Customer Relationship Management (CRM) has been an important part of enterprise decision-making and management. In this regard, Decision Tree (DT) models are the most common tools for investigating CRM and providing an appropriate support for the implementation of CRM systems. Yet, this method does not yield any estimate of the degree of separation of different subgroups involved in analysis. In this research, we compute three decision-making models in SMEs, analyzing different decision tree methods (C&RT, C4.5 and ID3). The methods are then used to compute ME and VoE for the models and they were then used to calculate the Mean Errors (ME) and Variance of Errors (VoE) estimates to investigate the predictive power of these methods. These decision tree methods were used to analyze small- and medium-sized enterprises (SME’s) datasets. The paper proposes a powerful technical support for better directing market tends and mining in CRM. According to the findings, C&RT shows a better degree of separation. As a result, we recommend using decision tree methods together with ME and VoE to determine CRM factors.

Investigating the Customer Satisfaction Measurement (CSM) plays an important role in determining the range of customer needs and expectations resulting from delivered products or received services. In this research, a novel approach is proposed for measuring the customer’s satisfaction measurement. Due to ambiguity and lack of information related to evaluation criteria, in the proposed model, the customer feedbacks are considered as linguistic terms and due to the dominance of non –linear relations on behaviors and judgments of human, the result is obtained using a Fuzzy Neural Network. In continuation, roles of the fuzzy inference system for customer’s satisfaction are defined and determined for different conditions of customer’s judgments. Applicability of the proposed model has been successfully implemented through a case study for investigating the customer’s satisfaction on the basis of both qualitative and quantitative inputs.

Recently, researchers have focused on how to minimize the negative effects of industrial activities on environment. Consequently, they work on mathematical models, which minimize the environmental issues as well as optimizing the costs. In the field of supply chain network design, most managers consider economic and environmental issues, simultaneously. This paper introduces a bi-objective supply chain network design, which uses fuzzy programming to obtain the capability of resisting uncertain conditions. The design considers production, recovery, and distribution centers. The advantage of using this model includes the optimal facilities, locating them and assigning the optimal facilities to them. It also chooses the type and the number of technologies, which must be bought. The fuzzy programming converts the multi objective model to an auxiliary crisp model by Jimenez approach and solves it with ?-constraint. For solving large size problems, the Multi Objective Differential Evolutionary algorithm (MODE) is applied.

There have been many unexpected natural disasters such as earthquake, flood, etc. in developing countries, which have created catastrophic incidents and we need to do appropriate planning for relief to reduce the possible casualties. Such actions normally face different challenges such as damages on transportation infrastructures including roads, bridges, etc. One of the primary actions for such crises management is associated with facility location for relief distribution centers. This paper presents a multi-objective mathematical problem and applies it for a real-world case study in northern region of Iran. The study uses Lp metric to handle different objectives and fuzzy programming is used to cope with uncertainty. The preliminary results indicate that the proposed study of this paper has been able to provide efficient results.

Clustering is absolutely useful information to explore data structures and has been employed in many places. It organizes a set of objects into similar groups called clusters, and the objects within one cluster are both highly similar and dissimilar with the objects in other clusters. The K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms are the most popular clustering algorithms for their easy implementation and fast work, but in some cases we cannot use these algorithms. Regarding this, in this paper, a hybrid model for customer clustering is presented that is applicable in five banks of Fars Province, Shiraz, Iran. In this way, the fuzzy relation among customers is defined by using their features described in linguistic and quantitative variables. As follows, the customers of banks are grouped according to K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms and the proposed Fuzzy Relation Clustering (FRC) algorithm. The aim of this paper is to show how to choose the best clustering algorithms based on density-based clustering and present a new clustering algorithm for both crisp and fuzzy variables. Finally, we apply the proposed approach to five datasets of customer's segmentation in banks. The result of the FCR shows the accuracy and high performance of FRC compared other clustering methods.

Nowadays, the advance and enhance in competitive area, convert the supply chain management into one of the most important issues for industries, organization, and firms. Increasing the quality of products, decreasing the costs, and representing the satisfying service are the primary objectives of organization and managers. Apart from that, the amount of CNGs (such as CO2) has been raised by industrial activities. Therefore, the concern of air pollution motivates managers and researchers to consider this issue in the process. This paper represents a multi objective supply chain network fuzzy programming, which is multi product, multi period, multi-layer, and has reverse product network. Operational risks are considered as deficiency in suppliers’ units and production center. The model’s duty is to choose the optimal suppliers based on different factors such as selling price, the average of deficiency and transportation costs. In order to solve the model, the Jimenez and TH approach are used and for large-scale problems, the paper uses the NSGA-II algorithm.

Distribution network design is an important issue in supply chain management and plays an important role in making new market development. Because of JIT philosophy, most of managers now have focused on designing appropriate distribution networks. Thus, categorizing distributors and selecting the best ones are crucial for companies. This paper provides a new method to categorize and select distributors. The fuzzy Adaptive Resonance Theory (ART) algorithm is utilized to categorize distributors according to their similarity. To improve the performance of the algorithm, we train the algorithm using the past data. Finally, a numerical example is illustrated to examine the validity of the proposed algorithm.