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This study proposes a novel approach to forecast a transhipment port's competitiveness on the basis of market share. The potential reasons for escalating or diminishing competitiveness are explored by utilising a multi-criteria decision-making (MCDM) methodology. We assess the competitiveness of transhipment ports by seven major components: connectivity, port facility, efficiency, cost factor, policy and management, information systems and green port management, and contribute to the understanding of the interdependence among the components. We investigate the Bangladesh container market due to the uniqueness of its regional context and dimensions of competitiveness. Bangladesh has direct connections to four regional hubs, namely, Singapore, Colombo, Port Klang and Tanjung Pelepas. These regional hubs compete to grasp a significant volume of container movements in line with the rapid growth of Bangladesh's industrialisation and national economy. We find that Singapore outperforms others in terms of competitiveness, but all ports perform below expectations in green port management practices. These findings would benefit port authorities for improving their competitiveness, and liner shipping companies and shippers in making strategic port choice decisions.

Objectives Primary healthcare will not be effective unless there is a proper referral system. In contrast, comparing the performance of healthcare systems provides an opportunity for policy makers to determine the status of the country’s healthcare system compared with their international counterparts. Therefore, we ranked the countries in terms of indicators affected by the referral system. Methods This study was conducted in 2020. In the first phase, which was to determine the indicators affected by a country’s referral system, data were collected by the Delphi method, and therefore, 13 indicators with a content validity ratio equal to or greater than 0.42 were selected. In the second phase, the data of 13 indicators selected in the first phase were extracted from the 2018 and 2019 World Health Organization reports. The weight of the indicators was calculated based on the Decision-Making Trial and Evaluation Laboratory method–based Analytic Network Process (DANP) and Shannon’s entropy, and the VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method was used to rank the countries. SPSS 24 and Excel 2013 software were used for data analysis. Results Switzerland, Germany, and Sweden ranked first, second, and third, respectively. In all the 3 countries, there are no mandatory gatekeeping systems. Physicians, especially general practitioners, are the core of primary healthcare, and in all the 3 countries, there is a uniform and coherent health financing system that is either based on mandatory health insurance (Switzerland and Germany) or taxes (Sweden). India had the lowest ranking. Conclusions It seems that the study of the health system of the countries that have obtained higher rankings can indicate their efforts in establishing a gatekeeping system, family physician program, and appropriate financing system. Therefore, other countries can study successful countries and copy them as a model to improve their health system.

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.

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.

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.

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.

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.

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.

This study uses industrial upgrading (I), export development (E), and investment promotion (K), or collectively, the IEK model to identify critical indicators to asses competitiveness of the service industry in Taiwan. The proposed development and evaluation model for the service industry uses the DEMATEL (decision-making trial and evaluation laboratory) method to construct an influential relationship between the dimensions and criteria; a DEMATEL-based analytic network process (DANP) to obtain influential weights; and a modified Vlsekriterijumska Optimizacija I Kompromisno Resenje (m-VIKOR) method to assess gaps in industry development performance. It illuminates current industry demands and helps to improve service firms’ operations through fewer developmental gaps and stronger policy and strategy. Further, an influential network relationship map (INRM) and influential weights can unravel the complex assessment system and prioritise industry improvements. The results demonstrate that upgrades must occur first to the industry, then to promoting of investments, and lastly, in developing exports. Finally, the practical implications for managing service firms have been discussed.

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.

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.

This study explores the interdependence of big data analytics (BDA) capabilities and the impact of these capabilities on firm performance using an integrated multicriteria decision-making (MCDM) methodology. Drawing on a rich data set obtained from selected case study firms in Pakistan, three MCDM tools, namely, intuitionistic fuzzy decision-making trial and evolution laboratory (IF-DEMATEL), analytic network process (ANP), and simple additive weighting (SAW), are employed to assess the relative importance of BDA capabilities and the relationship of these capabilities with the firm performance. The results show that BDA capabilities are interdependent, and infrastructure capabilities are the highest-ranked among all, followed by management and human resource capabilities, respectively. The SAW results indicate an association between BDA capabilities and firm performance. Moreover, BDA capabilities are more strongly related to operational performance than to market performance. This study explores the interdependence of big data analytics (BDA) capabilities and the impact of these capabilities on firm performance using an integrated multicriteria decision-making (MCDM) methodology. Drawing on a rich data set obtained from selected case study firms in Pakistan, three MCDM tools, namely, intuitionistic fuzzy decision-making trial and evolution laboratory (IF-DEMATEL), analytic network process (ANP), and simple additive weighting (SAW), are employed to assess the relative importance of BDA capabilities and the relationship of these capabilities with the firm performance. The results show that BDA capabilities are interdependent, and infrastructure capabilities are the highest-ranked among all, followed by management and human resource capabilities, respectively. The SAW results indicate an association between BDA capabilities and firm performance. Moreover, BDA capabilities are more strongly related to operational performance than to market performance.

