Nurnadiah Zamri

A Linear Assignment Method of Simple Additive Weighting System in Linear Programming Approach Under Interval Type-2 Fuzzy Set Concepts for MCDM Problem

The ranking phase is valuable to examines the final alternative rankings of decision making problems. Based on simple additive weighting (SAW) and linear programming (LP) within the context of interval type-2 fuzzy sets (IT2 FSs), we develop a linear assignment method to produce the final ranking order of all alternatives for interval type-2 fuzzy TOPSIS (IT2 FTOPSIS) method. A numerical example is used to check the efficiency and applicability of the proposed method. The results shows consistent outcomes of the decision making process. Thus, the proposed method offers an alternative, user-friendly method that is robust in the decision making framework.

A New Aggregating Phase for Interval Type-2 Fuzzy TOPSIS Using the ELECTRE I Method

Aggregating phase is considered as one of the important steps in interval type-2 fuzzy TOPSIS (IT2 FT) instead of ratings of alternatives under criteria and the importance weights of criteria and ranking of alternatives. However, some problems occur in aggregating phase of IT2 FT when it is have a large computational procedure due to the hardly defined in the second membership function. Therefore, we offer a more easier and practical in defining the new aggregating phase. Our proposed method is to establish a new aggregating phase for IT2 FT using the ELECTRE I method in the interval type-2 fuzzy set (IT2FS) concept. A numerical example is constructed to show the practicality and effectiveness of the proposed method.

A New Qualitative Evaluation for an Integrated Interval Type-2 Fuzzy TOPSIS and MCGP

Sometimes, information needed an objectively evaluation. It is hard to determine the value of some parameters because of their uncertain or ambiguous nature. However, most of the study neglected the qualitative evaluation. This paper aims to propose a new qualitative evaluation which considers three different aspects which are linguistic to crisp, the unconvinced decision and in between. This new qualitative evaluation is developed to produce an optimal preference ranking of an integrated fuzzy TOPSIS and multi-choice goal programming MCGP in interval type-2 fuzzy sets (IT2 FSs) aspects. An example is used to illustrate the proposed method. The results show that the qualitative evaluation in the new method is suitable for the integrated interval type-2 fuzzy TOPSIS and MCGP. Results are consistent with the numerical example. This new method offers a new dimension to type-2 fuzzy group decision-making environment.

A New Positive and Negative Linguistic Variable of Interval Triangular Type-2 Fuzzy Sets for MCDM

Fuzzy linguistic variable in decision making field has received significant attention from researchers in many areas. However, the existed research is given attention only in one side rather than two sides. Therefore, the aim of this paper is to introduce a new linguistic variable which considers both sides, positive and negative sides for symmetrical interval triangular type-2 fuzzy set (T2 FS). This new linguistic variable is developed in line with the interval type-2 fuzzy TOPSIS (IT2 FTOPSIS) method. Besides, a ranking value for aggregation process is modified to capture both positive and negative aspect for triangular. Then, this new method is tested using two illustrative examples. The results show that the new method is highly beneficial in terms of applicability and offers a new dimension to problem solving technique for the type-2 fuzzy group decision-making environment.

Flood control project selection using an interval type-2 entropy weight with interval type-2 fuzzy TOPSIS

Flood control project is a complex issue which takes economic, social, environment and technical attributes into account. Selection of the best flood control project requires the consideration of conflicting quantitative and qualitative evaluation criteria. When decision-makers’ judgment are under uncertainty, it is relatively difficult for them to provide exact numerical values. The interval type-2 fuzzy set (IT2FS) is a strong tool which can deal with the uncertainty case of subjective, incomplete, and vague information. Besides, it helps to solve for some situations where the information about criteria weights for alternatives is completely unknown. Therefore, this paper is adopted the information interval type-2 entropy concept into the weighting process of interval type-2 fuzzy TOPSIS. This entropy weight is believed can effectively balance the influence of uncertainty factors in evaluating attribute. Then, a modified ranking value is proposed in line with the interval type-2 entropy weight. Quantitative and qualitative factors that normally linked with flood control project are considered for ranking. Data in form of interval type-2 linguistic variables were collected from three authorised personnel of three Malaysian Government agencies. Study is considered for the whole of Malaysia. From the analysis, it shows that diversion scheme yielded the highest closeness coefficient at 0.4807. A ranking can be drawn using the magnitude of closeness coefficient. It was indicated that the diversion scheme recorded the first rank among five causes.Flood control project is a complex issue which takes economic, social, environment and technical attributes into account. Selection of the best flood control project requires the consideration of conflicting quantitative and qualitative evaluation criteria. When decision-makers’ judgment are under uncertainty, it is relatively difficult for them to provide exact numerical values. The interval type-2 fuzzy set (IT2FS) is a strong tool which can deal with the uncertainty case of subjective, incomplete, and vague information. Besides, it helps to solve for some situations where the information about criteria weights for alternatives is completely unknown. Therefore, this paper is adopted the information interval type-2 entropy concept into the weighting process of interval type-2 fuzzy TOPSIS. This entropy weight is believed can effectively balance the influence of uncertainty factors in evaluating attribute. Then, a modified ranking value is proposed in line with the interval type-2 entropy weight. Quantita...

