Eda Bolturk

A novel interval-valued neutrosophic AHP with cosine similarity measure

Neutrosophic Logic (Smarandache in Neutrosophy neutrosophic probability: set, and logic, American Research Press, Rehoboth, 1998) has been applied to many multicriteria decision-making methods such as Technique for Order Preference by Similarity to an Ideal Solution, Višekriterijumsko kompromisno rangiranje Resenje, and Evaluation based on Distance from Average Solution. Interval-valued neutrosophic sets are subclass of neutrosophic sets. Interval numbers can be used for their truth-membership, indeterminacy-membership, and falsity-membership degrees. The angle between the vector representations of two neutrosophic sets is defined cosine similarity measure. In this paper, we introduce a new Analytic Hierarchy Process (AHP) method with interval-valued neutrosophic sets. We also propose an interval-valued neutrosophic AHP (IVN-AHP) based on cosine similarity measures. The proposed method with cosine similarity provides an objective scoring procedure for pairwise comparison matrices under neutrosophic uncertainty. Finally, an application is given in energy alternative selection to illustrate the developed approaches.

Fuzzy Methods for Demand Forecasting in Supply Chain Management

Forecasting the future demand is crucial for supply chain planning. In this chapter, the fuzzy methods that can be used to forecast future by historical demand information are explained. The examined methods include fuzzy time series, fuzzy regression, adaptive network-based fuzzy inference system and fuzzy rule based systems. The literature review is given and the methods are introduced for the mentioned methods. Also two numerical applications using fuzzy time series are presented. In one of the examples, future enrollments of a university is forecasted using Hwang, Chen and Lee’s study and in the other example a company’s oil consumption is predicted using Singh’s algorithm. Finally, the forecasting accuracy of the methods is determined by using Mean Absolute Error (MAE).

Fuzzy Forecasting Methods for Energy Planning

For energy planning, forecasting the energy demand for a specific time interval and supply of a specific source is very crucial. In the energy sector, forecasting may be long term, midterm or short term. While traditional forecasting techniques provide results for crisp data, for data with imprecision or vagueness fuzzy based approaches can be used. In this chapter, fuzzy forecasting methods such as, fuzzy time series (FTS), fuzzy regression, adaptive network-based fuzzy inference system (ANFIS) and fuzzy inference systems (FIS) as explained. Later, an extended literature review of fuzzy forecasting in energy planning is provided. Finally, a numerical application is given to give a better understanding of fuzzy forecasting approaches.

Fuzzy Shewhart Control Charts

Process Control is the active correction of a process based on the results of process monitoring. Once the process monitoring tools have detected an assignable cause, this cause is removed to bring the process back into control. This chapter presents the process control techniques under fuzziness. Variable and attribute control charts are extended to their fuzzy versions.