was the aspect of sales with a value weight of 49.3 %%, followed by the business actor aspect with a value weight of 31%, and the aspects of business products with a value weight of 19.5%. The finding of this research is that the sales aspect is a priority aspect to be developed. From the final results the determination of priorities obtained three main priorities namely aspects of product innovation with a weight of 39.16%, partnership with a weight of 38.81% and training with a weight of 36.32%. Keywords: earthenware, business, kasongan, analytic network process Strategi Pengembangan Kelompok Usaha Kerajinan Gerabah Dengan Pendekatan Analytic Network Process Abstrak Penelitian ini bertujuan untuk menyusun strategi pengembangan kelompok usaha kerajinan gerabah di Desa Wisata Kasongan. Metode penelitian ini menggunakan pendekatan kualitatif dan kuantitatif. Jenis data yang digunakan ada dua yaitu data primer dan sekunder. Metode analisis yang digunakan adalah analisis Analytic Network Process (ANP). Hasil penelitian menunjukkan bahwa urutan prioritas dari kriteria tersebut yang pertama adalah aspek penjualan dengan bobot nilai sebesar 49,3%%, selanjutnya diikuti oleh aspek pelaku usaha dengan bobot nilai sebesar 31 %, dan aspek produk usaha dengan bobot nilai sebesar 19,5%. Temuan dari penelitian ini adalah aspek penjualan adalah aspek yang diprioritaskan untuk dikembangkan. Dari hasil akhir penentuan prioritas diperoleh tiga prioritas utama yaitu aspek inovasi produk dengan bobot 39,16%, kemitraan dengan bobot 38,81% dan pelatihan dengan bobot 36,32%. Kata kunci: gerabah, kasongan, kerajinan, pengembangan Keywords: Earthenware, Business, Kasongan, Analytic Network Process

Supplier selection can be regarded as a classic multiple criteria decision making (MCDM) problem. To a great extent, experts’ evaluations play a decisive role in the decision-making process. There will inevitable exist a variety of indefinite factors, which result from imprecision, uncertainty, and fuzziness due to the subjective judgment of human beings. As an effective tool to express uncertain information, the theory of D numbers performs better in comparison to other existing methods. In addition to that, analytic network process (ANP) method is applied more broadly for its advantages of flexibility, rationality and creditability than analytic hierarchy process (AHP) method. In this study, the D-ANP methodology is proposed to apply in the field of supplier selection, which is the extension of the traditional ANP method using D numbers. The validity of the presented methodology is illustrated by an application for supplier selection.

The decision-making process involves, most of the time, working with limited and/or incomplete information to obtain a one-time synthesis of one’s preferences. However, additional information might be acquired by the decision-maker at a later time. To identify the impact that the additional information could have on the initial result, we developed a new multi-dimensional stability analysis that provides a comprehensive image of how preferences change or evolve, as perturbations are applied to the criteria weights, without reiterating the preference elicitation process. The insights provided by the new method helped define a set of stability measures useful in a practical setting. To show the direct implementation of the findings of the multi-dimensional stability analysis, we applied the method developed to a randomly generated ANP model.

To have the software development process a fruitful one we have to consider the critical factors of success of agile development. Any organisation is expected to deliver the end product timely with required and on agreed upon terms which is many a times beaten by the extended time taken to complete the software projects or also leading to unexpected increase in the budget of the projects. So today’s development processes embed agile implementation for the accelerating growth of the company, organisation with fast competition of their work. The conventional approach of software systems succumbs towards the ever changing and much needed twist of the “Agile process” to be implemented. Agile focuses on collaborative work for the lifecycle of the project to continue iteratively with anticipating changes and introducing more flexibility than prior traditional approaches. Small sprints remain utilized which are mandatory for effective item conveyance. Conventional programming advancement forms are very little proficient to deal with the fast change in necessities. In spite of the upsides of Agile, feedback on deft approach expresses that it neglects to focus on building and configuration issues and subsequently will undoubtedly create little outline decisions. Agile programming improvement has quickly picked up a great deal of enthusiasm for the field of programming designing. Agile programming development, in spite of its curiosity, is a critical area of research inside programming building discipline. Logical research is yet rare, there has been minimal nitty gritty revealing of the use, entrance and achievement of light-footed systems in customary, proficient programming advancement organisations. In this paper we will evaluate the agile success factors for the product outcomes. The manuscript takes the approach for deciding best agile success factors influencing quality in software industry using analytic network process (ANP). This paper applies ANP to conclude the best way out using nine criteria namely Agile implementation, root cause analysis, mutation testing, cycle time, test and learn approach, continuous integration, process action, IT governance strategy, organisational change, effective risk mitigation, feedback, tangible outcomes, and two alternatives in the form Scrum and Kanban agile framework. The projected approach was tested in a organisation application in software reliability, software development environment and this paper evaluates its effectiveness by giving the weightage to the factor by realising the precise objective.