The number of green energy options in different fields is growing because of the need for reducing energy consumption and eliminating gas emission. One of these options is the use of eco-friendly cars produced by various manufacturers. Given the many available options and conflicting evaluation criteria, evaluation of such options becomes complex, and different multi-criteria decision-making (MCDM) approaches have been used to select the most suitable car in terms of eco-friendly car selection. Previous research has been somewhat limited with respect to consideration of interdependencies between evaluation criteria and multi-levels of preference. This paper proposes a new integrated MCDM approach with the capability for considering interdependent criteria for eco-friendly car evaluation and different levels of preference. An analytic network process (ANP) will be used to evaluate the levels of importance of interdependent criteria, and ELECTRE III will be modeled to include thresholds of preference. The introduced model, based on twenty-eight evaluation criteria, was demonstrated in a case study dealing with five alternative electric and hybrid cars. The results from this model were compared to a traditional decision-making method and better results were obtained from the model. The model can help users make proper decisions regarding their future car options in a comprehensive manner. Sensitivity analysis showed the robustness of the model under different setups.

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.

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.

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.

Organizations need management models, which will enable their executives to develop systemic thinking. In addition, executives should keep in mind that: some decision-making processes should be shared; impose political influence according to their preferences; value innovation strategies may be present; it is essential to consider the environmental, economic and social dimensions of sustainability. In this context, this study describes a model to hierarchize strategic decisions according to criteria innovation value, sustainability and budgetary constraint, developed according to the methodology proposed by Mitroff et al. (1974). In addition to hierarchizing, the model allows identifying the degree of importance of each of the strategic decisions in the performance indicators defined as evaluation criteria and sub-criteria. In the conceptualization phase, the model is influenced by concepts that describe complex adaptive systems. Next, the Analytic Network Process with Benefits, Opportunities, Costs and Risks Analysis and Linear Programming techniques are used in order to define the mathematical structure that operationalizes the model. The use of a hypothetical example demonstrates the capacity of the model proposed in this work to support the decision-making process of an organization in the selection of its decision alternatives. Thus, the model can help the academic and business communities concerned with the progress of sustainable societies, insofar as it subsidizes decision-making for the development and implementation of new products and processes related to cleaner production.

Due to problems such as limited land area for waste disposal and waste-borne diseases, waste management organizations have increasingly been offering technologies for recovering energy from waste. These technologies can help governments, local authorities, developers and investors for mitigating climate change and building sustainable societies. The suitable waste-to-energy production technology selection is a complex issue in waste supply chain management that must not only be assessed in terms of both socioeconomic and environmental criteria. With the purpose of balance between energy trilemma issues in the context of waste-to-energy generation and develop sustainable waste management strategies in the waste chain, energy justice criteria must also be taken into consideration. The paper considers the application of an integrated multi-criteria decision-making model consisting of fuzzy decision-making trail and evaluation laboratory method, the analytic network process and the simple additive weighting approaches. The integrated method can be applied to select the suitable technology in a sustainable manner, taking into account energy justice criteria. The applicability of proposed model is demonstrated by a case study of the technology selection in the city of Behbahan, Iran. It includes various technologies for waste-to-energy generation and ranks technologies from the most to least preferred as: Anaerobic digestion, Gasification, Pyrolysis, and Incineration.

This paper proposes an integrated approach to evaluate and optimize performance of petrochemical plants based on asset integrity management criteria. The proposed approach consists of analytic network process (ANP) and data envelopment analysis (DEA). Asset management criteria are identified by surveying related standards and literature and categorized into three levels. First level criteria which are major criteria are based on plan-do-check-act (PDCA) cycle, second level criteria are sub sets of the first level criteria and third level criteria are quantitative and qualitative indicators derived from the literature. ANP is used to calculate relative importance of the second level criteria which enables decision makers to calculate the final score of each decision-making unit. Constant return to scale (CRS) and variable return to scale (VRS) DEA models in both input and output oriented forms are applied and the preferred model for measuring DMUs’ efficiency is selected via normality test. The proposed approach is applied to a set of real time petrochemical plants. Sensitivity analysis is performed to find significant factors affecting DMUs’ efficiency scores. The proposed approach would improve overall performance and could reduce operational loss, safety issues and incidents.

