International Journal of Research in Industrial Engineering
Volume:1 Issue: 1, Spring 2012

In this paper, a method for ranking efficient DMUs based on TOPSIS has been proposed. The difference between the distance of the center of gravity of all efficient DMUs to the ideal point and the anti-ideal point after and before deleting efficient DMUs one by one is the criteria of ranking efficient DMUs. In this paper, the proposed method is compared with AP (input oriented), MAJ (input oriented), AP(output oriented), MAJ (output oriented) models and norm l1 method. This comparison shows that the proposed method is better than the above-mentioned models. The proposed method is also always feasible and simpler in comparison with other methods.

Evaluating the performance of the different departments in an organization or evaluating and comparing the performance of a single department over different time points may boost the organizational efficiency and the involved industry. Making the most efficient use of the existing resources to realize all the expected outcomes. Therefore, in their projected plans, the efficiency enhancement specialists have always attempted to measure the existing efficiency of an organization and estimate the room for improvement. A Data Envelopment Analysis (DEA) approach would be an appropriate model to measure efficiency and provide answers to the above questions. In order to estimate the efficiency of a research unit over a period of years, first different indicators were identified and then through a questionnaire their priorities were determined. Finally, using the CCR model the performance efficiency and the ranking of the research center over the years were conducted.

Data envelopment analysis (DEA) is a methodology for identifying efficient frontier of decision making units (DMUs) with multiple outputs and inputs. Context-dependent DEA refers to a DEA approach where a set of DMUs are evaluated against a particular evaluation context. Each evaluation context represents an efficient frontier composed by DMUs in a specific performance level. Context-dependent DEA measures the attractiveness and the progress for each DMU. Current paper extends the context-dependent DEA by ranking all units on the basis of attractiveness and progress measures. The method is applied to measure the attractiveness and progress of 49 bank branches, and ranking them with Context-dependent DEA.

CRM or customer relationship management is a continuous process consisting of creating and applying knowledge and market intelligence to establish and maintain the relationships with customers that have the most efficiency. The customer relationship management (CRM) software is an important application in e-commerce. The concept of customer relationship management has come into the consideration in today's world. Nowadays, the organizations put their customers in the heart of all their activities, and their marketing and sales strategies are being revised based on this. On the other hand, it should be kept in mind that the most crucial concern of all today firms and enterprises can be recognized as how to communicate with the customers and the customer relationship management. Today's organizations consider themselves committed to provide the bests for their customers, and the goal of customer relationship management is to develop permanent and long-term dependencies between company and customers. Sometimes even the CRM is known as a customer-oriented strategy that deals with the most valuable assets of a business, which are the customers. Two important influencing factors on the performance of customer relationship management include the customer' profile and the customer participation.

Fuzzy multi-objective zero-one linear programming (FMOZOLP) has many applications in various fields such as assembly line balancing, assignment, project portfolio selection and maximal covering location problems. In many of the existing methods for solving FMOZOLP problems, membership degree of different points of a fuzzy number is not considered or by performing α-cut, points with membership function more than or equal to α, are included in calculations. However, even in this case, membership degree of these points has no effect on optimal solution. In this paper, in addition to modifying defects and failures of Yu and Li method [1] in solving fuzzy zero-one linear programming problems, we develop a novel approach to solve FMOZOLP problems considering membership degree of coefficients. Finally, an illustrative example for the project portfolio selection is included to compare results obtained by the proposed approach with results obtained by the other fuzzy methods.