The purpose of this research is to design a crisis management model in the archival centers of Iran during the pre-crisis phase. The present study was conducted with a qualitative approach using the Grounded theory method. The necessary data was collected using semi-structured interviews. Data saturation was a major factor in determining the sample size. The research population included 20 crisis management specialists and managers of archives in Iran and the data analysis was performed in three stages: open, axial, and selective coding. The paradigmatic model derived from the findings of the research includes the sections of the causal conditions, strategies, intervening conditions and the underlying conditions and their outcomes. The causal conditions are located in two categories of internal crises such as biological crises and lack of proper management, and external crises such as natural disasters, fires and human-made crises. Intervener conditions and backgrounds include cat-egories such as lack of specialist staff training, lack of appropriate environmental conditions and management mechanisms, archival building and human resources issues. To prevent and mitigate the effects of the crisis in archives, some strategies are required such as modern education for resil-ience, teaching how to use documents, standardizing buildings and equipment (non-use of steel skeletons in building archives, use of concrete skeletons Armed or metal, as well as the use of walls and doors and floors of anti-fire covers) in the building of archives and the establishment of multi-layer protective systems for the protection of documents.

The industry has expressed significant concern regarding the issue of parallel machines and the influence of learning and deterioration. This research investigates non-identical parallel machine scheduling, taking into account the simultaneous consideration of learning effects, deterioration, and past-sequence-dependent setup times. Due to the existence of uncertain parameters in real-world scenarios, the processing times and due dates are assumed to be triangular fuzzy numbers. A fuzzy nonlinear mathematical model with two objective functions is presented and solved using the fuzzy Chance Constraint Programming approach. The two objectives are the summation of earliness and tardiness, as well as makespan. To achieve an efficient near-optimal Pareto front for the problem, a hybrid NSGA-II and VNS multi-objective meta-heuristic is proposed and the results are discussed. Finally, the augmented ε-constraint method is utilized to address issues with small dimensions. The computational analysis demonstrates the effectiveness of this proposed algorithm in tackling problems, especially those with substantial dimensions.

In this paper, the authors proffer a novel mathematical model for the simultaneous optimization of facility location and network design in the presence of uncertainty, with the aim of minimizing operational and transportation costs. The proposed model constitutes a departure from conventional methods in its consideration of probable events in the real world and the incorporation of uncertainty assumptions into the mathematical framework. An algorithm based on simulated annealing is then advanced for the solution of the problem, and the performance of the algorithm is evaluated through comparison with exact methods for problems of modest size, as well as with a basic simulated annealing algorithm for larger problems. The results of these comparisons demonstrate the superiority of the proposed meta-heuristic algorithm. Finally, the robust approach is compared with four other approaches in the presence of uncertainty, with a thorough analysis of the results obtained from each of the methods conducted in a suite of sample problems.

This study presents a new hybrid framework based on the multi-criteria decision making in order to rank the potential site layout locations by consideration of the cost and safety criteria in the Mehr Construction Project in Tehran, Iran. To this end, all of the criteria in selecting suitable potential locations are extracted from the research literature and the most effective ones, which are matched with existing conditions in Tehran are considered based on the opinion of experts,. Then, the proper locations for site layout are determined as the potential alternatives and ranked by experts based on the structure. According to the data collected from the questionnaires, the weights of the selected criteria are calculated using Best Worst Method (BWM) and the final ranking of the locations is performed using two Gray Relational Analysis and VIKOR methods. The computational results indicate that both VIKOR and GRA methods yield the same ranking. However, a method with higher reliability should be used to select the best potential location of construction site layout. Therefore, the sensitivity analysis of final outputs on the parameters existing in VIKOR and GRA methods is used in order to rank the alternatives and select the best approach. According to the computational results, the GRA method provides higher robustness compared with the VIKOR method. Accordingly, the ranking obtained from the GRA method is employed as the final solution in implementing the case study.

The cardiac output and stroke volume of a healthy person was estimated using a 3D fluid-structure interaction (FSI) simulation coupled with an echocardiogram Doppler (echo-Doppler) during exercising and resting. The geometry, dimensions, and blood flow through the aortic valve were measured using the echo-Doppler. A 3D FSI simulation was modeled using an arbitrary Lagrangian-Eulerian mesh. The 3D FSI cardiac output and stroke volume results were 15.4% lower than Doppler results when brachial pressures applied with differences between brachial, central, and left ventricular pressures. While without considering these brachial pressures differences, the discrepancy between the FSI cardiac output and stroke volume results with Doppler increased to 22.3% and 26.2%, respectively. Eventually, the comparisons of the 3D FSI results and clinical measurements demonstrated that numerical methods can be a potential computational tool to estimate cardiac output and stroke volume for different heart rates when they coupled with the clinical measurements.