Work zone accidents are important concerns for transportation decision-makers. Therefore, knowledge of driving behaviors and traffic patterns are essential for identifying significant risk factors (RF) in work zones. Such knowledge can be difficult obtain in a field study without introducing new risks or driving hazards. This research uses integrated data mining and multi-criteria decision-making (MCDM) methods as part of a simulator-based case study of work zone logistics along a highway in Missouri. The research design incorporates k-mean clustering to cluster driving behavior trends, analytic network process (ANP) to determine weights for criteria that are most likely to impact work zones, and the Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to rank the alternatives (clusters). Transportation engineers and decision makers can use results from this case study to identify driving populations most likely to engage in risky driving behaviors within work zones, and to provide guidance on effective work zone management.

Coal is an irreplaceable part of the world energy structure and the condition will not be changed in the near future. Under the Belt and Road initiative, more and more Chinese energy enterprises are planning to invest in coal-fired power plant (CFPP) abroad. However, there are multiple potential risks in investing in CFPP overseas. So, assessing the risk level can help the investors avoid too risky countries and at the same time facilitate the government to take measures to reduce the potential risks to attract more investors. To reasonably assess overseas investment risk of CFPP in countries along the Belt and Road initiative, this paper establishes an evaluation criteria system from eight dimensions which consists of a total of 39 criteria. Then, these criteria weights are determined through a combined analytic network process-Entropy method. Furthermore, considering the psychological characteristics of decision-makers, a TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) approach is used to rank the overall risk level of CFPP investment for 23 nations. The findings indicate that the criterion of economic foundation owns the largest weight. Moreover, among these evaluated nations, Singapore have the lowest risk for China’s CFPP investment, followed by New Zealand and Thailand.

Increasingly, composite indicators and multi-criteria approaches are applied in environmental assessment and decision-making, including the EU Water Framework Directive. For example, integrated evaluation of aquatic ecosystem conditions and functioning usually involves a group of criteria, such as biological organisms and communities, physicochemical and hydromorphological variables, which are measured individually and combined by a weighted linear function into an overall ‘score’. We argue that the network interactions of evaluation components are useful information for expert judgments, which have not been sufficiently considered in existing multi-criteria combination strategies in environmental assessment and management. Built upon the Analytic Network Process and demonstrated with the Chishui River Basin in China, this paper introduces a network-based expert judgment approach to construct ecological water quality indicators, and to determine and adjust their variable weight settings with information of interaction networks. This approach has potential to construct composite indicators for a broad environmental context.

Identifying the flood susceptible areas is a vital and substantial element of disaster management for every country to control and mitigate injuries of the natural hazards. The current research presents a framework for the preparation of flood prone areas' maps by the integration of Geospatial Information System (GIS), fuzzy logic, and Multi-Criteria Decision Making (MCDM). To achieve this goal, a spectrum of geophysical, geomorphological, meteorological, hydrological, and geographical criteria have been addressed. Considering the linkage and the interdependencies of the criteria, DEcision-MAking Trial and Evaluation Laboratory (DEMATEL) approach are used to form the network of relations among the criteria. Moreover, considering inevitable uncertainty, ambiguity/vagueness of the experts' opinions, fuzzy theory is integrated with DEMATEL to handle the issue. Finally, Analytic Network Process (ANP) are implemented to calculate the final weight of every single criterion. The Kurdistan province, located in the North-West of Iran, is considered as the case study in which numerous flood events has had recently happened. The efficiency of the model is assessed through the area under the curve (AUC) and statistical measures such as the Kappa index. In order to evaluate the produced classified flood susceptibility map, the map of historical flood events in the province is also used. About 85% of validation area is classified as “Very High flood Susceptible” which implies the efficiency of proposed framework for flood susceptibility mapping. Furthermore, for evaluating the functionality of the framework in comparison with traditional approaches, the well-known AHP methodology is implemented too. The validation results demonstrate that the Fuzzy-DEMATEL ANP model (AUC-ROC = 0.938, Kappa = 0.88) has a higher performance accuracy compare to the AHP model (AUC-ROC = 0.918, Kappa = 0.79). Moreover, comparing the proposed and validation model shows that the proposed framework can effectively improve decision makers to provide flood susceptibility maps, and recognize flood susceptible areas in data-scarce and ungauged regions.

Simultaneous application of lean and green practices requires adequate methods to evaluate the contribution and effects of both paradigms on organizational performance. Thus, the objective of this study is to develop an integrated approach to evaluate the impacts of lean and green practices on organizational performance and prioritize improvements in the system. To this end, this research used the Analytic Network Process (ANP) for the operationalization of the theoretical framework and tested it through an application in a footwear company. By applying the assessment approach, it was possible to evaluate the lean and green systems individually and in an integrated way through the lean-green index, a performance measure developed specifically for this work. From a managerial perspective, this research provided a tool that enables companies to evaluate their lean and green systems and identify which practices should be prioritized to improve operational and environmental performance.