The hybridisation of conceptual design methods is presented in the development of multi-operations creep flexural test rig. The paper explains the integration of theory of inventive problem solving (TRIZ), Morphological chart, and Analytic Network Process (ANP) method to ensure the quality function is fulfilled with the essential requirements. The objective of this project is to produce and choose the best design concept for the product development depending on the product design specifications (PDS). In the early stage of product development, the TRIZ contradiction principles and 40 TRIZ inventive solutions were applied as the guidance tools. To refine the specification of the product design, the principle solution parameters were detailed systematically with the aid of morphological chart. In this article, four innovative conceptual designs of the multi-operations creep flexural test rig were produced and the selection process was conducted using ANP method to perform the multi-criteria decision making process in selecting the best concept design. Keywords: Conceptual design, TRIZ, Morphological chart, Analytic Network Process, Flexural creep, Test rig

This study identifies indicators of urban sustainability and smart cities and then integrates them into a unified concept. Its aim is to reduce the gap in the literature between sustainabl...

The issue of sustainability has been a prime concern as a continued surging amount of construction and demolition waste (CDW) is being generated while natural resources are consumed and contaminated at an alarming rate. However, CDW utilization involves a range of options based on combinations of different crushing options and manufacture of various recycled products, which is confusing for practitioners to decide the optimal CDW utilization scheme from a comprehensive perspective. To assist decision making among CDW utilization schemes, a heterogeneous multi-criteria evaluation framework is proposed where interval numbers, triangular fuzzy numbers (TFNs) and trapezoidal fuzzy numbers (TrFNs) are used for comprehensive evaluation, enabling experts to present assessment information flexibly based on their preferences, knowledge and nature of different data forms. Firstly, a comprehensive index system is constructed from social, environmental, financial and technical dimensions for the evaluation of CDW utilization schemes. Afterwards, a novel method is proposed to unify heterogeneous evaluation values into rough numbers. Thirdly, to fully reflect both inner and outer dependencies among criteria, the analytic network process (ANP) method is utilized to derive weights of criteria and sub-criteria, before the fuzzy multi-attributive border approximation area comparison (MABAC) method is applied to obtain the final ranking. Finally, a case study of CDW utilization schemes selection problem is illustrated where the scheme of on-site crushing combined with production of recycled bricks and aggregates is selected as the best option with the sum of the distance Sk between alternatives and the border approximation areas obtained as [0.0037, 0.0544], and a comparative analysis is conducted to verify its effectiveness and validity of the proposed framework.

[EN] The Analytic Multicriteria Valuation Method (AMUVAM) was designed to value environmental assets. This method and its software allow one to incorporate all the experts' decisions in a global evaluation matrix and assign a degree of importance (weight) to the criteria. This multicriteria decision aid methodology has traditionally been based on the Analytic Hierarchy Process (AHP) and the Discounted Cash Flow. However, it can be substituted by the Analytic Network Process (ANP). Although it involves a higher complexity, it considers all the current relationships between the different alternatives and criteria so it should be more accurate. Therefore, the aim of the present work is to value the ecosystem services of an environmental area by using both methods and compare the results. A real application has been presented; therefore, this work has been applied to the valuation of the Albufera Natural Park in València (Spain). This area is considered one of the most important Mediterranean wetlands of the Mediterranean countries. Having obtained the results, the method can be carried out using either of the two processes when the aim of the assessment is to get the Total Economic Value. In this case, the AHP can be used as a less time-consuming and cheaper method. However, if the goal is to value the ecosystem services, there are significant differences between both methods. Some of the services are overvalued or underestimated when the AHP has been used.

