The Experts below are selected from a list of 400272 Experts worldwide ranked by ideXlab platform
Haobo Zhang - One of the best experts on this subject based on the ideXlab platform.
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a cloud Decision Framework in pure 2 tuple linguistic setting and its application for low speed wind farm site selection
Journal of Cleaner Production, 2017Co-Authors: Kaifeng Chen, Bingxin Zeng, Meng Yang, Haobo ZhangAbstract:Abstract Low-speed wind farm site selection is crucially important for investment returns. However, three great problems reducing the Decision-making accuracy and restricting applications exist in the present multiple criteria Decision analysis. Firstly, the uncertainty of information fails to be fully described, without considering its randomness. Secondly, during dimensionless treatment and normalizing, some information distortion and loss are caused when evaluating the differences among criteria values just from a mathematical standpoint. Thirdly, the managers are excluded from the Decision-making process, which decreases the practicality and operability, and considerably restricts the application of the Decision-making methods at the same time. In order to overcome these deficiencies, a cloud-based Decision Framework under pure 2-tuple linguistic environment is proposed for low-speed wind farm site selection in this paper. First, the criteria values are transformed into 2-tuple linguistic through dimensionless treatment and normalizing; then the extended golden section method is used to transform 2-tuple linguistic into cloud variable. Next, a pure cloud weighted arithmetic averaging operator is constructed to rank the alternatives. After that a case from China is presented to demonstrate the effectiveness. Finally, the comparison analysis and sensitive analysis are conducted, proving the correctness and advantages of the proposed Decision Framework.
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Decision Framework of solar thermal power plant site selection based on linguistic choquet operator
Applied Energy, 2014Co-Authors: Yunna Wu, Shuai Geng, Haobo Zhang, Min GaoAbstract:Site selection plays an important role in the entire life cycle of solar thermal power plant (STPP) and the multi-criteria Decision making (MCDM) methods are very important in the whole STPP site selection process. Some problems in the present MCDM methods decrease the evaluation quality of STPP site selection: first, the qualitative information cannot be processed reasonably in the evaluation of the STPP site; second, it is difficult to satisfy the independent assumption of the multi-criteria Decision making methods used in STPP site evaluation in fact. So this paper proposes a Decision Framework for evaluating and selecting optimal site for STPP. The Framework involves three stages. First, potential feasible sites are identified based on energy, infrastructure, land, environmental, and social factors; second, the fuzzy measure is used to weight the important degrees of criteria to avoid the independent assumption; third, the linguistic variable instead of fuzzy set theory or numerical value reflects the experts’ intuitive preferences; fourth, a group Decision making method with linguistic Choquet integral (LCI) is used to rank the alternative sites. Finally, a Chinese case study demonstrates the effectiveness of Decision Framework proposed in this paper.
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study of Decision Framework of wind farm project plan selection under intuitionistic fuzzy set and fuzzy measure environment
Energy Conversion and Management, 2014Co-Authors: Shuai Geng, Haobo ZhangAbstract:Abstract Project selection plays an important role in the entire life cycle of wind farm project and the multi-criteria Decision making (MCDM) methods are very important in the whole wind farm project plan selection process. There are problems in the present MCDM methods decrease evaluation quality of the wind farm project plans: first, the information loss exists in the wind farm project plan evaluation process. Second, it is difficult to satisfy the independent assumption of the multi-criteria Decision making methods used in the wind farm project plan evaluation in fact. Third, the compensatory problem of performance scores of the wind farm project plans is processed unreasonably. Hence the innovation points of this paper are as follows: first, the intuitionistic fuzzy numbers are used instead of fuzzy numbers or numerical values to reflect the experts’ intuitive preferences to decrease the probability of information loss; second, the fuzzy measure is used to rate the important degrees of criteria in order to avoid the independent assumption and to increase the reasonability; third, the partial compensatory problem of performance scores is well processed by using intuitionistic fuzzy Choquet (IFC) operator and generalized intuitionistic fuzzy ordered geometric averaging (GIFOGA) operator. These operators can deal with the compensatory performance scores and non-compensatory performance scores respectively. Finally, a case study demonstrates the effectiveness of Decision Framework.
Shuai Geng - One of the best experts on this subject based on the ideXlab platform.
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cloud based Decision Framework for waste to energy plant site selection a case study from china
Waste Management, 2016Co-Authors: Kaifeng Chen, Bingxin Zeng, Meng Yang, Shuai GengAbstract:Waste-to-energy (WtE) plant site selection is crucially important during the whole life cycle. Currently, the scholars launch some research in the WtE plant site selection. However, there are still two great problems in the present methods. Firstly, the uncertainty of information is not fully described. Secondly, the correlation among criteria lacks rationality, which is mainly manifested in two aspects: one is ignoring the correlation, and the other is measuring unreasonably. Firstly cloud model is introduced to describe the fuzziness and randomness of the information fully and precisely. Secondly, the 2-order additive fuzzy measures based on the Mobius transform and correlation coefficient matrix is introduced for fuzzy measure scientifically and reasonably. Thirdly, Cloud Choquet integral (CCI) operator is constructed to evaluate the alternatives. Finally, a case from China proves the effectiveness.
