Li-Wei Lee

Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method

Type-2 fuzzy sets involve more uncertainties than type-1 fuzzy sets. They provide us with additional degrees of freedom to represent the uncertainty and the fuzziness of the real world. In this paper, we present an interval type-2 fuzzy TOPSIS method to handle fuzzy multiple attributes group decision-making problems based on interval type-2 fuzzy sets. We also use some examples to illustrate the fuzzy multiple attributes group decision-making process of the proposed method. The proposed method provides us with a useful way to handle fuzzy multiple attributes group decision-making problems in a more flexible and more intelligent manner due to the fact that it uses interval type-2 sets rather than traditional type-1 fuzzy sets to represent the evaluating values and the weights of the attributes.

Fuzzy multiple attributes group decision-making based on the ranking values and the arithmetic operations of interval type-2 fuzzy sets

In this paper, we present a new method to handle fuzzy multiple attributes group decision-making problems based on the ranking values and the arithmetic operations of interval type-2 fuzzy sets. First, we present the arithmetic operations between interval type-2 fuzzy sets. Then, we present a fuzzy ranking method to calculate the ranking values of interval type-2 fuzzy sets. We also make a comparison of the ranking values of the proposed method with the existing methods. Based on the proposed fuzzy ranking method and the proposed arithmetic operations between interval type-2 fuzzy sets, we present a new method to handle fuzzy multiple attributes group decision-making problems. The proposed method provides us with a useful way to handle fuzzy multiple attributes group decision-making problems in a more flexible and more intelligent manner due to the fact that it uses interval type-2 fuzzy sets rather than traditional type-1 fuzzy sets to represent the evaluating values and the weights of attributes.

Handling forecasting problems based on two-factors high-order fuzzy time series

In our daily life, people often use forecasting techniques to predict weather, economy, population growth, stock, etc. However, in the real world, an event can be affected by many factors. Therefore, if we consider more factors for prediction, then we can get better forecasting results. In recent years, many researchers used fuzzy time series to handle prediction problems. In this paper, we present a new method to predict temperature and the Taiwan Futures Exchange (TAIFEX), based on the two-factors high-order fuzzy time series. The proposed method constructs two-factors high-order fuzzy logical relationships based on the historical data to increase the forecasting accuracy rate. The proposed method gets a higher forecasting accuracy rate than the existing methods.

Temperature prediction and TAIFEX forecasting based on fuzzy logical relationships and genetic algorithms

In this paper, we present a new method for temperature prediction and the TAIFEX forecasting based on fuzzy logical relationships and genetic algorithms. The proposed method constructs two-factors high-order fuzzy logical relationships based on the historical data and uses genetic algorithms to adjust the length of each interval in the universe of discourse for temperature prediction and the TAIFEX forecasting to increase the forecasting accuracy rate. The proposed method gets a higher forecasting accuracy rate than the existing methods.

Temperature prediction and TAIFEX forecasting based on high-order fuzzy logical relationships and genetic simulated annealing techniques

In this paper, we present a new method for temperature prediction and the Taiwan Futures Exchange (TAIFEX) forecasting, based on high-order fuzzy logical relationships and genetic simulated annealing techniques, where simulated annealing techniques are used to deal with mutation operations of genetic algorithms. We use genetic simulated annealing techniques to adjust the length of each interval in the universe of discourse to increase the forecasting accuracy rate. The proposed method gets higher forecasting accuracy rates than the existing methods.

Fuzzy multiple attributes group decision-making based on ranking interval type-2 fuzzy sets

In this paper, we present a new method to deal with fuzzy multiple attributes group decision-making problems based on ranking interval type-2 fuzzy sets. First, we propose a new method for ranking interval type-2 fuzzy sets. Then, we propose a new method for fuzzy multiple attributes group decision-making based on the proposed ranking method of interval type-2 fuzzy sets. We also use some examples to illustrate the fuzzy multiple attributes group decision-making process of the proposed method. The proposed method is simpler than the methods presented in Chen and Lee (2010a, 2010b) for fuzzy multiple attributes group decision-making based on interval type-2 fuzzy sets. It provides us with a useful way for dealing with fuzzy multiple attributes group decision-making problems based on interval type-2 fuzzy sets.

