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Selecting resilient-sustainable suppliers can improve sustainability status and reduce supply chain disruption. This study aims to design a model for selecting resilient-sustainable suppliers in the supply chain of the Shahid Ghandi Corporation Complex. For this purpose, after reviewing the theoretical literature, 76 and 50 indicators were identified for evaluating sustainable suppliers and resilient suppliers, respectively. These indicators were investigated by supply chain experts in Shahid Ghandi Corporation Complex and, then, 15 indicators were determined to be suitable for each of the sustainable and resilient suppliers. A questionnaire was distributed among the supply chain experts of Shahid Ghandi Corporation Complex and the resilient-sustainable supplier selection model was confirmed using confirmatory factor analysis (CFA) based on the 136 questionnaires gathered from the participants. Sustainability indicators were classified into three economic, social, and environmental dimensions, and resilience indicators were divided into three categories of absorptive capacity, adaptive capacity, and restorative capacity. The results showed that the economic dimension had the first rank, the environmental dimension the second rank, and the indices of adaptation capacity, restorative capacity, social capacity and absorption capacity in choosing the sustainable-resilient supplier model were the next priorities, respectively.

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Today, new technologies have changed the global financial panorama and communities. Due to the advancement of new technologies and the competitiveness of the company, the nature of innovation has changed. In order to take full advantage of the potential of technological transformation, companies must incorporate platforms into their operations. The purpose of this have a look at became to designing and explaining the model of technological platforms capabilities within the cosmetics enterprise. The prevailing take a look at is carried out in terms of motive and descriptive in phrases of the way to acquire information. The information evaluation method locations this study within the discipline of qualitative research of interpretive type. The study population in the present study consists of all university professors in the field of business management and information technology management. In order to design the model of the present study, interviews were conducted based on purposive and theoretical sampling methods to the extent of theoretical saturation. In this study, in order to evaluate the validity of the interview from the approach of credibility or credibility criteria including the use of negative case strategies, triangulation, rich explanation and reliability approach including the use of third parties and also repetition of the coding process based on the validity model. Qualitative research by Lincoln and Guba (1982) was used. In evaluating the reliability of the interview, two strategies of using third party as well as repetition of coding were used (Lincoln and Guba, 1985). The analysis of the interview data using the data-based method was based on the systematic approach of Strauss and Corbin (1998), based on three stages of open, axial and selective coding. Data coding showed the extraction of 16 selected codes, 60 axial codes and 248 open codes which were classified based on causal conditions, central phenomenon, interfering factors, contextual factors, strategies and consequences.

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The present study aims at designing a cold multi-cycle supply chain based on a multi cross-dock system taking into account uncertainty. In the first step, we identified the factors and variables of the model. In the second, by selecting the study period through designing data collection forms and using the documents reviewing methodologies, the raw data required to measure the final indicators were collected and processed in the project model. Then, they were analyzed considering the research topic and using the techniques of genetic algorithm and particle swarm optimization. The primary objective function is minimizing the cost of transportation and warehousing throughout the supply chain, the second minimizing the total operation time and the number of vehicles within the supply chain, and the third maximizing the product freshness time. Also meta-heuristic optimization methods (strongly adjustable) were adopted to deal with the travel time of suburban vehicles. We also provide an example of the performance of optimization models for a small-sized sample. The computational results showed that longer travel time and further distance do not necessarily increase costs. In fact, it is possible to distribute the products with the right number of trucks at an optimal cost at the right time.

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In this study, an alternative method is proposed based on recursive deep learning with limited steps and prepossessing, in which the data is divided into A unit classes in order to change a long short term memory and solve the existing challenges. The goal is to obtain predictive results that are closer to real world in COVID-19 patients. To achieve this goal, four existing challenges including the heterogeneous data, the imbalanced data distribution in predicted classes, the low allocation rate of data to a class and the existence of many features in a process have been resolved. The proposed method is simulated using the real data of COVID-19 patients hospitalized in treatment centers of Tehran treatment management affiliated to the Social Security Organization of Iran in 2020, which has led to recovery or death. The obtained results are compared against three valid advanced methods, and are showed that the amount of memory resources usage and CPU usage time are slightly increased compared to similar methods  and the accuracy is increased by an average of 12%.

