A Fuzzy Logic Approach to Prove Bullwhip Effect in Supply Chains

The bullwhip effect in nowadays Supply Chains has become a major source of problems and has attracted supply chain scientists attentions. This paper explores the concept of bullwhip effect in supply chains throughout a completely new approach. Assuming all demands are fuzzy in supply chain, fuzzy If-Then rules are used to show the bullwhip effect. Application of fuzzy logic is due to the fuzzy nature of supply chain problems. The new approach can be the source of inspiration for new solutions to the bullwhip effect in supply chains base on fuzzy logic and fuzzy If-Then rules. Fuzzy time series are widely used in this paper. First for data generation, we apply a modified version of Hwang fuzzy time series with a neural network for defuzzification and finally to show the bullwhip effect, we use Lee fuzzy time series which is based on Fuzzy If-Then rules, Genetic Algorithm and Simulated Annealing

A Fuzzy Logic Approach to Prove Bullwhip Effect in Supply Chains

The bullwhip effect in nowadays Supply Chains has become a major source of problems and has attracted supply chain scientists attentions. This paper explores the concept of bullwhip effect in supply chains throughout a completely new approach. Assuming all demands are fuzzy in supply chain, fuzzy If-Then rules are used to show the bullwhip effect. Application of fuzzy logic is due to the fuzzy nature of supply chain problems. The new approach can be the source of inspiration for new solutions to the bullwhip effect in supply chains base on fuzzy logic and fuzzy If-Then rules. Fuzzy time series are widely used in this paper. First for data generation, we apply a modified version of Hwang fuzzy time series with a neural network for defuzzification and finally to show the bullwhip effect, we use Lee fuzzy time series which is based on Fuzzy If-Then rules, Genetic Algorithm and Simulated Annealing

A type-2 fuzzy system model for reducing bullwhip effects in supply chains and its application in steel manufacturing

AbstractThe purpose of this paper is to evaluate and reduce the bullwhip effect in fuzzy environments by means of type-2 fuzzy methodology. In order to reduce the bullwhip effect in a supply chain, we propose a new method for demand forecasting. First, the demand data of a real steel industry in Canada is clustered with an interval type-2 fuzzy c-regression clustering algorithm. Then, a novel interval type-2 fuzzy hybrid expert system is developed for demand forecasting. This system uses Fuzzy Disjunctive Normal Forms (FDNF) and Fuzzy Conjunctive Normal Forms (FCNF) for the aggregation of antecedents. An interval type-2 fuzzy order policy is developed to determine orders in the supply chain. Then, the results of the proposed method are compared with the type-1 fuzzy expert system as well as the type-1 fuzzy time series method in the literature. The results show that the bullwhip effect is significantly reduced; also, the system has less error and high accuracy

A Novel Dual Factor Fuzzy Time Series Forecasting based on new Fuzzy sets and Interval Definition by Evolution Strategies

This paper proposes a new dual factor time-invariant fuzzy time series method that is capable of forecasting stock marketPrice Index. The proposed approach uses a new fuzzy logic relationship definition. According to the utilized membershipdegrees used to define the fuzzy relationships, each datum may belong to two distinct intervals rather than only one interval.This assumption, which has not been considered in the other studies, contributes to better forecasting results. In addition, anappropriate meta-heuristic algorithm for continuous solution schemes, namely evolution strategies (ES), is utilized to identifythe appropriate interval lengths. The proposed approach has been tested on TAIFEX index. The computational results showedthat the proposed approach outperforms the former studies

Image Segmentation: Type–2 Fuzzy Possibilistic C-Mean Clustering Approach

Image segmentation is an essential issue in image description and classification. Currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. Moreover, there are many uncertainties and vagueness in images, which crisp clustering and even Type-1 fuzzy clustering could not handle. Hence, Type-2 fuzzy clustering is the most preferred method. In recent years, neurology and neuroscience have been significantly advanced by imaging tools, which typically involve vast amount of data and many uncertainties. Therefore, Type-2 fuzzy clustering methods could process these images more efficient and could provide better performance. The focus of this paper is to segment the brain Magnetic Resonance Imaging (MRI) in to essential clusters based on Type-2 Possibilistic C-Mean (PCM) method. The results show that using Type-2 PCM method provides better results

A comprehensive fuzzy multi-objective model for supplier selection process

Supplier selection is understood as one of the key processes in strategic decision making level in Supply Chains (SC). This paper develops a comprehensive multiple products and multiple suppliers model for this process. Moreover, various targets are discussed and analyzed in the form of objectives, in addition to related constraints. Such model development is fulfilled in a real-world situation with wide ranges of uncertainties. In this paper, a fuzzy decision making model is presented. In the proposed Fuzzy Multiple Objectives Decision Making (FMODM) model, all goals, constraints, variables and coefficients are fuzzy. It is shown that with the application of the fuzzy methodology, the complex multi-objective problem is converted to a single one that can be solved and interpreted easily.</p

Developing fuzzy expert systems models for supply chain complex problem: a comparison with linear programming

Supply chain management (SCM) system endeavors to achieve global optimum solution(s) in supply network problems. SCM systems are mostly large-scale and are recognized as complex systems. This paper concentrates on supply chain system modeling with fuzzy expert systems (FES). The FES is developed based on the knowledge and information of the experts in an automotive supply chain. Then the results are compared with those of the fuzzy linear programming models in this area. The results of the FES show its superiority over linear programming models especially in CPU times and satisfaction of decision makers of results and their understandability