Road accidents as a global challenge, imposing irreparable financial and human life losses in almost all countries, especially in developing countries, annually. According to world health organization (WHO), if this trend continues, road accidents will become the 7th cause of human death by 2030. Thus, road safety policy makers have been trying to use safety promotion and preventative actions. In this paper, the road safety performance of Iranian provinces is studied. To evaluate road safety efficiency scores, data envelopment analysis based on road safety (DEA-RS) method in two deterministic and non-deterministic situations is used. To consider the uncertainty in input and output data, this paper develops credibility DEA-RS (CreDEA-RS) model. In fact, the constraints of DEA-RS model are considered as credibility constraints and a counterpart credibility DEA-RS (CreDEA-RS) model is proposed for evaluating road safety of provinces of Iran. According to the results, provinces located in mountainous and forest areas such as Gilan had a much weaker performance than provinces in desert areas such as Yazd.

A vehicle routing problem with time windows (VRPTW) is an important problem with many real applications in a transportation problem. The optimum set of routes with the minimum distance and vehicles used is determined to deliver goods from a central depot, using a vehicle with capacity constraint. In the real cases, there are other objective functions that should be considered. This paper considers not only the minimum distance and the number of vehicles used as the objective function, the customer’s satisfaction with the priority of customers is also considered. Additionally, it presents a new model for a bi-objective VRPTW solved by a revised multi-choice goal programming approach, in which the decision maker determines optimistic aspiration levels for each objective function. Two meta-heuristic methods, namely simulated annealing (SA) and genetic algorithm (GA), are proposed to solve large-sized problems. Moreover, the experimental design is used to tune the parameters of the proposed algorithms. The presented model is verified by a real-world case study and a number of test problems. The computational results verify the efficiency of the proposed SA and GA.

This study presents a new mathematical model for the design of reliable cellular manufacturing systems, which leads to reduced manufacturing costs, improved product quality and improved total reliability of the manufacturing system. This model is expected to provide a more noticeable improvement in time and solution quality in comparison with other existing models. Each part to be manufactured may select each of the predefined manufacturing routes, such that the total reliability of the system is increased. On the other hand, the model adopts to categorize the machines to determine the manufacturing cells (cell formation) and reduce the transportation costs. Thereby, both criteria of system reliability and manufacturing costs will be simultaneously improved. Due to the complexity of cell formation problems, a two-layer genetic algorithm is applied on the problem in order to achieve near optimal solutions. Furthermore, the performance of the proposed algorithm is shown for solving some computational experiments. Finally, the results of a practical study for designing a cellular manufacturing system as a case study in Iranian Diesel Engine Manufacturing Co., Tabriz, Iran are present.

Data envelopment analysis models play an important role in decision making. In this paper, one-stage and two-stage nonlinear programming problems are investigated in order to evaluate the efficiency of two types of network data envelopment analysis model. The first type of network data envelopment analysis model has a series structure with three stages and a feedback between the last step and the middle step, the second model has a three-stage series structure with two feedback between the final step and the first step and the middle step. By examining the overall efficiency of the models based on the one-stage programming problem, a two-stage programming problem is also applied in order to evaluate the efficiency of each step. In order to solve one-stage nonlinear programming problems and two-stage linear and nonlinear programming problems derived from modeling, a linearization method based on coordinate transformation, and constant assumption and gradual growth of some variables is presented. In the last section, the proposed methods have been discussed using some numerical examples.

In recent years, one of the goals of any company is to increase overall production and process reliability. Hereupon supply chain reliability has been gaining growing attention and provides a technical framework for quantifying supply chain risks and uncertainties. In this paper, supply chain reliability was investigated in a two-stage stochastic programming model to design reliable closed-loop green four-echelon forward/backward supply chain networks. The purpose of this model was to maximize the total reliability of the supply chain based on the structural reliability theory. Our proposed model also minimized the cost of the supply chain by definition of recycling centres and the cost of penalizing unauthorized carbon emission and damages. The model optimized the locations of factories, warehouses, and recycling centres considering stochastic modes for demands and carbon price, as well as the flow between different sectors and the optimal orders. As the proposed model was a mixed-integer nonlinear problem, both e-constraint method and the metaheuristic algorithm (NSGA-II) were used in different scales and the sensitivity analysis was performed for critical parameters.