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Decision Framework of solar thermal power plant site selection based on linguistic choquet operator
Applied Energy, 2014Co-Authors: Yunna Wu, Shuai Geng, Haobo Zhang, Min GaoAbstract:Site selection plays an important role in the entire life cycle of solar thermal power plant (STPP) and the multi-criteria Decision making (MCDM) methods are very important in the whole STPP site selection process. Some problems in the present MCDM methods decrease the evaluation quality of STPP site selection: first, the qualitative information cannot be processed reasonably in the evaluation of the STPP site; second, it is difficult to satisfy the independent assumption of the multi-criteria Decision making methods used in STPP site evaluation in fact. So this paper proposes a Decision Framework for evaluating and selecting optimal site for STPP. The Framework involves three stages. First, potential feasible sites are identified based on energy, infrastructure, land, environmental, and social factors; second, the fuzzy measure is used to weight the important degrees of criteria to avoid the independent assumption; third, the linguistic variable instead of fuzzy set theory or numerical value reflects the experts’ intuitive preferences; fourth, a group Decision making method with linguistic Choquet integral (LCI) is used to rank the alternative sites. Finally, a Chinese case study demonstrates the effectiveness of Decision Framework proposed in this paper.
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study of Decision Framework of wind farm project plan selection under intuitionistic fuzzy set and fuzzy measure environment
Energy Conversion and Management, 2014Co-Authors: Shuai Geng, Haobo ZhangAbstract:Abstract Project selection plays an important role in the entire life cycle of wind farm project and the multi-criteria Decision making (MCDM) methods are very important in the whole wind farm project plan selection process. There are problems in the present MCDM methods decrease evaluation quality of the wind farm project plans: first, the information loss exists in the wind farm project plan evaluation process. Second, it is difficult to satisfy the independent assumption of the multi-criteria Decision making methods used in the wind farm project plan evaluation in fact. Third, the compensatory problem of performance scores of the wind farm project plans is processed unreasonably. Hence the innovation points of this paper are as follows: first, the intuitionistic fuzzy numbers are used instead of fuzzy numbers or numerical values to reflect the experts’ intuitive preferences to decrease the probability of information loss; second, the fuzzy measure is used to rate the important degrees of criteria in order to avoid the independent assumption and to increase the reasonability; third, the partial compensatory problem of performance scores is well processed by using intuitionistic fuzzy Choquet (IFC) operator and generalized intuitionistic fuzzy ordered geometric averaging (GIFOGA) operator. These operators can deal with the compensatory performance scores and non-compensatory performance scores respectively. Finally, a case study demonstrates the effectiveness of Decision Framework.
Naveen Eluru - One of the best experts on this subject based on the ideXlab platform.
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analyzing commuter train user behavior a Decision Framework for access mode and station choice
Transportation, 2014Co-Authors: Vincent Chakour, Naveen EluruAbstract:The purpose of the current research effort is to develop a Framework for a better understanding of commuter train users’ access mode and station choice behavior. Typically, access mode and station choice for commuter train users is modeled as a hierarchical choice with access mode being considered as the first choice in the sequence. The current study proposes a latent segmentation based approach to relax the hierarchy. In particular, this innovative approach simultaneously considers two segments of station and access mode choice behavior: Segment 1—station first and access mode second and Segment 2—access mode first and station second. The allocation to the two segments is achieved through a latent segmentation approach that determines the probability of assigning the individual to either of these segments as a function of socio-demographic variables, level of service (LOS) parameters, trip characteristics, land-use and built environment factors, and station characteristics. The proposed latent segment model is estimated using data from an on-board survey conducted by the Agence Metropolitaine de Transport for commuter train users in Montreal region. The model is employed to investigate the role of socio-demographic variables, LOS parameters, trip characteristics, land-use and built environment factors, and station characteristics on commuter train user behavior. The results indicate that as the distance from the station by active forms of transportation increases, individuals are more likely to select a station first. Young persons, females, car owners, and individuals leaving before 7:30 a.m. have an increased propensity to drive to the commuter train station. The station model indicates that travel time has a significant negative impact on station choice, whereas, presence of parking and increased train frequency encourages use of the stations.