Although certain countries have proposed revitalization to address the issue of inefficient industrial parks in recent years, there is little research exploring the causes of inefficient industrial parks—although this would help in formulating improvement strategies and further resource allocation. This study presents an evaluation framework for the causes of inefficient urban industrial parks based on a novel hybrid modified multi-attribute decision-making model in real cases, which includes three dimensions and 10 criteria. Two cases of “industrial parks designated by urban planning” in Taiwan were examined. The results of the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and the DEMATEL technique based on the basic concept of the analytic network process indicated that the parcel dimension (A1) and policy dimension (A3) had more influence on causing inefficiency in industrial parks. The criteria with bigger impact were “property rights (B1),” “size and shape (B2),” “location (B3),” “planning and development mechanisms (B8),” “management mechanisms (B9),” and “spatial planning policy for industry (B10).” The modified Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) revealed that policy dimension (A3) and its criteria have relatively poor performance in two cases; solving the policy dimension is the most critical strategy for IPDUP. This presented decision-making model not only identifies the key causes of the inefficient industrial park and further improvements to it, but also provides a direction for evaluating, planning, and developing sustainable industrial parks.

Purpose The purpose of this paper is to examine the issues affecting end of life (EOL) management of flexible packaging. It focuses on Sustainable Solid Waste Management by using multi-criteria decision making, analytic network process (ANP), and Strengths, Weaknesses, Opportunities, and Threats (SWOT). Design/methodology/approach Data were collected from 33 expert stakeholders, though a series of interviews and questionnaires. The subject seven aspects were applied from integrated sustainable waste management with 19 sub-criteria identified. Criteria were prioritized by using ANP and SWOT to the internal and external environments of organizations directly responsible for waste management. Findings The five most important factors in the management of flexible packaging waste include: techniques for waste management, material and design, management support, legislation and rule, and environmental care and environmental health, respectively. Solutions addressing flexible packaging waste were identified, including reuse and recycle, waste to energy, biopolymers, new innovative materials and material recovery. Research limitations/implications Data were derived from the national authorities and large companies. The findings may not represent local authorities and small-scale manufacturers. Future research should be conducted, in order to investigate and focus around small manufacturing enterprises. Practical implications The findings provide a strategic framework for policy makers and industrial manufacturers. The benefits of this will enable them to address flexible packaging waste, by using qualitative and quantitative criteria. Originality/value This is the first paper developing a multi-criteria assessment model to specifically manage EOL flexible packaging, a possible pioneering piece of research in this field.

Analytic network process (ANP) is a significant multiple criteria decision making (MCDM) method. Although lots of efforts have been spent on improving the ANP, there still exist the four aspects of problems to be solved such as too many comparison times, unconsidering expert knowledge structure, ignoring consensus level of experts' group and ineffectively determining holistic information of the group. This study focuses on providing a simplified group ANP with consensus reaching named as DS/ANP method which incorporates the Dempster-Shafer theory of evidence (DS) with the ANP. An expert information extraction mechanism with the help of knowledge matrix and basic probability assignment function is introduced to simplify comparisons of group ANP and reflect expert knowledge structure. Then Shafer's discounting and Dempster's rule are both used to get the holistic information of the group, in which three levels of consensus indices (element level, cluster level, and holism level) are defined in terms of Jousselme distance. A group ANP method with consensus reaching and its corresponding algorithm are both established by following the principles of ANP. Finally, a numerical example is presented to make comparisons and discussions for the proposed method.

At present, China's offshore photovoltaic (PV) industry is developing rapidly. Risk assessment on offshore PV power generation (OPVPG) projects is important. The existing risk assessment methods in OPVPG projects in China are insufficient in dealing with different types of uncertainties. In this paper, we propose a computational model based on D Numbers and analytic network process (ANP) method to handle risk assessment on OPVPG projects. Firstly, factors that influencing offshore photovoltaic power generation projects in China are identified and the weights of the factors are determined by domain analyst using ANP. Secondly, risk evaluation judgment on each factor is given by expert referring to predefined anchors and linguistic labels under specific situations. Thirdly, the judgments are represented as D numbers and are integrated into a fused D number. Finally, the overall risk level of OPVPG in China is obtained based on the fused D number. The main contribution of the proposed method based on D numbers is to deal with uncertainties and the degree of confidence in experts judgments. Uncertainty includes situations like: do not know how to assign accurate beliefs to risk levels and incomplete frame of discernment. The degree of confidence shows the cognition of the experts. The situation that the confidence of expert is equal to 1 shows the certainty of experts. When the confidence of expert is equal to 0, it means that experts cannot decide the risk levels. It can be considered as a new strategy for risk assessment and enriches the framework of decision-making. Several case studies are illustrated to show the effectiveness and flexibility of the proposed method.

