Showing 3 results for Taghavifard
Volume 8, Issue 2 (9-2018)
Abstract
Knowing customer behavior patterns, clustering and assigning them is one of the most important purpose for banks. In this research, the five criteria of each customer, including Recency, Frequency, Monetary, Loan and Deferred, were extracted from the bank database during one year, and then clustered using the customer's K-Means algorithm. Then, the multi-objective model of bank service allocation was designed for each of the clusters. The purpose of the designed model was to increase customer satisfaction, reduce costs, and reduce the risk of allocating services. Given the fact that the problem does not have an optimal solution, and each client feature has a probability distribution function, simulation was used to solve it. In order to determine the neighbor optimal solution of the Simulated Anneling algorithm, neighboring solutions were used and a simulation model was implemented. The results showed a significant improvement over the current situation. In this research, we used Weka and R-Studio software for data mining and Arena for simulation for optimization. The results of this research were used to develop Business Intelligence software for customers in one of the private banks of Iran.
Volume 19, Issue 2 (8-2015)
Abstract
Supplier selection and determination of the lot sizing is an important component of production and logistics management for many companies. Therefore, after the select of preferred suppliers at the first should obtaine the optimize order of each of the suppliers that is the purposes and constraints of determiners. One of the most effective techniques, which can provide optimal solutions with different targets, is multi-objective programming model.Purpose of this study is to design an efficient multi-objective model of optimal to determine the lot sizing to each supplier.This work is done with designation of multi- objective model, to achieve minimizing the cost of the chain, such as the cost of purchasing, storage, transportation, etc., and also maximize the quality of materials that purchased from suppliers. Finally the model is solved by using the meta-heuristic method, multi-objective Non-dominated Sorting Genetic Algorithm II (NAGA-II), and also in order to validate the model using meta-heuristic particle swarm optimization algorithm (PSO) has been solved and results compared with the first method.
Volume 19, Issue 3 (9-2015)
Abstract
Nowadays, organizations tend to aggregate and increase the knowledge resources of work groups. In this paper, we introduce a framework to classify knowledge-sharing mechanisms, especially in project-based organizations. Prior research concentrated on identifying dimensions of knowledge sharing mechanisms such as personalization vs. codification, and individualization vs. institutionalization. Personalization strategy aims at encouraging individuals to share their knowledge directly. Information technology plays less important role, as it is only supposed to facilitate communication and knowledge sharing among the members of an organization. Codification mechanisms focus on collecting and storing codified knowledge in previously designed electronic databases to make it accessible to the organization. Individualization mechanisms facilitate the sharing of knowledge at individual level while institutionalization mechanisms facilitate knowledge sharing at group level. Taking these dimensions into account and based on an empirical study of MAPNA Corp, this paper presents a framework to utilize knowledge sharing mechanisms in organizations while they have different size, nature of work, and geographical dispersion.