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Hubs are facilities that can decrease the cost of many-to-many distribution systems by acting as an interconnector between the demand and supply nodes. This type of facility can reduce the number of direct links needed in a logistics network. Hub location problems (HLP) have been discussed by many authors for more than four decades, and different approaches have been developed for modeling and solving this problem. We propose a fuzzy type I and II programming approach for a new model presented in the literature, i.e., the single allocation ordered median problem. The level of flow among the nodes will be considered as a fuzzy parameter. In the fuzzy type I approach, a linear programming problem with fuzzy parameters is used, while for the fuzzy type II approach, the rules of interval arithmetic are developed to simplify the problem to the fuzzy type I case. Finally, we apply our method on Kalleh Dairy Co. data of transportation as a case study and compare crisp and fuzzy situations. We show that the results of the fuzzy approach could be 2% better than the crisp approach and also discuss the pros and cons of fuzzy type I and type II approaches.

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Location theory is one of the most important topics in optimization and operations research. In location problems, the goal is to find the location of one or more facilities in a way such that some criteria such as transportation costs, customer traveling distance, total service time, and cost of servicing are optimized. In this paper, we investigate the goal Weber location problem in which the location of a number of demand points on a plane is given, and the ideal is locating the facility in the distance Ri , from the i-th demand point. However, in most instances, the solution of this problem does not exist. Therefore, the minimizing sum of errors is considered. The goal Weber location problem with the lp  norm is solved using the stochastic version of the LBFGS method, which is a second-order limited memory method for minimizing large-scale problems. According to the obtained numerical results, this algorithm achieves a lower optimal value in less time with comparing to other common and popular stochastic optimization algorithms. Note that although the investigated problem is not strongly convex, the numerical results show that the SLBFGS algorithm performs very well even for this type of problem.
 

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Competing in today's marketplaces necessitates mobilizing resources and improving critical capabilities, one of which is agile marketing, or the capacity to quickly and cost-effectively react to changing international markets. The goal of this research is to create a model of agile marketing capability in the health tourism business so that it can progress. The agile marketing capacity model in health tourism was built and presented utilizing the data theory of the foundation using a qualitative research method and interviews with experts in the field. The research findings led to the identification of 38 secondary codes and finally 14 main concepts that in the form of paradigm model, the central category of agile marketing capabilities (specialized and structural capabilities), causal conditions (human capital, technology and understanding customer needs, existing structures and competition), Strategies (cost leadership and differentiation leadership strategies) are underlying factors (appropriate advertising and communication channels), interveners (environmental factors) and outcomes (improving marketing performance and sustainable development).
 

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In this paper, an efficient method is proposed for solving nonlinear quadratic optimal control problems with inequality constraints. The method is based upon Chebyshev cardinal wavelets. The operational matrix of integration is given for related procedures. This matrix is used to reduce the solution of the nonlinear constrained optimal control to a nonlinear programming one to which existing well-developed algorithms may be applied. Finally, the applicability and validity of method are shown by numerical results of some examples. Moreover, the comparison with the existing results show the preference of this method.
 

