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Purpose: In classical data envelopment analysis models, a production system for measuring performance is considered as a black box and no attention is paid to the internal structure of decision-making units in the process of evaluation performance. However, it is important to consider the internal structure of the units to identify sources of inefficiency to calculate efficiency. On the other hands, the observed values of input and output data in real world problems are sometimes imprecise and vague. Therefore, in this paper, the network data envelopment analysis model is used in order to evaluate the performance of decision-making units with a two-stage structure in a fuzzy environment in which input and output values are displayed in terms of triangular fuzzy numbers. Methodology: To solve the fuzzy two-stage data envelopment analysis model, the fuzzy arithmetic approach is used and a lexicographic optimization method for calculating the fuzzy efficiency of processes and the fuzzy efficiency of the system is proposed. Findings: The main advantage of the proposed approach over the exsiting approaches is that it solves fewer models for finding fuzzy efficiency. Originality/Value: The application of the proposed model is explained by evaluating the performance of 24 insurance companies. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms are suitable methods for solving complex problems. Many changes have thus been made on their original structures in order to obtain more desirable solutions. Parallelization is a suitable technique to decrease the runtime of the algorithm, and therefore, to obtain solutions with higher quality. In this paper, a new algorithm is proposed with two approaches, which is based on a parallelization technique with shared memory architecture. In the proposed algorithm, the search space is firstly decomposed into multiple equal and independent subspaces. Then, a subtask is performed on each subspace simultaneously in a parallel manner which leads to providing more qualified solutions. Splitting the search space into smaller subspaces causes the algorithm to find optimal solutions in each region in an easier way. The algorithm RAPSO is improved with applying a new acceleration coefficient which has been named IRAPSO. In the proposed algorithm, the IRAPSO is used as the subtask. For the sake of testing the proposed algorithm, fourteen well-known benchmarks of numerical optimizing problems are inspected. Then, the proposed algorithm is compared with algorithms PPBO and PSOPSO that were both based on the island model. The results of the proposed algorithm are much better than those of the other two algorithms. [ABSTRACT FROM AUTHOR]

A number of software programms are developed in several releases. Before developing any new release, a set of requirements is suggested for inclusion in the release. Having multiple constraints, it is impossible to develop all the requirements proposed in the next release. The presence of competing companies, replication of product ideas, shortening of the development time and lack of project funding will reduce the cost of developing a release. Developer teams should select a subset of the proposed requirements for development that would provide their clients with the highest amount of satisfaction despite the deadline limitations or cost constraints. The existence of conflicting goals and other constraints makes this choice very complicated. In this paper, an algorithm is introduced which is based on a fuzzy inference system to determine the suitability of each requirement for development in the next release. The proposed algorithm, rather than the developer team, takes the responsibility to select the optimal subset of requirements for the development of the next release. Experimental results of the proposed algorithm are then compared with the results of the genetic algorithm. The subset selected by the proposed algorithm provides much more satisfaction than the genetic algorithm. [ABSTRACT FROM AUTHOR]

Two-stage production systems are often encountered in many real applications where the production process is divided into two processes. In contrast to the conventional data envelopment analysis (DEA) models, two-stage DEA models take the operations of the internal processes into account. A number of studies have used two-stage DEA models in order to evaluate the performance of decision making units (DMUs) having a network structure. In this paper, we use a non-radial DEA model called the network slacks-based measure (NSBM) model to measure the efficiency of a system with a multi-period two-stage structure. Then we describe the properties of the proposed model in details. Moreover, we shall decompose the overall efficiency of the system over a number of time periods as a weighted average of the efficiency in each period. The efficiency of the stages, in respect to the entire periods shall be decomposed in terms of the weighted average efficiency of the stages in each period. Finally, the real data of Mellat bank branches in Tehran extracted from extant literature is used to illustrate the proposed approach. [ABSTRACT FROM AUTHOR]

Evaluating the efficiency and the performance of decision making units (DMUs) at different time periods is one of the most critical and important issues of managers. Data envelopment analysis (DEA) is a powerful non-parametric technique to measure the relative efficiency of a set of DMUs where each DMU consumes multiple inputs to produce multiple outputs. In many DEA applications, DMUs are considered as systems with a two-stage structure. In these situations, two-stage DEA models are used to measure the efficiencies of these systems. In many of such systems, the simultaneous presence of two stages is not necessary for the final product and the shortcoming of one stage is compensated by another stage. Therefore, this paper will use compensatory property of the sum operator and will propose the additive model to measure the multi-period efficiency of these systems under the constant returns to scale (CRS) assumption. In addition, based on the obtained efficiencies, the new efficiency changes Indexes (ECIs) related to the whole system and the first and second stages between two periods will be proposed that have circularity property. Furthermore, ECI of the whole system (and stages) for two periods is defined as the difference between the efficiencies in these periods. Moreover, positive changes (or negative changes), or unchanged in the efficiency of stages will be concluded by the positive changes (or negative changes), or unchanged of the whole system. Finally, the data of 21 non-life insurance industry in Taiwan are used to describe our suggested model that extracted from the extant literature. [ABSTRACT FROM AUTHOR]

In this paper, we suggest a method for determining the restarting time for web services to increase availability, known as rejuvenation. We consider different parameters such as number of users, maximum service request number, response time, and throughput of a web service to determine its restarting time. Software rejuvenation is an effective technique to counteract software aging in continuously running applications such as web service-based systems. In these systems, web services are allocated based on the needs of the receivers and facilities of servers. One of the challenges while assigning web services is selecting the appropriate server to reduce faults. Since the selection of a server among candidates while maintaining the optimal quality of service is an NP-hard problem, metaheuristics seem to be suitable. In this paper, we propose dynamic software rejuvenation as a proactive fault-tolerance technique based on the whale optimization algorithm. The threshold for the rejuvenation of each of the web services is considered and training is done based on the features of the service providers as well as the needs of the receivers. The whale optimization algorithm with the criterion of movement radius is utilized for flexibility of web service provider selection. Here, we detect and rejuvenate systems that required rejuvenation before the occurrence of a fault. The simulation results reveal that our strategy can decrease the failure rate by an average of 30 percent in comparison with state-of-the-art strategies and improve the system availability in web services. [ABSTRACT FROM AUTHOR]

The memory management in the operating system includes a part called the page replacement algorithms. Replacement algorithms in environments that require high-performance computing are considered as an important issue. For example, these algorithms are very important in cache management in microprocessors, web caching, replication strategies in distributed information systems and so on. Due to the important role of replacement algorithms in overcoming the problem of performance caused by the difference in processor speeds and memory, many algorithms were proposed. Most of them are the developed schemes of the least frequently used (LFU) and least recently used (LRU). Although most proposed designs can solve the LRU and LFU defects, they are implemented in a difficult way. The most important advantage of LRU and LFU is their simple implementation. This research proposes a page replacement algorithm that is simple to implement. The algorithm, which uses three parameters to cluster cache pages, is called the fuzzy page replacement algorithm. Whenever a miss occurs, it selects a page of the cluster with the lowest priority which has the smallest Euclidean distance with its center and then exits the cache. The most significant advantage of the algorithm is using the FCM (fuzzy c-means) algorithm to cluster pages, resulting in better replacement and hence higher memory performance. [ABSTRACT FROM AUTHOR]