Purpose Universities play a central role in scientific development and progress in societies, so, the need for supporting new businesses in universities becomes very important that science and technology park-based incubators can meet it. The purpose of this paper is to prioritize the factors influencing strategic management of university business incubators (UBIs). Design/methodology/approach Present research prioritizes the factors influencing strategic management of incubators using analytic network process (ANP). The authors gathered data from UBIs affiliated with science and technology park of Guilan, located in city of Rasht (Iran) using the ANP questionnaire during year 2017. Findings Factors influencing strategic management of incubators comprised of four main criteria and 14 sub-criteria. The criteria and sub-criteria were ranked based on their importance. Results show that “talented managers” has the highest importance for strategic management of UBIs. Originality/value This study offers a novel analysis and contribution to the knowledge of ranking UBIs with a multi-criteria decision-making technique. Results of this research show the relative importance of criteria and sub-criteria, which contributes to further improvement of incubator management. University managers and incubator directors can utilize the findings for better resource allocation and aligning the strategies of incubators with macro strategies of the country.

The industrial clustering process in the chemical industry is becoming progressively more important due to economic, social and political issues. Industrial clustering means agglomeration of companies in the same geographical area in order to increase productivity and reduce costs. Nonetheless, clustering also has some important safety and security implications. The aim of this study is twofold: firstly, the development of an algorithm for the classification of chemical industrial clusters with regards to safety and security risks. Secondly, considering the importance of a multi-plant safety and security management system, highlighting the greater efficiency in the reduction of risk where adequate cooperation exists. The methodology is divided in three main steps, namely, “selection” (of the chemical parks to be processed), “assessment” of average hazard and vulnerability of installations within the cluster area, followed by an analysis of the relationships within companies in terms of strategic and operational cooperation, and “ranking”. The last step evaluates the strong influences of the above-mentioned parameters through the analytic network process (ANP) and leads to a final classification.

This paper proposes fuzzy integral-based decision methods to identify the core factors and their relationships for smart homes product improvement. The dominance-based rough set approach was used to retrieve core attributes and obtain rough knowledge-based rules. The decision-making trial and evaluation laboratory (DEMATEL) technique was used to build an influential network relationship map, and influential weights were determined through the DEMATEL-based analytic network process. Subsequently, the inter-relationships among criteria were calculated. Finally, the fuzzy integral method was used to measure the plausible synergy effects among the criteria, evaluate/rank alternatives for smart homes, and then provide suggestions for product improvement. The main innovation is the use of rough knowledge-based rule retrieval procedures and fuzzy measures for exploring the synergy effects on smart home improvement. Three smart home products/systems were examined to illustrate their performance on each criterion for improvement planning. This study contributes knowledge to research on consumer adoption of smart homes and presents improvement strategies.

Cross arm is the main component used to lift the electric cable in a transmission tower. Recently, a synthetic cross arm was proposed to substitute the wooden cross arm due to the extreme creep deformation, which can induce a structural collapse. Several creep studies were conducted on a coupon scale for the synthetic cross arm. However, there is a lack of study on the assessment of creep properties for the actual size cross arm. Thus, the development of a special rig is required to accommodate the creep test. The paper explains the development of creep testing rig for a full-scale cross arm using the integration of theory of inventive problem solving (TRIZ), morphological chart, and analytic network process (ANP). In the beginning, the finding of principle solutions was conducted using the TRIZ contradiction matrix. The characterisation of the design concepts was elaborated using the morphological chart. Finally, the ANP principle was exploited to select the best design via the pairwise comparison technique. The results show that Concept Design 5 (hybrid bracing design) scored the highest value and ranked first. Lastly, challenges on the design of creep testing machine and the improving criteria in concurrent engineering is presented. Keywords: TRIZ, ANP, Morphological chart, Creep testing rig, Full-scale cross arm