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Batch scheduling is among the important problems in industrial engineering and has been widely attendant in practical applications. Clustering is the set of observation assignment into some subsets so that the observations in the same cluster are similar in some sense and the similarity of generated clusters is very low. Clustering is considered as one of the approaches in unsupervised learning and a common technique for statistical data analysis which has been applied in many fields, including machine learning, data mining and etc. This paper studies a case study in Iran Puya company (as a home appliance maker company in Iran). In the production line of refrigerator of the current company, a cutting machine is identified as a bottleneck that can process several iron plates simultaneously. In this regard a good scheduling on this cutting machine improves the effectiveness of production line in terms of cost and time. The objective is to minimize the total tardiness and maximizing the job values when the deteriorated jobs are delivered to each customer in various size batches. Based on these assumptions a mathematical model is proposed and two hybrid algorithms based on simulation annealing and clustering methods are offered for solving it and the results are compared with the global optimum values generated by Lingo 10 software. Based on the effective factors of the problem, a number of sensitivity analyses are also implemented including number of jobs and rate of deterioration. Accordingly, the running time grows exponentially when the number of jobs increases. However the rate of deterioration could not affect the running time. Computational study demonstrates that using clustering methods leads an specified improvements in total costs of company between 15 to 41 percent.

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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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According to current development in credit allocation and recent economic crises, planning for identification of credit risk has found special importance for investors, banks, shareholders and financial analysts, so that they are able to make proper decisions. Although credit loss is a common cost in banking industry, however, increase in this loss might affect the bank performance. Therefore, there is a strong need to reassess current approaches in risk evaluation of each loan and default rate of loan portfolios. Banks usually have their own internal validation models for loan risk measurement but these approaches are inappropriate and utilize simple mathematical approaches based on incomplete premises. In this paper, we have tried to estimate the possibility of default for legal customers using 20 financial ratios for 200 healthy and 200 unhealthy companies receiving civil participation facilities from Eghtesad Novin (EN) Bank in 2009 and 2010 and 4 approaches for choosing financial ratios including remarks from credit experts of Raah Eghtesad Novin Co., Altman, comparison between averages and choosing correlation attribute. Results show that Support Vector Machine approach can differentiate between healthy and unhealthy companies with average accuracy of 84.63% using all chosen ratios.

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This research uses Network Data EnvelopmentAanalysis (NDEA) by  undesirable factors to analyze and evaluate the performance of automotive industry. The modeling used is applied to five production lines of an automobile company by 16 indicators. The data used are for the year 2019. The main purpose is to provide a model to improve the quality of the product by evaluating the performance of quality health in production lines able  to rank by providing appropriate quality indicators to identify, formulate and achieve corrective measures. Accompanied with accurate problem solving and operational scheduling according to the most efficient organization/production line and so investigating the source of the problem and preventing the occurrence of the problem. Because determining the direction of performance and key performance indicators (KPI) of the organization and measuring them to increase its health efficiency requires an efficient and integrated system. On the other hand, creating a homogeneous and orderly development process between the elements of the organization as a common language to solve the quality problems by aiming the improvement of the performance, customer satisfaction, sustainable production and cost management has been proposed.

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Data Envelopment Analysis (DEA) is a nonparametric approach for evaluating the relative efficiency of a homogenous set of Decision Making Units (DMUs). To evaluate the relative efficiency of all DMUs, DEA model should be solved once for each DMU. Therefore, by increasing the number of DMUs, computational requirements are increased. The Cerebellar Model Articulation Controller (CMAC) is a neural network that resembles a part of the brain known as cerebellum. The CMAC network with a simple structure is capable of estimating nonlinear functions, system modelling and pattern recognition. Meanwhile, the CMAC approach has fast learning convergence and local generalization in comparison to other networks. The present paper is concerned with assessing the efficiency of DMUs by the CMAC neural network for the first time. The proposed approach is applied to a large set of 600 Iranian bank branches. The efficiency results are analyzed and compared with the Multi-layer Perceptrons (MLP) network outcomes. Based on the results, it can be seen that the DEA-CMAC results tend to be similar to those of DEA-MLP in terms of accuracy. In addition, the Mean Squared Error (MSE) in DEA-CMAC decreases much faster than that in DEA-MLP. The DEA-CMAC model takes 1008 and 1107 iterations to reach MSE errors of 2.03×〖10〗^(-4) and of 6.01×〖10〗^(-4), respectively, while the DEA-MLP model takes 1190 iterations keeping the MSE error stable at 2.07×〖10〗^(-1). Moreover, DEA-CMAC requirements for CPU time are far less than those needed by DEA-MLP.