A new linguistic scale for Interval Type-2 Trapezoidal Fuzzy Number based Multiple Criteria Decision Making method

Decision making is a process for managing the decision problem for human beings that use linguistic information. However, it is sometimes limited by the fact that the linguistic models use only positive linguistic terms, which may not reflect exactly what the experts mean. The previous studies neglected the equilibrium concept (i.e., two sides of a matter) that takes its roots from the Yin Yang theory. The Yin Yang theory philosophically deals with two sides of things in the universe, and focuses on the balance of the two sides. Thus, the purpose of this paper is to introduce the new linguistic scales of positive and negative Interval Type-2 Trapezoidal Fuzzy Number (IT2TrFN) to the decision environment of interval type-2 fuzzy context for solving Interval Type-2 Fuzzy Technique for Order Preference by Similarity to Ideal Solution (IT2FTOPSIS) problems. This new linguistic scales reacts to the subjective judgments from the experts where the lowest of the scale and the highest of the scale are equally strong. In decision making, it is rare to find the negative scale, where it actually does not mean wrong or corrupt. Here, the negative data represents a hypothesis that can make it well-separated. The positive and negative are relatives. Along with considering the context of the new linguistic scale, this paper employs a hybrid averaging approach with ambiguity method and type-reduction method to formulate a collective decision environment. This hybrid averaging approach helps to reduce values of Interval Type-2 Fuzzy Sets (IT2FS) to a crisp number. The feasibility and applicability of the proposed methods are illustrated with an example.

Ranking of causes lead to road accidents using a new linguistic variable in interval type-2 fuzzy entropy weight of a decision making method

A linguistic data is a variable whose value is naturally language phase in dealing with too complex situation to be described properly in conventional quantitative expressions. However, all the past researchers on linguistic variables used positive fuzzy numbers in expressing meaning of symbolic word. It seems that positive and negative numbers were never put concurrently in defining linguistic variables. Accordingly, we intend to construct a new positive and negative linguistic variable in interval type-2 fuzzy entropy weight for interval type-2 fuzzy TOPSIS (IT2 FTOPSIS). This paper uses a new linguistic variable in interval type-2 fuzzy entropy weight to capture the problems on reducing number of road accidents due to all the previously mentioned methods had no discussion about ranking of factors associated with road accidents. Specifically the objective of this paper is to establish rankings of the selected factors associated with road accidents using a new positive and negative linguistic variable and interval type-2 fuzzy entropy weight in interval type-2 fuzzy TOPSIS. This new method is hoped can produce an optimal preference ranking of alternatives in accordance with a set of criterion wise ranking in selection of causes that lead to road accidents. The proposed method produces actionable results that laid the decision-making process. Besides, it does not require a complicated computation procedure and will be beneficial to decision analysis. Language: en

Road traffic accidents models using threshold levels of fuzzy linear regression

It has been hypothesized that number of road traffic accidents and road casualties are increased in line with the rapid recent increase in the variables of registered vehicles, population and road length. However the effects of these variables toward road traffic accidents are still inconclusive. Therefore, this paper develops models based on the variables which can be used to determine number of road traffic accidents in Malaysia. In order to explain the effects of these variables to road traffic accident, fuzzy linear regression models with three threshold levels, h=0.1, 0.5, 0.9 are examined. Historical data from the year 1974 to 2007 were collected to test performances of the models. The results show that by applying a multi-variable approach to fuzzy linear regression, the model provides not only crisp output but also output range for road traffic accident in Malaysia. The fuzzy linear regression model with threshold level h=0.1 was outperformed the other two models. The variables of registered vehicles and population were notable predictors to number of road traffic accidents in Malaysia.

A New Concept of Fuzzy TOPSIS and Fuzzy Logic in a Multi-criteria Decision

In reality, humans usually uncertainty or vague in expressing their preference or votes based on crisp number or scale. Much of the information on which decision are based is uncertain, the methods can be used to support the system’s decision is to use Fuzzy Multi-Criteria Decision Making (FMCDM). This method was chosen because it can selecting the best alternative from a number of alternatives Criteria. Fuzzy Multi-Criteria Decision Making (FMCDM) is a method of decision-making to determine the best alternative from a number of alternatives based on certain criteria. The criteria usually in the form of action, rules or standards used in decision making. The lack of capability to handle vagueness in the decision making, has been main weakness of Fuzzy TOPSIS. Thus, the purpose of this paper is to introduce Fuzzy TOPSIS and z-number to several criteria fuzzy group decision making (FMCDM). Fuzzy TOPSIS is used to determine the alternative most suitable in relation to different selection criteria and z-number to present experts reability, this method can choose the best alternative from a number of alternatives based on some specific criteria. A numerical example on FMCDM is used to describe the efficiency of the proposed method.

A Fuzzy TOPSIS with Z-Numbers Approach for Evaluation on Accident at the Construction Site

The construction industry has been identified as one of the most risky industries where involves fatalities accidents. Identifying the causes that lead to the accidents implicates a lot of uncertain and imprecise cases. Z-numbers involve more uncertainties than Fuzzy Sets (FSs). They provide us with additional degree of freedom to represent the uncertainty and fuzziness of the real situations. In this paper, we introduce a Fuzzy TOPSIS (FTOPSIS) with Z-numbers to handle uncertainty in the construction problems. Five criteria and six alternatives are used to evaluate the causes of workers’ accident at the construction sites. Data in form of linguistic variables were collected from three authorised personnel of three agencies. From the analysis, it shows that the FTOPSIS with Z-numbers provides us with an another useful way to handle Fuzzy Multi-Criteria Decision Making (FMCDM) problems in a more intelligent and flexible manner due to the fact that it uses Z-numbers with FTOPSIS.