A supplier selection using a hybrid grey based hierarchical clustering and artificial bee colony

Selection of one or a combination of the most suitable potential providers and outsourcing problem is the most important strategies in logistics and supply chain management. In this paper, selection of an optimal combination of suppliers in inventory and supply chain management are studied and analyzed via multiple attribute decision making approach, data mining and evolutionary optimization algorithms. For supplier selection in supply chain, hierarchical clustering according to the studied indexes first clusters suppliers. Then, according to its cluster, each supplier is evaluated through Grey Relational Analysis. Then the combination of suppliers’ Pareto optimal rank and costs are obtained using Artificial Bee Colony meta-heuristic algorithm. A case study is conducted for a better description of a new algorithm to select a multiple source of suppliers

A Hybrid Grey Relational Analysis and Nondominated Sorting Genetic Algorithm-II for Project Portfolio Selection

Project selection and formation of an optimal portfolio of selected projects are among the main challenges of project management. For this purpose, several factors and indicators are simultaneously examined considering the terms and conditions of the decision problem. Obviously, both qualitative and quantitative factors may influence the formation of a portfolio of projects. In this study, the projects were first ranked using grey relational analysis to form an optimal portfolio of projects and to create an expert system for the final project selection. Because of the fuzzy nature of the environmental risk of each project, the environmental risk was predicted and analyzed using the fuzzy inference system and failure mode and effect analysis based on fuzzy rules. Then, the rank and risk of each project were optimized using a two-objective zero-one mathematical programming model considering the practical constraints of the decision problem through the nondominated sorting genetic algorithm-II (NSGA-II). A case study was used to discuss the practical methodology for selecting a portfolio of projects

A Grey-Based Fuzzy ELECTRE Model for Project Selection

Project selection is considered as an important problem in project management. It is multi-criteria in nature and is based on various quantitative and qualitative factors. The main purpose of this paper is to present a new rank-based method for project selection in outranking relation. According to this approach, decision alternatives were clustered in the concordance matrix and the discordance matrix through the ELECTRE model based on intuitionistic trapezoidal fuzzy numbers. Then, the two matrices were integrated and ranked using grey relational coefficients and the Minkowski space distance. The results of the model were compared with grey relational projection method with intuitionistic trapezoidal fuzzy number. To illustrate the proposed methodology, a case study was conducted to select National Iranian Oil Company projects

Sense Marketing, Experiential Marketing, Customer Satisfaction and Repurchase Intention

Sense marketing is focus on five senses, offering customers with pleasures or excitement. If managed well, sense marketing is able to distinguish company and product, stimulating customers' sense experiences and delivering values to customers. Product and service marketing in the past usually adapted low-price strategy and eventually leaded to battles in prices. However, time is changing, those traditional marketing techniques could not represent the main stream in this competitive new age. Experience economy arrived after knowledge economy. In the new era of experience economy, the aim of marketing demand is “creating valuable experience”.  “Experience/ Experiential marketing” concept, proposed by Schmitt, has been a trend around the world.  Surveying comprehensively for the past studies, sense in experiential marketing is rarely explored with its relations versus consumers' repurchase intentions. This study explored consumers of Mos Burger in Kaohsiung City and probed into how sense marketing could raise consumer loyalty to Mos Burger, and furthermore affecting repurchase intention. The purpose of this study is to explore different constructs of “sense marketing” in experiential marketing, and discovering the correlations among the variables of “customer satisfaction” and “repurchase intention” using regression model. The result proved that sense of experiential marketing is an indispensable factor, and every construct of sense marketing also showed significant correlation between consumer satisfaction and repurchase intention. Keywords: Experience Marketing, Sense Experience, Satisfaction, Repurchase Intentio

A Hybrid Grey based Two Steps Clustering and Firefly Algorithm for Portfolio Selection

Considering the concept of clustering, the main idea of the present study is based on the fact that all stocks for choosing and ranking will not be necessarily in one cluster. Taking the mentioned point into account, this study aims at offering a new methodology for making decisions concerning the formation of a portfolio of stocks in the stock market. To meet this end, Multiple-Criteria Decision-Making, Data Mining, and Multi-objective Optimization were employed. First, candidate stocks were clustered using two-step clustering method. Available stocks in each cluster were independently ranked using grey relational analysis. Firefly algorithm was employed for Pareto analysis of risk and ranking. The results of clustering in the stocks revealed that all candidate stocks were not placed in one cluster. The results of robustness analysis employed in ranking method verified the accuracy of calculations in the grey relational analysis through stock repetition of candidates in each cluster

An FMEA analysis using grey theory and grey rough sets

This paper presents a hybrid method for detecting the most important failure items as well as the most effective alternative strategy to cope with possible events. The proposed model of this paper uses grey technique to rank various alternatives and FMEA technique to find important faults. The implementation of the proposed method has been illustrated for an existing example on the literature. The results of this method show that the proposed model has been capable of detecting the most trouble making problems with fuzzy logic and finds the most important solution strategy using FMEA technique

Selecting an optimal mixed products using grey relationship model

This paper presents an integrated supplier selection and inventory management using grey relationship model (GRM) as well as multi-objective decision making process. The proposed model of this paper first ranks different suppliers based on GRM technique and then determines the optimum level of inventory by considering different objectives. To show the implementation of the proposed model, we use some benchmark data presented by Talluri and Baker [Talluri, S., & Baker, R. C. (2002). A multi-phase mathematical programming approach for effective supply chain design. European Journal of Operational Research, 141(3), 544-558.]. The preliminary results indicate that the proposed model of this paper is capable of handling different criteria for supplier selection

A Hybrid Grey Based KOHONEN Model and Biogeography-Based Optimization for Project Portfolio Selection

The problem of selection and the best option are the main subject of operation research science in decision-making theory. Selection is a process that scrutinizes and investigates several quantitative and qualitative, and most often incompatible, factors. One of the most fundamental management issues in multicriteria selection literature is the multicriteria adoption of the projects portfolio. In such decision-making condition, manager is seeking for the best combination to build up a portfolio among the existing projects. In the present paper, KOHONEN algorithm was first employed to build up a portfolio of the projects. Next, each portfolio was evaluated using grey relational analysis (GRA) and then scheduled risk of the project was predicted using Mamdani fuzzy inference method. Finally, the multiobjective biogeography-based optimization algorithm was utilized for drawing risk and rank Pareto analysis. A case study is used concurrently to show the efficiency of the proposed model