This study proposes comprehensive evaluation criteria, involving aspects of energy, economy, environment, technology, and society (E3TS), for the development and utilization of renewable energy (RE). The criteria consist of 22 indicators. The comprehensive performance, from 2011 to 2015, of 30 provincial regions in China with respect to renewable energy development and utilization (REDU) was evaluated on the basis of the E3TS criteria, using an analytic network process (ANP). The results show that the total comprehensive performance (2011–2015) of all the 30 provincial regions of China improved in general. There was a marked improvement in technology performance. Qinghai, Yunnan, and Sichuan ranked the top three in comprehensive performance of REDU, while the comprehensive performance of Shaanxi, Hainan, and Henan were the lowest. During the study period, while the comprehensive performance of most provincial regions such as Ningxia and Xinjiang increased, that of five provincial regions, including Tianjin and Hainan, decreased. The regional comprehensive performance was severely affected by the installed capacity and power generation of RE.

The global extent of habitat degradation and accelerated rate of diversity loss requires conservation managers to quickly develop conservation plans within political, financial and time constraints. Despite the important role of surrogate species for structuring site-based conservation, these species have often been selected using unsupervised approaches. We aimed to incorporate the strengths of several surrogate species approaches within a new single species selection framework in order to select a suite of ecologically and culturally suitable landscape species as a complement to the network of current protected areas (PAs) for conservation of central Iranian deserts. Using carnivores, we utilised multiple decision making criteria, combining decision making trial and evaluation laboratory (DEMATEL) with analytic network process (ANP) methods, to rank the most important criteria in selecting landscape species. We then applied weighted criteria, ecological niche modelling, experts’ information, and attitudes of local communities to score ten candidate species based on the thirteen criteria. Finally we identified a suite of complementary landscape species for ecological planning of the landscape. This framework is as an effective approach for selecting landscape species especially in habitats where there are few data on candidate species.

The Energy Performance Contracting (EPC) industry in China faces many severe risks that hinder its development. This study aims at identifying the risk factors in China's EPC industry, developing a generic method for prioritizing these factors and identifying the key risk factors, and proposing some policy implications for China's decision-makers to draft effective measures and policies to promote the harmonious development of EPC industry in China. A total of 21 risk factors in five categories including external environmental risks, managerial and operational risks, financial and market risks, technical risks, and client risks are identified. A best-worst network (BWN) method was developed for ranking these risk factors based on the traditional Analytic Network Process (ANP) and the BW method. The results determined the BWN method were validated by ANP method, and the results determined by the BWN method were also compared with that determined by Analytic Hierarchy Process (AHP) method. According to the results, some policy implications have also been proposed for China's decision-makers.

Constructing maintenance networks is becoming an urgent requirement for ensuring the continuous reliable operation and fast response of agricultural machinery maintenance in busy farming seasons. According to dynamic characteristics, time variation, and the cooperativity of agricultural machinery cluster working, this paper proposes a service provider selection framework for the layout of dynamic maintenance network nodes. Furthermore, an integrated analytic network process (ANP) and balanced score card (BSC) service provider selection approach is provided to help construct the maintenance network. Concerning four determinants of BSC: service satisfaction, process capability, knowledge assets, and service cost, this method provides qualitative and quantitative indicators, four evaluative dimensions and a three-layer network evaluation system index. Moreover, this method is applied to maintenance services for agricultural machinery clusters working in cross-regional operations in China. The results indicate that process capability is the most important determinant for a service network, and the proposed method contributes to the ability of decision-makers to comprehend the complex relationship among factors during the service provider selection process. Meanwhile, this method improves the reliability of service provider selection during the construction of a maintenance network.

In the last decade, renewable installed power capacities in electrical grids have increased rapidly in most countries. It is very significant to utilize the highest rate of wind power in reaching the 100% renewable energy target in power grids, considering its widespread potential and technological developments. In the field of renewable energy, the Multiple-Criteria Decision Analysis (MCDA) is gaining popularity because of the versatility it gives decision-makers to strategic decisions when considering all parameters and goals collectively. Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis is commonly utilized to assess alternative strategies in multi-criteria decision issues. This paper uses a hybrid Fuzzy Analytic Network Process (FANP) approach based on SWOT to assess further development of wind power capacities in Turkey with the sectoral effects of the Covid-19 outbreak in 2020. To validate the proposed approach, the results are compared with the SWOT-based Analytic Network Process (ANP), Analytic Hierarchy Process (AHP), and fuzzy AHP (FAHP) results. The results of the study suggest long-term meaningful strategies and rank priorities under current scenarios so as not to slow down ongoing wind investment and ensure the sustainability of the wind energy sector.

