Selection of robots from the several proposed alternatives is a very important and tedious task. Decision makers are not limited to one method and several methods have been proposed for solving this problem. This study presents Polygons Area Method (PAM) as a multi attribute decision making method for robot selection problem. In this method, the maximum polygons area obtained from the attributes of an alternative robot on the radar chart is introduced as a decision-making criterion. The results of this method are compared with other typical multiple attribute decision-making methods (SAW, WPM, TOPSIS, and VIKOR) by giving two examples. To find similarity in ranking given by different methods, Spearman’s rank correlation coefficients are obtained for different pairs of MADM methods. It was observed that the introduced method is in good agreement with other well-known MADM methods in the robot selection problem.

The purpose of this research is to investigate the relationship between stock market development, business cycle and risk of the banks. The statistical population of the study is all banks whose shares were accepted in Tehran Stock Exchange from 2010 to 2015. The study selects 10 banks, which were active throughout the entire research period on the stock market. The research data were extracted from the financial statements of the companies and analyzed using regression models based on panel data. The research findings show that there was a direct and significant relationship between stock market development and risk of banks. In addition, the effect of stock market development on risk of banks was different in the presence or absence of a crisis, and the financial crisis in banks reduced the impact of the financial development on the risk of banks. The results also show that during the periods of commercial boom, the effect of the financial market development on the risk of banks was more than the periods of the recession.

Outbreak response can be viewed as a project for which the window of opportunity for planning is often quite limited. Therefore, regular evaluation for sharing the lessons learned is crucial, especially in the cases of national and global crises. As one of the major global concerns, the novel coronavirus, also known as COVID-19, has infected and killed many people in several countries worldwide. Hence, it is worth analyzing the performance of different countries in this regard. In this paper, the confirmed Case Fatality Rate (cCFR), and the confirmed Case Recovery Rate (cCRR) are considered as the main performance criteria, and the data are analyzed utilizing statistical confidence intervals (CIs) implemented in Python. The results identify the regions with high cCFR and low cCRR, as well as the regions with low cCFR and high cCRR. Finally, it is suggested to systematically transfer the knowledge and lessons learned from the high performing countries to where such information and knowledge is needed.

Nowadays, with the increasing number of projects in organizations, managers are enthusiastic about managing and controlling projects, and as projects become more complex and cause unforeseen risks, project management becomes even more critical. Using the earned value management method to control the current status of projects, and predicting the future status of projects has had many advantages. Representing the status of the project, and predicting the project performance by various indicators are some features of this method. As these indicators are deterministic, the risk of the prediction increases when the number of risks goes up in a project. Therefore, statistical methods can be employed to estimate the statistical distribution of risks and notably boost predicting accuracy. The purpose of this paper is to present a method for predicting project duration and cost of the project and selecting the best action plan. Control charts are used along with the earned value management method to increase accuracy. This model also provides the possibility of selecting the best action plan to improve project performance. Moreover, this method can be applied in each project phase separately. Finally, a case study is used to investigate the validity of the proposed method.

In this study, an integrated approach is presented for analyzing the impact of resilience engineering and ergonomics factors in aerospace supply chain using data envelopment analysis (DEA). The proposed approach selects the preferred supplier by considering traditional supply chain factors as well as resilience engineering and ergonomics factors. Also, the relevant performance efficiency of each decision making unit is calculated. The case study of this paper is the supply chain of real commercial airlines. Thus, the aerospace standards as well as resilience and ergonomics factors are considered to be modeled by the mathematical programming approach. 22 suppliers are evaluated by analyzing inputs and outputs through data envelopment analysis, and each supplier is considered as a decision making unit (DMU). In this study, the most effective factors are identified as “reliability”, “Human resource management”, “supplier’s delay” and “availability”. Also, “lead time” shows the highest potential for improvement. This study helps decision makers identify the weaknesses of their supply chain management to establish a performance improvement plan in aerospace industry.

