Modern Research in Decision Making

Abstract In this paper, the considered set covering issue is a stance of location-allocation issue in which the residents of each the eighteen district of region 6 of Tehran, have definite requirements to receive services (medical first aid) from some of emergency centers. We want to arrange so that at least one center, in each district, provides services to people who have been exposed to incidents. In this regard and to emplacement of these relay centers, a developed multi-objective model is used that has objectives such as minimizing the establishment cost of relay stations, maximizing the total covered population and minimizing the total distances between demand locations and allocated service providers. Since, each of the centers has only an ambulance and these ambulances being out of reach during providing services, possible limitations have been used in this model. So, in this research a combination of multi-objective decision making (MODM) methods and set covering problem (SCP) is used to specify the location of relay centers in 18 districts of region 6 in Tehran. The chosen solution is simulated annealing method that is performed using Matlab software. At the end, the model is implemented through the Ultra Innovative algorithm of particle mass optimization to compare to the answers of the Simulated Annealing algorithm. Results show that both algorithms give the same answers for the considered locations.

Abstract Determining the optimal and sustainable strategies to protect critical and sensitive systems, is one of the main objectives of the organization. To achieve this goal, efficient and stable strategies must be determined. In this paper first, modeling the optimal strategies to defend and attack in the stationary state is presented, provided that the defender to deceive the attacker provides a number of false targets. In the static model, considering the probability of a successful attack, attacker capability in identifying false targets, reliability block diagram and game theory approach in finding the balance point, a nonlinear programming model is proposed to determine the amount of investment defend and attack. Then, according to the results of the static model, system dynamics and implications of evolutionary game theory, a new and dynamic approach to determine sustainability strategies of defense and attack is presented. According to the proposed model, evolutionary stable strategy for the defender, attacker and system is examined. Finally, presented model is illustrated for an applied case and final findings are analyzed.

Abstract Bank facilities, as one of the most important functions of banks, are very important to the survival of banks. The importance of identifying facility consumers is more than depositor customers. In previous studies, the importance of such customers has not been well considered. This study aims at designing recommender systems for predicting the customer behavior in receiving facilities, using clustering and classification algorithms. The designed system helps banks to identify types of facility demanding costumers, and to make decisions related to each customer cluster in order to make maximum profits and reduce the cost related to loans and receivables. The system accurately predicts each customer's cluster and also forecasts account balance of each customer group according to the current data set. Based on Analyses carried out on the clusters and related data sets of clients, is the recommender system is provided for to branch users. In order to design the recommender system, clustering and classification outputs were also deeply validated. The margin of error was very low and therefore, data mining outputs were verified and used to develop the final system as well as a user interface for the final utilization of recommender system which was presented and validated by the supervisors of bank. Supervisors also provided some suggestions for improving the final solution.

Abstract Pallet management system is crucial part of logistic system in pallet intensive industries such as automotive manufacturing. In practice pallet management system is in charge of delivering right amount of pallet to the right customer at the right time. In automotive manufacturing industry, pallet management system is mostly used in delivering parts and components to the OEM. System fleet size, system responsiveness and cycle time are the most important criterias in assessing system performance. The objective of this study is to develope a model for pallet management system under risk pooling approach. We start with clarifying pallet management system components using returnable transport items scientific literature review and then we propose a model for pallet management system of Saipa corporation supply chain (an Iranian automotive factory) under risk pooling approach using simulation technique. MATLAB software is used as the tool for simulation. The method of data gathering is interviewing and also reviewing document and literature and the method of data analysis is discrete event simulation

Abstract Organizations have life stages like living organisms. One day they are born, raised and die another day. This article seeks to examine the situation of a staff life cycle in Social Sciences faculty of Khalij Fars university. The main purpose of this research is understanding the life cycle of the faculty members, then can be predict the future path of staff life cycle. In this regard, by search in the literature, 23 organic growth model were identified. By studying these models, proposed a related model for a didactic, educational and collegiate center. After by developing this model, the next step is to expanded a four-stage approach to gathering and analyzing the data. At the end of this algorithm introduced two indicators including fuzzy index and mathematics index. Based on these index the life cycle of faculty members in Social Sciences faculty of Khalij Fars university is at the end of the first stage, i.e. childhood in the period 1382 to 1393.

Abstract Supplier selection plays a key role in the organizations because the costs of the raw materials constitute the main part of the final product cost. Today, selecting a suitable supplier is one of the most important purchasing decisions. It generally depends on various criteria. To effectively manage the strategically-important purchasing activity, suitable methods and criteria must be selected for the problem. This paper proposed a “data envelopment analysis with efficient and inefficient frontiers” approach to evaluate and select the best supplier in the presence of undesirable outputs and imprecise data. The data envelopment analysis with efficient and inefficient frontiers considered two efficiencies for the decision-making: One efficiency was measured with respect to the efficiency frontier and is called the best relative efficiency or the optimistic efficiency. The other one was measured with respect to the inefficiency frontier and is called the worst relative efficiency or the pessimistic efficiency. In addition, this approach was utilized to provide new overall performance measures to score the suppliers in the presence of undesirable outputs and imprecise data. Compared to the traditional approach, data envelopment analysis with efficient and inefficient frontiers approach identified the best supplier more easily and accurately. Two numerical examples were provided to illustrate the application of the proposed approach.

Abstract In this study, a longitudinal study on cluster production strategy in two time periods with an interval of five years is discussed. Using the longitudinal and transverse analysis has dramatically expanded in recent decades and become well known among researchers. Despite the cross-sectional study, In longitudinal study, data collected from a representative sample in different times. To conduct a longitudinal study, two different clustering was conducted on production strategies used in Iran’s factories, in two time periods. Therefore, the data in the two years 2010 and 2016 period were used from production factories. Population were similar in two period. Clustering validity was assessed by discriminant analysis, and then, changes in production objectives surveyed using Euclidean distance, multivariate data analysis and analysis of variance were used. Finally, changes in cluster’s production objectives are discussed. Based on the results there are similarities and differences between two groups are in two time periods.

Abstract In evaluating of organizations and institutions, one of the most important goals is the ranking of the units based on the performance of them. According to the commonly used method of decision-making in data envelopment analysis, it may lead to multiple efficient units that choose the best of units, is the main problems in data envelopment analysis. In this paper, a new method as the common weights has been studied in details. Two techniques (common weight and TOPSIS) have been studied on real data of the 17 banks in Semnan province. The results indicate that the two methods have achieved perfect ratings. By comparing the two methods, results have been showed that the common weights rating is closer to reality. In addition, common weight is a non-parametric method and has received a better ranking based on the performance (relative) efficient. Also, in the common weight all of the decisions making have taken together, while in the TOPSIS mode is not.