In recent years, numerous researchers have investigated the implementation of Product-Service System (PSS) in the industry as a means of enhancing companies’ performances, representing a win-win solution for both companies and customers. Nevertheless, the elicitation of the customer needs and expectations through the assessment of the mutual interactions between the product’s and the service’s elements has been scarcely addressed. To accomplish this, the paper proposes a methodology based on the analysis of the market demand and customer requirements by means of the Quality Function Deployment for Product Service System (QFDforPSS) and the Analytic Network Process (ANP) methods. To verify the proposed methodology, it was applied to a case study in the medical devices sector, in collaboration with a provider of medical imaging equipment. Practical results showed that the proposed methodology allowed engineers to make the assessment of the PSS requirements comparable and homogenous, providing an accurate evaluation of their mutual interactions, as well as of those factors that are hidden to the customers and have an indirect effect on its satisfaction. Although the proposed approach needs to be validated through further studies in different contexts, it can be valuable for theory development and also for practitioners in the PSS field.

This paper presents a quantitative basis to analytically determine the ranking of factors through strengths, weaknesses, opportunities, and threats (SWOT) analysis using a fuzzy multi-criteria decision-making method known as analytic network process (ANP). The ANP approach is chosen due to its promising capability in modelling potential dependencies among SWOT factors. This study is conducted for the first time in a ceramic and tile industry to determine the most important influential factors affecting the performance. Moreover, the prior mentioned presents unique factors to take into account the inherent uncertainty of human decision making processes via fuzzy logic. The possible benefits of the presented study are: (1) an increased level of exports in the tile and ceramic industry, (2) selecting the best investment opportunities on valuable, inimitable, and irreplaceable resource, and (3) the formulation of a competitive strategy on the basis of existing potentials in the company and throughout the industry. To achieve these aims, grey-relational and fuzzy logic are utilized as well. The proposed approaches are applied and tested for the first time on an Iranian ceramic and tile company as a case study. Finally, the applicability of the proposed approach is investigated by obtaining and identifying the most and the least important strategies. The results indicate that the proposed fuzzy logic and grey relational ANP-based SWOT are highly capable and applicable approaches in providing valuable insights for strategic decisions in the ceramic and tile industries.

This paper proposes a methodology to select an expert for the development of humanitarian and social projects based on multi-criteria decision-making with incomplete information relations to add more objectivity to the recruitment process. The proposed approach starts with assessing the decision makers (DMs) by means of Fuzzy Analytic Hierarchy Process (AHP). Then, Analytic Network Process (ANP) is used to weigh the criteria in order to take into account the interdependencies between criteria. In the recruiting process, an expert could be unable to assess all criteria. Therefore, the incomplete preference relations is used when a DM is unable to express his judgment. At a later stage, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is addressed to rank the different candidates. The proposed methodology is applied to a situation where an extra DM evaluates four DMs, and six criteria are used to select one candidate among five. The obtained criteria weights and the final ranking of the candidates are analysed and compared to an approach where there is no lack of information in the decision maker’s preferences.

Risk, complexity, and uncertainty are inherent components of megaprojects due to their unique features. However, existing project management practices lack a structured synthesis of these concepts, which leads to unrealistic risk assessments, ineffective management strategies, and poor project performance. In order to fill this gap, this research aims to develop a holistic risk quantification approach incorporating risk-related concepts. For this purpose, a conceptual risk assessment process developed for mega construction projects was operationalized with an Analytic Network Process (ANP) model. The weights of the risk sources in the ANP model were determined by the domain experts through a two-round Delphi study. With the purpose of improving the efficiency and reliability of the knowledge elicitation process, the Delphi study was supported by an interactive data collection tool capable of ANP calculations. The resulting model helped to prioritize the risk sources in mega construction projects. The validity of the findings was tested through the data of 11 mega construction projects. Validation studies revealed the potential of the ANP-based model in quantifying the project risks. Hence, the novel approach proposed in this study is expected to contribute both to the literature by unveiling the interactions between risk-related concepts and to the practitioners by assisting them in assessing the project risks more realistically. Although the risk quantification model has been developed for mega construction projects, it can also be implemented in other project-based industries with minor modifications.

A Smart Healthcare Management System (SHMS) is an intelligent technology integration practice for public medical centers. During the implementation of the SHMS, information on strategy and decision-making problems play an important role in smart hospital development, which directly affects the effective workflow of the hospital operation and the service quality of patient care. Existing literature has explored the main factors for evaluation the SHMS, but a qualitative and quantitative analysis is rarely mentioned. To address the research gap, this study proposes a hybrid exploratory three-phased Multi-criteria Decision-making (MCDM) model that combines the Decision-Making Trial and Evaluation Laboratory Model (DEMATEL) approach, the Analytic Network Process (ANP), and Zero-One Goal Programming (ZOGP) for evaluating SHMS portfolios in a resource-limited environment. The results indicate that the Financial Subsidy Policy is the most important determinant for SHMS development, while the Medical Data Informational System (MDIS) and Medical Device and Drug Management System (MDMS) are the optimal SHMS portfolios that satisfy the goal of strategy weights and limited resources. The proposed hybrid decision model can improve the reliability of SHMS portfolios.