Supply chain disruptions pose significant challenges to global economic stability, necessitating advanced predictive tools for effective risk management. As Machine Learning (ML) offers promising solutions for enhancing resiliency, this study investigates its applications in supply chain management. Utilizing a systematic literature review, we examined recent research to identify effective ML models and techniques, focusing on both supervised and unsupervised learning. Our analysis covered various industries to understand the adaptability and effectiveness of these models in mitigating supply chain risks. The results highlight the growing implementation of ML in anticipating disruptions, with supervised learning demonstrating superior predictive precision under specific conditions. At the same time, unsupervised approaches offer valuable insights in data-scarce scenarios. Context-specific data surfaced as crucial in model accuracy, underscoring the need for tailored approaches. This study concludes that integrating ML with current supply chain systems can significantly enhance operational resilience, advocating for continued exploration of novel data sources and interdisciplinary collaborative efforts.

In this study, a multi-product, multi-period and non-linear programming model is developed for production planning problem where demand is under uncertainty. The proposed study is designed for a real-world case study of chemicals production factory with 1 production line and 2 manual and automatic technologies. In manual technology, workers are working with 3 amateur, typical and professional skills in 2 typical and overtime working. Automatic technology in this system has n machines in which the repairing and maintenance of the machineries are also included. This system has n products and the products are life-limited and with diversity. The primary goal is to propose a model for improvement of the production planning and minimization of the production system costs. The products in high volume and various types are produced and they are stored in bottles as the final products. For different production periods, the human forces capacities are considered and the level of employment or forces dismissal are considered. The production process is forwarding and backward process is not acceptable; that is, it is not allowable to rework in this system. Delivering final product from stockpiles to the retailers is conducted using vehicles with limited capacity. To solve the model in larger space and because of the complexity of the model, meta-heuristic algorithm is used. Finally, it is concluded that due to covering most of the assumptions in perishable products production, the proposed model is closer to the real-world circumstances and reduces costs in production systems.

In the present study, the flexural behavior of concrete beams reinforced with longitudinal rebars made of glass fiber reinforced polymer composites (GFRP), is studied using finite element method. For this purpose, a number of concrete beams with square sections are modeled in ABAQUS. In all of the beams, four GFRP rebars with diameter of 12 mm, and 10 steel rebars with diameter of 8 mm are vertically used to prevent creating shear cracks in the beams. The beam no. 1 is without transverse rebar, beam no. 2 has a row of transverse rebar, which is placed at the bottom and in the plane of vertical rebars. Beam no. 3 has a row of transverse rebars that is placed at the top and in the plane of vertical rebars; and the beam no. 4 has two rows of transverse rebar at the top and bottom of the beam in the plane of vertical rebars. The beams are gradually loaded under up to 6 ton, and the amounts of displacement and strain at the middle of beams are compared together. The obtained results reveal that the force-displacement diagram of reinforced beams with composite rebars are almost linear until ultimate phase, and in all of the beams, adding transverse rebar leads to less deflection in the middle of the beam under an identical loading. Moreover, the load bearing capacity of beams containing transverse rebars, were higher than the other beams.

Customers are financial and intellectual capital of the firm and its survival and success depend on the attitude and firm performance to its customers. The goal of this article is to study customer satisfaction in Bonny Chow National Distribution Company and offering a model to evaluate customer satisfaction and also to study strength and weakness to improve and facilitate the defects. This study is descriptive, because it collects data and analyzes hypothesis based on expert survey. The questionnaire used in this paper has 10 questions on measure customer satisfaction. The results show that responders were satisfied with the visitors and delivery personnel and they were dissatisfied with the firm. In other words hypothesis 1 and 2 were accepted and hypothesis 3 was rejected.

Marketing capabilities such as advertising and distribution, in the past, were considered as an intermediate level marketing activities but our focus in this study is on higher potentials of marketing, innovation and branding. The primary objective of this study is to investigate the effect of marketing capabilities on the market of small and medium enterprises in the province of Isfahan, Iran. The research consisted of small and medium enterprises in the industrial towns of the province of Esfahan using random sampling technique to gather the necessary data. Out of the 62 industrial parks and industrial zones in the province, there were 4,326 industrial units, which were active and the study distributes 384 questionnaires and managed to collect 308 properly filled ones. Using some statistical tests, the study has confirmed that innovation and branding influence positively on SMEs in this province.