Journal of Operational Research in its Applications
Year:2015 | Volume:12 | Issue:1 (44)|Papers Count:8

In a collaborative process, several independent organizations work together in order to achieve a specified goal and each of them is responsible for executing a part of the relevant process. Since the relevant process is executing through the flow of information and knowledge among the participating organizations, so facilitating inter-organizational knowledge sharing is suggested as a way to improve these process performances. Creating facilities is impossible without identification of influential factors of inter-organizational knowledge sharing. The current paper tries to identify influential factors of knowledge sharing among public sector organizations using the study of existing literature and a case study of a collaborative process in tax determination process among tax administers, Bank and Municipality. The required information was gathered in two phases through semi-structured interview and questionnaire. Gathered data were analyzed by grounded theory and structural equation model technique. Findings show that “technical tools”, “knowledge nature”, and “similarity” are the most influential factors of inter- organizational knowledge sharing. Also setting a good operational protocol for collaborative processes can help to decrease risk of inter-organizational knowledge sharing and facilitating it.

One of the related models in the facility location literature for the hubs location is transfer point location problem (TPLP) to serve as collector points for customers who need the services of a facility. For example, demand for emergency services by patients is generated at a set of demand points that need the services of a central facility (such as a hospital). Patients are transferred to a helicopter pad (transfer point) at normal speed, and from there they are transferred to the facility at increased speed. In this paper, we have developed a new mixed integer-programming model for multi facility and multi transfer point location problem (MFMTPLP) with capacity constraint on facilities. Our model attempts to minimize the sum of the total time cost of patients travel in the relief chain and, patients that are not transferred to a facility. Each customer goes to one of the facilities directly or via one of the transfer points. A case study of district 4 of Tehran municipality is presented to illustrate the potential applicability of our model. Numerical experiments demonstrate the significance and applicability of the proposed model for actual decision making problems.

Modern marketing is based on customer segmentation; this is because the product-centric view has been in its place to the customer orientation. So, proficiency in establishing proper communication with the customer is essential to retain the key existing customers. Segmentation is one of the issues in the area of customer relationship management. For this purpose, using a model of customer segmentation is the opportunity for organizations to design and provide their valuable suggestions that fit the needs and wants of targeted sectors and thus improve their performance from a different perspective. The purpose of this study is to use an appropriate model for customers segmenting based on criteria such as the length of the customer relationship, recent exchanges, exchange frequency and monetary value of exchange. For clustering data in this article, combining particle swarm optimization algorithm with k-mean to overcome the problems as being sensitive to initial value has been used to trap in local optimum. Research findings show that customers who belong to the first cluster have high mean indicators of “the length customer relationship” and “just buy” and have less than the total customers’ average in indicators of “frequency of purchase” and “sale price”, and also customers who belong to the second cluster have high mean indicators of “just buy” and have less than the total customers’ average indicators of “length customer relationship”, “frequency of purchase” and “sale price”. Therefore, in terms of loyalty matrix, the first cluster customers are loyal customers, and in terms of the values matrix are uncertain customers, and so the second cluster customer in terms of loyalty matrix are new customers and in terms of the values matrix are uncertain customers. In the end it is clear that the algorithm designed to achieve more accurate clustering of customers is more performance than k-mean algorithm.

Today’s competitive pressures and customer recruitment costs are increasing for organizations, building personal relationship with customers, an opportunity to differentiate from competitors. Customer relationship management enables organizations through understanding customer and preferences, make long-term profitable relationships with customers as key to profitability in today’s dynamic market. Besides the advantage of customer relationship management, its risks should not be ignored. The purpose of this study is to identify the risks of customer relationship management projects at the sader bank branches in Rasht and determine the significance of these risks. In this regard, customer relationship management risks have first been recognized using library studies; those indexes then have been localized using localizing questionnaires. And with putting the risks in the most relevant category, a proposed hierarchy structure has been introduced for the risk priority. Finally, because of the plurality nature of these indices, F AHP has been used for the rank criteria and to prioritize, customer relationship management risks in the banking industry has been FVIKOR. The results demonstrated the importance of the different risks associated with each category that can assist managers in decision making for management of these risks.

Portfolio selection and its management are one of main areas in financial decision making. For this purpose, several models have been developed in a manner trying to improve and eliminate existing deficiencies in the existing model. As a result, the old one has been replaced by new one. Among the most important problems, the lack of considering indicators and multiple criteria in evaluating portfolio performance and also not regarding uncertainty can be mentioned. In this paper, to resolve the noted problems in portfolio selection and also to create more realistic models, the portfolio is selected according to combination of DEA and robust optimization. Finally, the developed model and the stated method in this paper are evaluated by real data, and the results are analyzed.

Facility location problems are important and applied topic in decision making problems with different effective factors are useful in their analysis. Costs and efficiency of system are the most important criteria in decision making. Considering these criteria in facility location models simultaneously, help decision makers to obtain more insight in analysis of the problem and finding results which cannot be achieved by other methods. In this study, the concept of efficiency using data envelopment analysis is utilized simultaneously with uncapacitated single- source multi-product facility location problem and a bi-objective mixed binary linear program is proposed for it. Then an LP-metric approach is used to solve it. Optimal solution of the model gives the optimum and efficient location-allocation pattern. Finally, a numerical example is presented to describe the impact of the new modeling.

Huge volume of data in today’s organizations and society is the result of human great gains in computer science. Today’s managers concerns are not lack of data, but useful use of available data and changing them to information and knowledge, to make the best decision with the lowest percentage of having errors. In recent decades, data mining techniques and multi-criteria decision making methods have helped managers in their decisions in various ways. However, these methods have weaknesses that have led to the continuous search of researchers for designing and using new methods of decision making. In the present study we have attempted to develop a hybrid model of data mining and multi-criteria decision making method to improve the results of both methods. In this context, multi-criteria decision making models can highlight the role of decision makers in the results of decision making. On the other hand, using data mining techniques allows execution of multi-criteria decision making methods on the large quantities of data. After the presentation of the proposed framework for the hybrid model of research, a case study with a suitable database has applied to determine the applicability of the proposed framework. This case study is about the clustering and ranking households in one of the provinces of Iran using the proposed hybrid model.

Basic data envelopment analysis (DEA) models have been developed for the non-negative data, but in the real world problems we face with the inputs and outputs which can take negative values. In this paper the multidirectional efficacy analysis (MEA) with negative data is discussed. It is shown that that MEA model in the presence of negative data also has favorable properties and can be used under different returns to scale assumptions. Some relations between the new MEA model in the performance measurement of the units with two other DEA models, RDM and MSBM, are explored. The new MEA model based on MSBM can be used for negative data. The results of applying the new model on a real data set, including negative data are also provided for comparison purpose.