Urban planning and its relevant transportation deploying have a particularly profound influence on the sustainability and livability of a city, and which also be crucial to the quality of life to urban residents at the same time. It was also suggested that the conception of livability should be extended to embrace the concerns associated with the sustainability. However, planning frameworks or assessment patterns that address the dynamics of urban planning and demand for transportation deploying are relatively rare; there also few public policies in related research fields have discussed the effects of the changes in various assessment indicators over time. Furthermore, following the rising advancements in social communication and computer technologies in modern society, the data collection, storage, and processing capabilities of people have improved substantially. And, the emergence of big data or extendible open data facilitates analysis and prediction availability, and enabled people to find immediate solutions to numerous dilemmas encountered. Therefore, based on the aforementioned intention, treating the city as a dynamic process with the trying of introducing the big data or extendible open data for facilitating urban sustainability and livability is undoubtedly worth to explore in further. The present study intends to initially examine the application of big data in sustainable and livable transportation strategies in Taipei City, Taiwan. Firstly, we investigate previous research on transportation sustainability in various countries to generalize our preliminary list of transportation sustainability indices that satisfy the principles of livable cities. And, key indices were then selected through the Fuzzy Delphi Method by administering a questionnaire to six experts from industrial, governmental, and academic sectors respectively. The research results were applied to develop decision-making strategies for responding to the environmental dynamics of Taipei City's transportation infrastructure system by using the analytic network process combined with a data-mining technique. Thus, big data pertaining to urban transportation were analyzed to predict the future dynamic trends of the key indices and prioritize the sustainable transportation strategies for a livable city under dynamic temporal and spatial changes. Ultimately, the policy implications of this study can not only offer a solution for current needs related to urban planning but also serve as a more transparent decision-making or well selection basis for developing sustainable and livable urban life in near future.

The aim of assessing the hierarchical medical policy proposals is to select the optimal one from possible alternatives so that it can better strengthen the policy development. After clarifying the research objectives, issues and method of this study, a new evaluation indicator system for HMP proposals is established from the perspective of stakeholders in the medical system by taking the length of observation time into consideration. Then, three HMP proposals are determined by analyzing the existing studies, which play the role of possible alternatives. Furthermore, we derive the initial weights of control criteria and sub-criteria based on multiplicative consistency of the hesitant fuzzy linguistic preference relation, and develop the decision matrix by combining the subjective and objective information together. In order to derive the comprehensive weights of control criteria, we construct a supermatrix and develop the hesitant fuzzy linguistic analytic network process. A numerical example is presented to illustrate the application of the evaluation indicator system and the HFL-ANP method. Some discussions of results and a comparative analysis are provided.

Although manholes are significant assets in sewer networks, their assessment is receiving little attention compared to sewer pipelines. It was reported that more than three-million manholes in the United States (US) were structurally weak and prone to collapse. To avoid sudden failure, systematic manhole condition assessment model is required. Therefore, the primary objective of this study was to develop a component-based condition assessment model for sewer access holes. The methodology was based on integrating the Quality Function Deployment (QFD), Decision-Making Trial Evaluation Laboratory (DEMATEL) techniques to study the causality relationship between the defects in the nine identified components of the manhole. The relative importance weights of the components were found by deploying the Analytic Network Process (ANP) and were used to compute the overall condition of the asset. Based on the results, the roots were the highest influence power in many of the components. In addition, the wall, cone, cover and frame, and seals components were higher importance weights compared to the remaining components. This model was tested on an actual case study from the city of Edmonton in Canada and compared with actual values. The model produced an average validity percentage (AVP) of 76.24%. The developed model is expected to enhance the assessment of manholes and therefore provide robust conclusions. Besides, the model offers a backward analysis to pinpoint critical components in the manhole. Consequently, better decisions are made pertinent to maintenance, rehabilitation, and replacement interventions.

