In the present study, to achieve a comprehensive view of pressure conditions in the Black Sea region, sea level pressure data in Reanalysis II database in National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) were used. The data temporal resolution is daily and the spatial resolution is 2.5×2.5 degrees of arc. The investigated framework covers the areas between -30-70 degrees east longitude and 20-70 degrees north latitude and has dimensions equal 41×21 pixels consisting of 861 pixels. The studied period of time is 33-year (1979 to 2012) and includes 12419 days and 861 spatial pixels. Therefore, sea level pressure data matrix is 12419×861, which has 12419 temporal pixels and 861 spatial ones. In other words, the arrangement of the data is S-shaped. The rows of the matrix represent time while columns represent space. At the end, using the cluster analysis, six sea level pressure circulation patterns based on the spatial - temporal features were obtained: Red Sea trough is fall pattern; Iraq trough is spring pattern; Persian Gulf trough is transitional pattern; Persian Gulf strong trough is summer pattern; Caucasian high pressure is fall - winter pattern and the Caucasian Strong high pressure is winter pattern. In most of these patterns, intensive allobaric conditions can be observed in the troughs and ridges.

In this paper, we develop a novel framework for defining radial measures of centrality in complex networks. This framework is based on the combination of two approaches: social network analysis and traditional social science approach by considering both structure of relations and individual characteristics. It is always an important issue to detect communities in complex networks as efficiently as possible to understand both the structure and function of the networks and to interpret radial centrality measures. Therefore, we propose spectral clustering by determining the best number of communities as a prerequisite stage before finding radial measures. Based on the proposed framework, an algorithm to compute the closeness centrality in complex networks is developed. We test the proposed algorithm on Zachary’s karate club network, which is considerably used as a benchmark for community detection in a network. The preliminary results indicate that the new method is efficient at detecting both good inter-cluster closeness centrality and the appropriate number of clusters.

With the inception of high-speed internet data services and ever-growing technical advancement in manufacturing technology, the integration of production systems and the internet of things to produce different types of jobs via cloud computing has become possible. The internet-enabled advanced automatic production systems can be referred to as the cyber-physical production systems (CPPS). The use of CPPS via cloud computing and the internet of things (IoT) can offer high productivity and high flexibility for the production of jobs in a dynamic production environment with varying specifications. The aim of this paper is to present a generalized synergic framework between different production facilities locating at different geographical locations to realize an energy-saving and efficient cyber-physical production system for the production of different types of jobs in the context of the industry 4.0 and beyond. In addition to the above, the study also identifies a need to address large scale multi-objective optimization issues to make the best decisions for different combinatorial production scenarios by using CPPS that are functioning in smart production facilities at different geographical locations.

In the present paper, a novel intuitionistic fuzzy Multiple Attribute Decision Making (MADM) is proposed for modelling and solving analytical hierarchy process (AHP) problems with small amount of relationship among various criteria. Assigning a membership degree, fuzzy sets can model some uncertainty to the decision space. Intuitionistic fuzzy sets model the uncertainty more accurately associated with both membership and non-membership degree. Based on advantages of Intuitionistic fuzzy sets, this paper first uses IF-AHP to evaluate the weighting for each criterion and then develops an intuitionistic fuzzy DEMATEL method to establish contextual relationships among those criteria. Finally, an integrated IF-DEMATEL-AHP method is proposed and used for a case study for selecting managers in the automobile industry in Iran.

This paper presents an empirical investigation to study the relationship between bank debt guarantee and profitability, increase in banks resources and customer retention. The proposed study has been implemented in one of Iranian banks located in city of Zanjan, Iran over the period of 10 years. The study has used Pearson correlation test to examine three hypotheses of the survey. The results indicate that there were positive and meaningful relationship between bank debt guarantee and profitability (r = 0.863, P-value = 0.001), a positive relationship between bank debt guarantee and bank resources (r = 0.708 with P-value = 0.015) and there was a positive and meaningful relationship between bank debt guarantee and customer retention (r = 0.252 with P-value = 0.001).