Purpose This paper aims to select and rank the solutions of knowledge management and apply them to the supply chain for boosting its performance. Therefore, organizations can focus on the high-priority solutions and assign the resources to the strategies related to these solutions. Design/methodology/approach A decision making trial and evaluation laboratory-based analytic network process method is first used to rank the barriers of implementing the knowledge solutions and preference ranking organization method for enrichment evaluations technique is then used to rank the knowledge-based solutions which improve the overall performance of the supply chain. Then, a mathematical model is proposed to select the best solutions where the effectiveness and fairness are maximized while the cost and risk are minimized. The proposed model is implemented over a dairy supply chain in Iran. The solutions are obtained using the literature and the experts’ panel of the supply chain. Findings Lack of trust and commitment of supply chain members, besides different interests, values and cultures of the supply chain members, is the most challenging barrier to knowledge management solution. Strengthening the cooperation culture in the chain, setting up a transparent workflow or open-door policy, setting up enough incentives and reward and punishment and setting up positive leadership toward knowledge management are the most important strategies for removing the existing barriers or decreasing their impact on the supply chain. Originality/value Solutions with the highest priority are not necessarily the best solutions for implementation; the requirements of organizations must also be considered. Mathematical model in this study helped to select solutions based on their cost and risk, as well as their priority level.

Photovoltaic with its main characteristics of clean and abundant reserves has been widely used. This paper investigates how to select a satisfactory industrial and commercial rooftop distributed photovoltaic (ICR-DPV) project to invest from the small and medium-sized enterprises’ (SMEs) view. Flaws and inadequacies existing in the current decision-making process may cause an inaccurate investment result. Therefore, this paper establishes a cloud-TODIM framework to deal with the problems. First, criteria covering the economy, resource, risk factors and engineering feasibility are established. Second, hesitant fuzzy linguistic term set (HFLTS) and cloud model are applied to describe the indeterminate information so that the hesitation and randomness of linguistic variables can be fully expressed. Third, the analytic network process (ANP) method and entropy method are combined to gain the criteria weights, which can not only avoid too much subjectivity in weight determination but also measure the mutual influence between the various criteria simultaneously. Furthermore, the TODIM method considers the psychological behavior of investors, so it is utilized to rank alternatives to make the framework more applicable for practical evaluation. Finally, a case in Shandong province validates the applicability of the proposed framework. This paper provides a more rational and scientific decision-making framework for investors.

In recent years, the safety situation in Chinese coal mine enterprises has been steadily improving, however, major accidents occur occasionally. Statistics reveal that human unsafe behaviors are the main cause of accidents. In this paper, based on the Fishbone Diagram, the influencing factors of unsafe behaviors of coal miners were identified from 5 aspects including individual factors, physical environment, safety leadership, safety management, and group factors. Next, the indicators system of influencing factors of unsafe behaviors for coal miners was established, and the weights were analyzed by the Analytic Network Process (ANP). On that basis, the primary and secondary indicators of the influencing factors were ranked. The weights determined by ANP were taken as the influencing coefficients between variables, and the System Dynamics (SD) model of the unsafe behaviors of coal miners was established. The related simulations were also conducted on the intervention strategies using this model. By combining the weights determined by ANP and the simulation results based on the established SD model, the influencing factors identified were reordered. Finally, this paper proposed the combination intervention strategies mainly from the aspects of safety management, individual factors, group factors, safety leadership and physical environment, which can provide theoretical supports and method guidance for improving the safety production level of coal miners.