Reusing historic buildings requires solving complex decision-making problems that involve the tangible and intangible values of numerous interest groups. In this study, the integrated multicriteria decision-making method was employed to optimise the assessment process of historic building reuse. The Sun Yat-Sen Historical Museum was selected as the case study in this study. By using a literature review, case analysis, and the fuzzy Delphi method, a decision-making model was established. Subsequently, the priority order of the reuse alternatives of the historic building was determined through an analytic network process. The results revealed that the most suitable reuse alternative of the museum was for community activities, followed in sequential order by commercial, education and exhibition, and composite use. A sensitivity analysis was conducted to assess the sensitivity of criteria of various “orientations,” which revealed that when the weights of economics-oriented criteria decreased or those of society and nature-oriented criteria increased, commercial use might become the optimal reuse alternative. However, when the weights of the economy and history-oriented criteria increased, the priority of community activities rose. Therefore, changes in economic, social, and natural factors directly influenced the priority order of the historic building reuse alternatives. Accordingly, we suggest that decision-making units focus on providing facilities that satisfy the requirements of the neighbourhood surrounding the museum. Examples include the building of facilities that are nonprofit or directly managed by the local government such as libraries, community activity centres, and nurseries. The possibility of transitioning the historic building to commercial use should be maintained through future space usage planning to adapt to possible changes in the urban environment.

The role of energy sector is quite crucial due to its economic, social, and political implications. World energy consumption has increased significantly with the growth of world population and gross domestic product throughout the history. There has been considerable increase in the renewable energy investments recently. However, conventional fossil fuels are still the primary source of energy generation of the world. The major objective of this study is to create a framework to support decision making process in renewable energy investments. For this purpose, main criteria that influence the decisions of the investors are determined based on an extensive literature survey and investigation of sector practice. The relevant criteria are categorized under three groups as technical, economic, and environmental & social. Then, interrelations between the criteria are identified and analytic network process method is applied to obtain the importance weights. Based on the findings of this research, economic criteria such as policies and regulations, availability of funds, and investment cost are found to be the most significant factors in the decision process of renewable energy investments. Research findings are expected to help (i) the investors assess the competencies of their investments and (ii) the governments shape their policies in line with the needs of renewable energy investments in order to increase the use of renewable energy.

China consumes more energy and emits more carbon dioxide than any other country. The building sector accounts for nearly 1/3 of the total national energy consumption in China; thus, it is critical to find a solution for improving buildings’ energy efficiency. Under the market-oriented mechanism, constrained by the energy performance contracting (EPC) mode, energy service companies (ESCOs) play an important role in improving energy efficiency for high-energy-consumption industries, including the building sector. The aim of this study is to determine the strategy for boosting the building ESCO industry. By conducting a strength, weakness, opportunity, and threat (SWOT) analysis, this paper presented the status of the building ESCO industry in China and proposed alternative strategies. By utilizing a conventional multi-criteria decision-making method, namely, the Analytic Network Process (ANP), this study provides a quantitative basis to analytically determine the ranking of the factors in SWOT analysis and select the best strategy to promote the development of the building ESCO industry. The results showed that the ANP-SWOT is a viable and highly capable methodology that provides invaluable insights for strategic management decisions in the building ESCO industry, and a set of strategies for promoting ESCO development in the building sector were proposed based on this approach.

A supply chain employing multiple sales channels, needs to continually enhance these channels for achieving competitive advantage. The purpose of this paper is to provide a channel-level benefit-cost analysis, through an integration of operations and marketing perspectives to ultimately provide a systematic procedure for optimal investment decisions towards improving existing sales channels. This study is motivated by the decision for improvement, on the part of many established firms that typically utilize multiple channel structures. In pursuing our objective, an analytic network process (ANP), that considers both customer priorities and demand correlations among channels, is suggested, for obtaining customer input data. Utilizing a proposed benefit-cost ratio metric for each channel, incorporating customer input information, as well as relevant operating costs, we outline a mathematical programming procedure for determining the optimal allocation of a limited investment budget among these channels. Our suggested methodological approach is illustrated by a realistic numerical example and some selected sensitivity analysis.

The maritime industry plays an important role in the economic growth of a country. In recent decades, Taiwan has faced the critical problem of flagging-out. For the future development of its maritime industry, Taiwan needs to understand the decision-making procedures of registry selection by shipowners, and offer the right policies for improving the attractiveness of the Taiwanese shipping registry. The purpose of this study is to structure a multiple-criteria decision-making (MCDM) analysis hierarchy for shipping registry selection by shipowners. The Analytic Network Process (ANP) methodology is utilized to find out the influencing factors in the decision-making procedure of registry selection. After structuring the fundamental hierarchy, the ANP model is applied to the shipping registry of Taiwan. The results show that operating costs are the most important factor, followed by market conditions, national policies and laws, and international laws and restrictions. Taxation of shipping, finance conditions, trade restrictions and barriers, crew costs, and incentives are the five most important sub-criteria. The flag of convenience (FOC) is the best choice as a shipping registry system, followed by the second ship register. The national flag is shipowners’ last choice. In addition, to illustrate the feedback relationship in the ANP structure, the paper also presents the ranking of important sub-criteria under various shipping registry choices.