By everyday development of storage and communicational and electronic media, there are significant amount of information being collected and stored in different forms such as electronic documents and document databases makes it difficult to process them, properly. To extract knowledge from this large volume of documental data, we require the use of documents organizing and indexing methods. Among these methods, we can consider clustering and classification methods where the objective is to organize documents and to increase the speed of accessing to required information. In most of document clustering methods, the clustering is mostly executed based on word frequency and considering document as a bag of words. In this essay, in order to decrease the number of features and to choose basic document feature, we use background knowledge and word clustering methods. In fact by using WordNet ontology, background knowledge and clustering method, the similar words of documents are clustered and the clusters with the number of words more than threshold are chosen and then their frequency of words is accepted as the effective features of document. The results of this proposed method simulation shows that the documents dimensions are decreased effectively and consequently the performance of documents clustering is increased.

This paper deals with a deterministic inventory model developed for deteriorating items having two separate storage facilities (owned and rented warehouses) due to limited capacity of the existing storage (owned warehouse) with linear time dependent demand (increasing) over a fixed finite time horizon. The model is formulated with infinite replenishment and the successive replenishment cycle lengths are in arithmetic progression. Partially backlogged shortages are allowed. The stocks of rented warehouse (RW) are transported to the owned warehouse (OW) in continuous release pattern. For this purpose, the model is formulated as a constrained non-linear mixed integer programming problem. For solving the problem, an advanced genetic algorithm (GA) has been developed. This advanced GA is based on ranking selection, elitism, whole arithmetic crossover and non-uniform mutation dependent on the age of the population. Our objective is to determine the optimal replenishment number, lot-size of two-warehouses (OW and RW) by maximizing the profit function. The model is illustrated with four numerical examples and sensitivity analyses of the optimal solution are performed with respect to different parameters.

Building Maintenance plays an important role throughout the building lifecycle from devis-ing conceptual plans to the end. Due to the high cost of building maintenance and the direct impact of maintenance effectiveness on the quality of life of building occupants, special attention must be devoted. One of the most important issues in this field is building maintenance contractor selection. This issue becomes even more critical in public buildings, such as hospitals, offices, and military centers. The purpose of this study is to present a method that can be used to select the contractor in such a way that the response robustness is high and the employed method is the most accurate one among other similar methods. To do this, the contractors are ranked by 7 multi-criteria decision-making methods. Then, the Spearman correlation coefficients are obtained for each pair of methods. When there is a significant difference between the outcomes of the methods, the output of each method is compared with the output of the Artificial Neural Network (ANN) model. The method with the least difference with the neural network output is taken as the superior method. After selecting the best method, a robustness analysis is performed on it to verify the stability of the answer. The proposed model is implemented on a real case study. Statistical analysis shows that the implementation of this method has increased the satisfaction of the residents.

This paper analyzes the choice of a PISA selector for a Hybrid Algorithm integrating it as a Multi-Objective Evolutionary Algorithm (MOEA) with a path-dependent search algorithm. The interaction between these components provides an efficient procedure for solving Multi-Objective Problems (MOPs) in operations scheduling. In order to choose the selector, we consider both NSGA and SPEA as well as their successors (NSGAII and SPEAII). NSGAII and SPEAII are shown to be the most efficient candidates. On the other hand, for the path-dependent search at the end of each evolutionary phase we use the multi-objective version of Simulated Annealing.

One of the most important methods for building good marketing strategy is to learn more about existing competitors, suppliers, etc. This paper presents an empirical investigation to study the relationship between wholesalers, bargaining pricing, new rivals and pricing strategy on preparing appropriate marketing strategy in meat market in city of shiraz, Iran. The proposed study designs a questionnaire in Likert scale and distributes it among 200 experts in this market. Cronbach alpha has been calculated as 97.3%, which validates the overall survey. The results of the implementation of Pearson correlation ratio indicate that there were some positive and meaningful relationships between all components of the survey with marketing strategy.