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 In this paper, we consider a multi-objective hub location problem (MOHLP) to locate two constrained    facilities in order to minimize the distance between these facilities and the weighted distance between each facility and related customers. For this purpose, we establish a necessary and sufficient condition of optimality for finding an efficient solution of the problem. We show that MOHLP can be reduced to a simple bi-level distance problem. Then we develop an efficient algorithm to find the optimal solution set of BDP,  and provide its convergence without any assumption. Moreover, an algorithm is proposed to solve MOHLP, which converges in a finite number of iterations. Some examples are stated to clarify the proposed algorithms.

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Health care facility systems are hierarchical as they consist of facilities at different levels such as clinics, health centers, and hospitals. Therefore, finding a proper location for the health care system can be categorized as a hierarchical location problem. Besides, partitioning a given region in a geographical area into different zones is very crucial to make sure the health services are available at their highest possible level for everyone in that region.  In this study, an optimization model for the integrated problem of hierarchical location and partitioning under uncertainty in the Iranian healthcare system is proposed. The objective function of this model maximizes the total social utility of districts while workload balance and distance limitation between the zones are considered as the main constraints. Since this study involves NP-hard problems, three metaheuristic algorithms, including Genetic, Salp Swarm Algorithm (SSA), and Grey Wolf Optimizer (GWO) were developed. The numerical results suggest that the Grey Wolf Optimizer (GWO) algorithm indicates a more appropriate level of performance in almost all responses compared to the other algorithms. Therefore, the case study was solved by the Grey Wolf Optimizer (GWO). Based on the results, 10 distrcis with their zones are identified to maximize the overall utility. A sensitivity analysis also performed to show the behavior of the model. It can be stated that the findings of this study can be utilized as a useful management tool in other organizations.

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This paper is concerned with presenting an exact algorithm for the Undirected Profitable Location Rural Postman Problem. This problem combines the profitable rural postman and facility location problems and also has some interesting real-life applications. Fixed costs are associated with end points of each profitable edge and the objective is to choose a subset of profitable edges such that the difference between the profit collected and the cost of opening facilities and traveling cost is maximized. A dominance relation is used to present an integer programming formulation for the problem and a branch and cut algorithm is developed for solving the problem and extensive numerical results on real-world benchmark instances are given to evaluate the quality of presented algorithms.
 

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The false location of airports is one of the most important issues and challenges that we face on some airports, finding scientific solutions to optimize airports, to achieve travelers, including these challenges. The main purpose of this research is to provide a metaheuristic technique for locating the construction of airport and compared with the results of the seca model and the Copras Method. The metaheuristic technique is based on new multi-criteria decision making techniques, aimed at prioritizing research alternatives and its difference with the rest of the methods is to use statistical methods and now it is possible to understand and simply process its process. The statistical population of this research is (experts and management in Iran airport and air Navigation Company). After research, alternatives were selected based on the opinions of experts who included five provinces of the country, as well as 10 standard indicators, including the average income per year, the population of the province and ... who were extracted from the questionnaire as input. Finally, the provinces were prioritized according to different ways, all results based on choosing Isfahan province as the right province and Najaf Abad city as the final alternative

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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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This paper develops slacks-based measure (SBM) and additive SBM (ASBM) to evaluate efficiency of decision making units (DMUs) in a two-stage structure with undesirable outputs and feedback variables from the internal perspective. The SBM model is linearized  for a specific weight and the ASBM model is reformulated as a second order cone program. The target values for all inputs, outputs (both desirable and undesirable) and intermediate products are  provided. This study shows that unlike the SBM model, ASBM can be adapted to the preference of the decision maker by selecting the weights to aggregate stages in the network.
 

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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.