Fuzzy decision making based on likelihood-based comparison relations of hesitant fuzzy linguistic term sets and hesitant fuzzy linguistic operators

In this paper, we propose a new fuzzy decision making method and propose a new fuzzy group decision making method based on the proposed likelihood-based comparison relations of hesitant fuzzy linguistic term sets and the proposed hesitant fuzzy linguistic weighted average (HFLWA) operator, the proposed hesitant fuzzy linguistic weighted geometric (HFLWG) operator, the proposed hesitant fuzzy linguistic ordered weighted average (HFLOWA) operator, and the proposed hesitant fuzzy linguistic ordered weighted geometric (HFLOWG) operator of hesitant fuzzy linguistic term sets. The proposed fuzzy decision making method can overcome the drawback of Rodriguez et al.s method (2012) and Wei et al.s method (2014) for fuzzy decision making, which cannot distinguish the preference order of alternatives in some situations. The proposed fuzzy group decision making method is more flexible than Rodriguez et al.s method (2013) for fuzzy group decision making because it considers different hesitant fuzzy linguistic operators for fuzzy group decision making. The proposed methods provide us with a useful way for decision making in fuzzy environments.

Fuzzy Multiple Criteria Hierarchical Group Decision-Making Based on Interval Type-2 Fuzzy Sets

In this paper, we present a new method for handling fuzzy multiple criteria hierarchical group decision-making problems based on arithmetic operations and fuzzy preference relations of interval type-2 fuzzy sets. Because the time complexity of the proposed method is O(nk), where n is the number of criteria and k is the number of decision-makers, it is more efficient than Wu and Mendels method, whose time complexity is O(mnk), where m is the number of α-cuts, n is the number of criteria and k is the number of decision-makers. Moreover, the proposed method can overcome another drawback of Wu and Mendels method, i.e., it can handle evaluating values represented by nonnormal interval type-2 fuzzy sets. The proposed method provides us with a useful way to handle fuzzy multiple criteria hierarchical group decision-making problems.

Group decision making using incomplete fuzzy preference relations based on the additive consistency and the order consistency

In this paper, we present a new method for group decision making using incomplete fuzzy preference relations based on the additive consistency and the order consistency with consistency degrees to overcome the drawbacks of Lees method [15], where Lees method cannot obtain the correct preference order of alternatives in some situations. First, we estimate unknown preference values of incomplete fuzzy preference relations based on the additive consistency. Then, we construct modified consistency matrices of experts which satisfy the additive consistency and the order consistency simultaneously. We also prove some properties of the constructed modified consistency matrices. Finally, based on the constructed modified consistency matrices of experts, we present a new method for group decision making. The proposed method provides us with a useful way for group decision making using incomplete fuzzy preference relations based on the additive consistency and the order consistency with consistency degrees.

Multiattribute decision making based on interval-valued intuitionistic fuzzy values

In this paper, we present a new multiattribute decision making method based on the proposed interval-valued intuitionistic fuzzy weighted average operator and the proposed fuzzy ranking method for intuitionistic fuzzy values. First, we briefly review the concepts of interval-valued intuitionistic fuzzy sets and the Karnik-Mendel algorithms. Then, we propose the intuitionistic fuzzy weighted average operator and interval-valued intuitionistic fuzzy weighted average operator, based on the traditional weighted average method and the Karnik-Mendel algorithms. Then, we propose a fuzzy ranking method for intuitionistic fuzzy values based on likelihood-based comparison relations between intervals. Finally, we present a new multiattribute decision making method based on the proposed interval-valued intuitionistic fuzzy weighted average operator and the proposed fuzzy ranking method for intuitionistic fuzzy values. The proposed method provides us with a useful way for multiattribute decision making based on interval-valued intuitionistic fuzzy values.