Journal of Industrial Engineering and Management Studies

Abstract Most organizations and companies have hierarchical structures, and appropriate models are required to measure the efficiency of this kind of network systems. Data envelopment analysis (DEA) is a well-known method introduced for measuring the relative efficiency of a set of decision-making units (DMUs) that use multiple inputs to produce multiple outputs. Conventional DEA models usually generate misleading results while evaluating the performance of network systems. The present study aims at developing suitable models for measuring the efficiency of hierarchical structures using the centralized and non-cooperative leader-follower game models. In the proposed method, the divisional efficiencies (within an organization) and the overall efficiency of the organization are calculated. The proposed models are applied to assess the performance of 20 schools in Iran. The results of the two proposed models show that none of the schools are efficient, suggesting that these schools do not optimally utilize their resources. The application of the results of the proposed models enables managers to identify inefficient sub-units and develop strategies to improve their performance.

Abstract Studies in the field of the tourism industry are often carried out using classical statistics. While the use of spatial statistics in tourism helps a lot in identifying existing models and trends in the industry and discovering them. Due to the interaction between economic, political, environmental and social elements in tourism activities, analysis methods of spatial statistics can be used by identifying information between samples and using large volumes of information by not indicating the independence of the data, can obtain suitable tourism clusters and help identify the appropriate tourism model in Iran. This study aims at to design a model of the tourism industry in Iran with the approach of the spatial correlation structure. The research method was qualitative and quantitative. To identify the variables affecting the tourism industry, the qualitative meta-analysis method, and to collect the required data in spatial statistics, the data of the Cultural Heritage and Tourism Organization in the summer of 2008-2018 have been used. To determine the model of tourism clusters Moran statistics and to study tourism clusters in all provinces of the country, the best interpolation method of tourism has been determined. ArcGIS software was used to analyze the research data. The results of data analysis showed that tourism data has a spatial autocorrelation and a cluster and regular model in the statistical period of summer 2008 to 2018. The most cluster model of tourism using the Moran spatial autocorrelation index is related to the summer of 2008 with 0.991 and the lowest cluster model of tourism is related to the summer of 2014 with the amount of 0.976. Also, the results of the study of the distribution of tourism direction in the provinces of the country in this statistical period showed that the predominant direction of tourism is with a slight change from northwest to southeast.

Abstract Data Envelopment Analysis (DEA) is one of the methods most widely used for measuring the relative efficiency of DMUs in the world today.  The efficiency evaluation of the network structure opens the “black box” and considers the internal structure of systems. In this paper, a three-stage network model is considered with additional inputs and undesirable outputs and obtains the efficiency of the network, as interval efficiency in presence of the imprecise datum. The proposed model of this paper simulates a factory in the factual world with a production area, three warehouses and two delivery points. This factory is taken into consideration as a dynamic network and a multiplicative DEA approach is utilized to measure efficiency. Given the non-linearity of the models, a heuristic method is used to linearize the models. Ultimately, this paper concentrates on the interval efficiency to rank the units. The results of this ranking demonstrated that the time periods namely, (24) and (1) were the best and the poorest periods, respectively, in context to the interval efficiency within 24 phases of time.