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Following the increasing growth of urbanization in recent decades in Iran, housing has become one of the most critical issues in the country. In this regard, mass production of housing has received more attention, and residential complexes can be considered a physical manifestation of the idea of mass housing in cities. Operational efficiency in residential construction production systems is evaluated based on average house completion time, the number of houses under construction, and processing time of activities. However, these systems are prone to non-uniformity problems and suspensions resulting from different variables, such as adverse weather conditions, workplace accidents, fluctuations in house demand, and rework. The purpose of this research is to show the effect of reprocessing on the manufacturing process.  In this study, the rework parameter and the variables of frequency, duration, and time of call-back have been considered. Also, the effects of these parameters on tangible performance criteria have been investigated. In this regard, we apply the combined approach of discrete-event simulation and computational modeling; then, we compare the results. Measurements show that the systems fragmented by repeated and short repetitions while referring to early are in optimal performance.

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Developing realistic models for the evaluation of sustainable supply chains has turned into a major challenge facing managers. The decision-making approaches proposed here consist of two stages. At the first stage, a dynamic-network data envelopment analysis (DNDEA) model is established for the first time, wherein the current efficiency of a business can be influenced by its prior social and environmental activities, as two main dimensions of sustainability. The second stage correspondingly presents, for the first time, a model in which total efficiency is calculated based on the value of historical data. Sensitivity analysis is exploited to determine the more effective factors of sustainability in efficiency evaluations. To validate the model, it is used to assess the sustainability of the suppliers of an auto spare parts manufacturer. The study results reveal that the model is well-able to evaluate the performance of dynamic network structures, with a very high discriminating power. Following the implementation of this model, only the supplier(KARAN) is found to reach the efficiency limit, and  SIRIN S.N. is recognized as the most inefficient supplier with an efficiency score of 0.6409. The sensitivity analysis outcomes demonstrate that the least amount of efficiency change is related to the economic pillar; however, the rising trend in wage costs, compared with other economic factors, brings a better effect on augmenting the efficiency of some inefficient suppliers. The highest efficiency changes during sensitivity analysis are further observed in both social and environmental dimensions. Therefore, it is claimed that investing in these two pillars can have a significant impact on the efficiency of suppliers.
 

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Data envelopment analysis (DEA), as a well-established nonparametric method, is used to meet efficiency evaluation purposes in many businesses, organizations, and decision units. This paper aims to present a novel integrated approach to fuzzy interpretive structural modeling (FISM) and dynamic network data envelopment analysis (DNDEA) for the selection and ranking of sustainable suppliers. First, suppliers' efficiency values in a supply chain are determined, using the dynamic network data envelopment analysis (DNDEA) model developed for this purpose. Then, a heuristic method is presented based on the fuzzy interpretive structural modeling (FISM) to find a common set of weights (CSWs) for the variables involved. Depending on the data conditions, two approaches, viz. centralized and decentralized, are proposed for efficiency measurement. To illustrate the model's capability, the proposed methodology is further applied to the real data of a company, named Nirou Moharekeh Industries (NMI). The results of a study on 12 suppliers within the DNDEA model accordingly reveal that one unit (i.e. KARAN) obtains an efficient value, but an inefficient score is observed in 11 units, whose technical efficiency value is in the range of 0.6409 to 0.9983. After utilizing the weights gained from the heuristic method, the efficiency value of the most inefficient supplier (that is, SIRINS.N.) dwindles from 0.6409 to 0.6319.

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Over the past few decades, there has been a growing need to address the limitations of the Data Envelopment Analysis methodology, particularly the issue of freely selecting weights. As a result, common weight models have emerged and expanded. This article aims to provide a comprehensive overview of CSW methods, analyzing papers and bibliometric information through a systematic literature review. In this study, a total of 116 articles on CSW published between 1991 and 2022 were carefully selected and reviewed. These contributions were categorized based on specific features related to the computational technique or the main purpose of the procedure. The findings revealed that uncertain models had the highest share among the articles in the field of CSW. Furthermore, the Journal of Expert Systems with Applications emerged as the leading journal in terms of the number of publications on CSW models in DEA. The analysis of the bibliometric information of the articles was carried out using advanced software tools, including R-Studio and VOS Viewer... This review offers valuable insights and discussion, which can guide future research endeavors in this field. By addressing the limitations of DEA and exploring various CSW methods, this study contributes to the advancement of knowledge and understanding in this area.
 

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As one of the main challenges of the 21st century supply chain, complexity has become a crisis, costly, and difficult for many supply chain managers. However, few studies have prioritized this strategy. This study aimed to investigate the effect of supply chain complexity on competitiveness performance in the Iran Khodro Company. To achieve this goal, the relationship between different sources of complexity (upstream complexity, domestic production, and downstream complexity) and competitiveness performance is examined using a conceptual model. The statistical population of this study included all Iran Khodro experts, whose number reached 218 in 2022. Owing to the limitations of the statistical community, all members were selected as a sample. The collection tools for this study included two standard questionnaires: the supply chain and Porter's competitiveness performance. The content validity of the questionnaires was confirmed by experts, its construct validity was confirmed by confirmatory factor analysis, and the reliability of the questionnaires was confirmed using Cronbach's alpha coefficient and the combined reliability index. The collected data were analyzed using the structural equations in the LISREL software. The results of hypothesis testing show that, in general, supply chain complexity has a significant inverse effect on competitiveness performance.
 

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This study aims to provide a comprehensive evaluation of the technical efficiency and scale of 15 suppliers of a production unit from 2020 to 2022. The research utilizes Data Envelopment Analysis (DEA) to analyze two scale assumptions that are generally employed: constant returns to scale (CRS), and variable returns to scale (VRS). The variables for the study were selected based on indicator availability, representation principles, and expert opinions. Investment, nonoperating expense cost, and operating costs (including raw material costs, wages, and overhead costs) were considered as inputs, while net sales and return on investment were regarded as outputs. The results indicate that only two suppliers were operating at the optimal scale, and the scale efficiency of the supply chain displayed an increasing dispersion over the mentioned period. However, the net technical efficiency of the supply chain demonstrated an increasing concentration, suggesting an overall reduction in the gap between suppliers and an improvement in pure technical efficiency within the manufacturing unit's supply chain. This study provides valuable insights into the differences between suppliers from a macro perspective and offers guidance for manufacturing units looking to expand their supply chain.