The research management processes in the universities is one of the key elements for the financial resources allocation, which promote and transfer promising technological developments to the society. The research entitled "Strengthening the institutional technology transfer and commercialization system of the University of Valle", co-financed by Colciencias, includes technologies that are still in an initial stage of development, but have detected their potential to become the most important technologies of the future. We evaluated, applying decision techniques, 38 type of technologies to be transferred, getting as a result five (5) technologies as the most prepared to be boosted with university resources; indicating Which of the 5 technologies to support first? The Analytic Network Process (ANP) multicriteria method is applied obtaining a ranking that determines the order in which technologies can enter the market, to lead efforts towards future innovations. Resumen Los procesos de gestión de la investigación llevada a cabo en las universidades, es uno de los elementos clave para la asignación de recursos financieros, que impulsan y transfieren al entorno desarrollos tecnológicos promisorios. La investigación titulada “Fortalecimiento del sistema institucional de transferencia y comercialización de tecnologías de la Universidad del Valle", cofinanciada por Colciencias, incluye tecnologías que se encuentran todavía en una etapa inicial de desarrollo, pero han demostrado su potencial para convertirse en las tecnologías clave del futuro. Se evaluaron, aplicando técnicas de decisión, 38 tecnologías candidatas para ser transferidas, obteniendo como resultado cinco (5) tecnologías como las más preparadas para ser impulsadas con los recursos universitarios; indicando ¿cuál de las 5 tecnologías apoyar primero? Para su priorización se aplica el método multicriterio Analytic Network Process (ANP) obteniendo un ranking que determine el orden en que las tecnologías pueden incursionar en el mercado, permitiendo canalizar esfuerzos hacia futura innovaciones.

Diversity multi-criteria decision-making methods have been developed to address different complex decision-making problems, and the analytic network process has been found to be one of the most effective techniques. There is an increase in the quality and quantity of publications related to the analytic network process. This detailed overview can provide the research status and development characteristics of analytic network process research and will be useful to researchers for future research directions. To achieve these goals, bibliometric techniques were used. In addition, past and present hotspots of analytic network process research were concluded, and future research trends were determined. The bibliometric analysis was carried out from various aspects including countries and regions, institutions, journals, authors, research areas, articles and author keywords based on data harvested from the Web of Science database. There were 1485 analytic network process-related publications retrieved from the Web of Science. The results show that Expert Systems with Applications was the most productive journal publishing articles in analytic network process research (118); its number of publications has decreased dramatically since 2013, while Journal of Cleaner Production has shown an upward trend in recent years and ranks second with 47 publications. The most collaborative country is the United States. Taiwan takes a leading position in analytic network process research with 436 publications (29.36%), and National Chiao Tung University, which is located in Taiwan, produced the most articles and has gained the highest h-index (28). The major hot topics that employ analytic network process are sustainability, environmental management and supply chain management. These topics may continue to attract more attention in the future.

In design, most of recent researches focused on criteria related only to the product. These criteria do not take into account the context of the organization which is related to the enterprise's system. In sustainable design, the choice of the best product design scenario is more complex. In fact, the number of issues is importantly high, the data is imprecise and the criteria are interdependent. This situation is more critical in an advanced stage of the design process where the decisions and choices will monitor the next steps of this process. The purpose of this paper is to improve the design approaches in a critical stage from an environmental, economic and social perspective. It deals with two main challenges, the first one is the identification of product design scenarios that are coherent with the organization's context, in terms of internal and external issues along with requirements of the stakeholders. The second challenge is the selection of the optimal option from a high number of identified scenarios. To this end, a new model with two main steps is proposed to deal with both challenges. The methodology is based on two steps. The first depends on a trilogy (i.e. Strategic, tactical, and operational). It generates operational scenarios on the basis of value chain-driven sustainable design strategies using SWOT (Strengths, Weakness, Opportunity, Threats), PESTEL (Political, Economic, Social, Technological, Environmental, Legal), 7S (Structure, Strategy, Shared Values, Style, Staff, Systems, Skills) to identify a multitude of scenarios. The second step is the choice of the optimal scenario that satisfies interdependent criteria using simplified life cycle assessment tools to compute sustainability impacts' indicators, the Choquet integral to minimize the number of scenarios, and the fuzzy analytic network process to select the optimal operational scenario. The proposed model is then implemented in a Tunisian company that manufactures lead acid batteries for motorcycles. The results proved the influence of the organization's context on the choice of the optimal sustainable design scenario. The organization was able to develop strategies and tactics on the basis of its strengths, weaknesses, threats and opportunities. The number of design alternatives was reduced using relevance checks based on influence and control criteria. Environmental, economic and social indicators were computed per life cycle phase in order to be used as inputs in the Choquet integral and the analytic network process algorithms. The proposed model guided the organization to adopt a coherent design scenario that respects the sustainable development criteria.