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analyzing commuter train user behavior a Decision Framework for access mode and station choice
Transportation, 2014Co-Authors: Vincent Chakour, Naveen EluruAbstract:The purpose of the current research effort is to develop a Framework for a better understanding of commuter train users’ access mode and station choice behavior. Typically, access mode and station choice for commuter train users is modeled as a hierarchical choice with access mode being considered as the first choice in the sequence. The current study proposes a latent segmentation based approach to relax the hierarchy. In particular, this innovative approach simultaneously considers two segments of station and access mode choice behavior: Segment 1—station first and access mode second and Segment 2—access mode first and station second. The allocation to the two segments is achieved through a latent segmentation approach that determines the probability of assigning the individual to either of these segments as a function of socio-demographic variables, level of service (LOS) parameters, trip characteristics, land-use and built environment factors, and station characteristics. The proposed latent segment model is estimated using data from an on-board survey conducted by the Agence Metropolitaine de Transport for commuter train users in Montreal region. The model is employed to investigate the role of socio-demographic variables, LOS parameters, trip characteristics, land-use and built environment factors, and station characteristics on commuter train user behavior. The results indicate that as the distance from the station by active forms of transportation increases, individuals are more likely to select a station first. Young persons, females, car owners, and individuals leaving before 7:30 a.m. have an increased propensity to drive to the commuter train station. The station model indicates that travel time has a significant negative impact on station choice, whereas, presence of parking and increased train frequency encourages use of the stations. Copyright Springer Science+Business Media New York 2014
Sezi Cevik Onar - One of the best experts on this subject based on the ideXlab platform.
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A fuzzy multi attribute Decision Framework with integration of QFD and grey relational analysis
Expert Systems with Applications, 2019Co-Authors: Morteza Yazdani, Pascale Zaraté, Cengiz Kahraman, Sezi Cevik OnarAbstract:Objective : This paper proposes a multi attribute Decision support model in a supply chain in order to solve complex Decision problems. The paper provides a platform to ease Decision process through the integration of quality function deployment (QFD) and grey relational analysis (GRA) in demonstrating main supply chain drivers under fuzzy environment. Methodology : The proposed method is important because of several points: First of all, in a supply chain system, evaluation factors are not really independent and must be addressed in relation to the external factors such as customer requirements. Hence, we have applied QFD tool. Second, due to the constant uncertainty in the supply chain environment, fuzziness among the factors has to be considered. So, an interval valued fuzzy model was implemented. Third, to examine the proposed Decision system in reality, it was applied in Risk and Uncertain Conditions for Agriculture Production Systems (RUC-APS) project. Contribution : An integrated version of QFD and GRA is presented. It is assumed that QFD can act to measure optimal solutions based on the distance to ideal solutions. In an interval-valued fuzzy environment the enormous volume of computation by Euclidean distance doesn't allow Decision makers to obtain the results easily. This drawback is addressed using gray relational analysis. The gray relational coefficient is integrated to the fuzzy QFD to measure the distance of potential solutions from ideal solutions. This integration facilitates Decision making process in further problems once big data are available. Results : To obtain the importance degrees of logistic indicators in the supply chain, expert team considered the environmental, social & cultural, and economic factors as external dimension of the QFD. The other dimension of QFD includes supply chain drivers such as quality, environmental management system, supply chain flexibility, corporate social responsibility, transportation service condition, and financial stability. The Decision model is solved and the ranking of indicators is achieved. A sensitivity analysis helps to test and check the performance of the Decision model.
Z Paydar - One of the best experts on this subject based on the ideXlab platform.
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evaluation of potential irrigation expansion using a spatial fuzzy multi criteria Decision Framework
Environmental Modelling and Software, 2012Co-Authors: Yun Chen, Z PaydarAbstract:This paper presents a spatial Framework which can be used to perform multi-criteria assessments for different purposes. We tested the Framework on a case study of evaluating potential expansion for irrigated pasture in the Limestone Coast of South Australia. The core of the Framework is the fuzzy linguistic ordered weighted averaging (FLOWA) model which integrates and implements fuzzy quantifiers, Ordered Weighted Averaging (OWA) and Analytical Hierarchy Process (AHP) in the ArcGIS environment. Fifteen criteria were chosen, including groundwater, topography, landscape and soil attributes which significantly affect irrigated landuse. Criterion weights were determined at both objective and attribute levels using the AHP, and several scenarios were derived using the OWA operator for selected values of fuzzy quantifiers. The resultant evaluation map from the weighted linear combination (WLC) approach was then compared with regional present landuse map. Most currently irrigated areas are contained within the area predicted to be suitable for irrigated agriculture. Relatively large additional areas are also predicted to be suitable, suggesting potential expansion, or that factors including total regional water availability and enterprise-specific economics are at play. The Framework provides a useful tool with flexibility and efficiency. It enhances the existing AHP and OWA methods in the spatial context, and incorporates the uncertainty mechanism for guiding the multi-criteria Decision making. It is particularly valuable given its capability to generate and visualise a wide range of multi-criteria Decision scenarios, which can facilitate a better understanding of the spatial patterns of alternative landuse suitability potentials for future regional-scale landuse planning and water resource management.