Research in service recovery has attracted significant attention in recent years, but few studies have focused on service recovery from both customer and expert perspectives simultaneously. This study aims to address this research gap by adopting a Quality Function Deployment (QFD) approach that integrates Analytic Network Process (ANP) method. The results show that among five major groups of service failure in hotels, customers perceived the most important ranking as the one covering “Guestroom”; followed by “Arrival, Billing and Departure”; “Restaurant, Food and Beverage”; “Staff”; and “Facilities and Other Services”. While for service recovery, the most effective means to deal with service failures were “Immediate Correcting of Problem”; followed by “Apology”; “Replacement”; “Discount”; and then the remaining four service recovery actions. This study may contribute to the literature as an important reference for academics and professionals, specifically those in the hospitality industry, as it identifies the critical factors of customer satisfaction to enhance the hotel service quality.

System resilience is defined as the ability of a system to withstand and recover from deliberate attacks, accidents, or naturally occurring threats or incidents. It is a key property to measure the robustness of complex and high coupling degree systems such as urban water supply system, electricity transmission system and waste disposal system. This paper proposes an integrated decision-making process to evaluate system resilience for urban lifeline systems. The decision-making process has two steps: First, the establishment of a system resilience indicator framework that contains five dimensional attributes, namely Materials and Environmental Resources, Society and Well-being, Economy, Built Environment and Infrastructure, and Governance and Institutes. Second, the development of a hybrid K-means algorithm, which combines entropy theory, bootstrapping and analytic network process. Using the real data of is resilient indicators data, this methodology identifies and classifies the resilience level of different regions supported by the lifeline systems. Finally, by combining with geographic information system, the calculation, visualization and classification of resilience leave can be realized. The results can serve as guidelines for governments to allocate resources and prevent large economic loss, especially in those low resilience level areas.

An engine rubber mounting is one of the important parts of a vehicle. It is a function to isolate or absorb and to reduce vibration to the vehicle body thus to the passenger itself. Due to the engine compartments environment such as heat and massive vibration due to road conditions, the engine rubber mountings lifespan has been reduced. Thus several studies have been conducted to upgrade the material lifespan to make it more reliable and better engine mounting components. This paper presents the conceptual design of kenaf fiber polymer as automotive engine rubber mounting composites using the integration of Theory of Inventive Problem Solving (TRIZ). In this early stage, the solution is generated using 40 inventive principles and TRIZ contradiction method. The solution parameter for the specific design character is the selected using the morphological chart to develop a systematic conceptual design for the component. Four (4) innovative design concepts were produced and Analytic Network Process (ANP) methods were utilized to perform the multi-criteria decision-making process of selecting the best concept design for the polymer composite engine rubber mounting component. Keywords: TRIZ, ANP, Morphological, Automotive composites component, Engine rubber composite mounting

This article proposes a bikeability assessment method to evaluate zone-based friendliness to biking within an area. The proposed method is developed as an analytic network process (ANP) that contains grey numbers and multiple assessment criteria. The grey ANP is unique in bikeability literature and overcomes the disadvantages of existing assessment methods in the following ways. (1) The ANP framework can manage the interdependences among criteria and zones, (2) the multiple assessment criteria can consider comprehensive perspectives of bikeability, and (3) the grey numbers measure possible ranges of criteria performances and are good at handling various performances within a zone. The application of the method to the bikeability assessment of zones in Daan District, Taipei City, Taiwan not only verifies the effectiveness of the method but also provides action directions to the local administration in developing bike-friendly environments.

Safety assessment is an essential work to guarantee the safety of oil drilling. There are relations and dependencies between human factors in oil drilling work system. Therefore, the safety of oil drilling work system should be analyzed in a comprehensive way. The Human Factors Analysis and Classification System (HFACS) is applied to establish rational and applicable index system for investigating human errors. The Analytic Network Process (ANP) method is used to obtain the priorities of human factors considering the interdependences, however, the deficiency of ANP is that the obtained results are subject to experts’ cognitive limitations and psychological biases. The Structural Equation Modelling (SEM) is used to form the ANP model auxiliary, which may be expected to overcome subjective opinions from experts and provide a more pertinent and practical safety strategies. A survey is conducted to explore the importance of human factors through questionnaires of which 283 pieces made up the original data. Afterwards, the human factors’ weights are calculated by the ANP method. As a comparison, a frequency-based method is also used to obtain the frequencies of factors and observations causing accidents using accident reports. The causal chain and the priorities of the importance of human factors are explored by this hybrid method; the results are consistent with the experience and knowledge of safety management. We discuss the interdependencies between the human factors and the priorities in general, whilst, the specific safety requirements and recommendations in the hoisting and lifting system are also provided as an example.