Today global warming is on the rise and the natural resources are getting consumed at a faster rate. Power consumption has increased many folds to cater the human need. Thus renewable energy resources are the only option available at this juncture. Wind energy is one of the renewable energy. Location selection for wind farm takes an important role on power generation. However, the location selection is a complex multicriteria problem due to the criteria factors which are conflicting in nature as well as uncertain. The process becomes more complex when a group of decision makers are involved in decision making. In the present study, a COPRAS (COmplex PRoportional ASsessment) based multi-criteria decision-making (MCDM) methodology is done under fuzzy environment with the help of multiple decision makers. More specifically, this study is aimed to focus the applicability of COPRAS-F as a strategic decision making tools to handle the group decision-making problems.

Previous studies have convincingly shown that employees' family lives can affect their work outcomes. We investigate whether family-to-work conflict (FWC) experienced by the employee also affects the turnover intention of a co-worker. We predict that the employee's FWC has an effect on the co-worker's turnover intention through the crossover of positive and negative work attitudes. Using a sample of 154 co-worker dyads, we found that the employee FWC was positively related to co-worker turnover intention through the crossover of (reduced) work engagement. Results show that family matters at work, affecting employee. In addition, employee's job engagement was positively related to his (her) co-worker job engagement and it was negatively related co-worker turnover intention and employee's FWC was not positively related to co-worker turnover intention trough the crossover of (reduced) feelings of engagement.

The FMCG sector in developing nations is still not prepared to withstand any disruption brought on by the worldwide pandemic. In order to adapt to the new normal, businesses must make both micro and broad changes to their supply chain strategy. The goal of this research is to create plans to minimize any disruptions caused by upcoming pandemics. To restore the broken supply chain, a number of new implications and adjustments to the current attributes were proposed in the areas of sourcing, manufacturing, and distribution. Finding the fundamental drivers that are frequently impacted by the disturbance is part of the technique. The afflicted locations were the focus of the models' development. The ideas work as preventative measures intended to thwart the disturbance when and if it happens. In order to assess the model's viability, the Key Performance Indicators (KPI) value was ultimately retrieved with the aid of 25 industry experts. These suggestions may result in improved transparency, real-time monitoring, cost effectiveness, and responsiveness, among other benefits. Our analysis indicates that the KPI scores for procurement, production, and distribution are 92.86%, 82.14%, and 87.50%, respectively. The models' total viability is 87.50%. The most recent Covid-19 pandemic has provided us with a vivid illustration of what could go wrong in such circumstances. In both pandemic and non-pandemic conditions, the adaptation of stated suggestions at the aspect of sourcing, production, and distribution might result in a significant shift to organization-wide activities.

Planning and scheduling processes in project management are carried out sequentially in prac-tice, i.e. planning project activities first without visibility of resource limitation, and then schedul-ing the project according to these pre-planned activities. This is a need to integrate these two pro-cesses. In this paper, we use Branch and Bound approach for generating all the feasible and non-feasible project schedules with/without activity splitting, and with a new criterion called “the Minimum Moments of Resources Required around X-Y axes (MMORR)”, we select the best feasible project schedule to integrate plan processing and schedule processing for engineering projects. The results illustrate that this integrated approach can effectively select the best feasible project schedule among alternatives, improves the resource utilization, and shortens the project lead time.

This study deals with appreciating the role of both governance system and executives cognitive and attitudinal aspects in the innovation decision-making. After discussing the theoretical relationship between board independence and CEOs attitude and behavior, we are advancing an empirical model testing the correlation between the managers’ attitude and behavior towards innovation and his psychological commitment level. The CEOs commitment bias and attitude constituent were measured using questionnaire. The data analysis was performed using the Bayesian network method on 220 Tunisian executives. Empirical results confirm the theoretical prediction and shows that processing with persuasive mechanism does not have an effective role on the alignment of the manager’s attitude and behavior in key tasks such innovation decision. CEOs authentic behavior was more related to an important manager involvement in this behavior rather than to persuasive effort committed by outside directors to make him contract this action. CEOs attitude and behavior towards innovation are shown related to commitment link “manager-task” and suggests that the board of directors plays no role in the CEOs discretion management. We argue that persuasive approach is not a sufficient path in behavior and interests alignment; yet, it should be applied with the commitment approach for understanding manager decision-making.

In this paper, we examine advanced optimization approach for portfolio problem introduced by Black and Litterman to consider the shortcomings of Markowitz standard Mean-Variance optimization. Black and Litterman propose a new approach to estimate asset return. They present a way to incorporate the investor’s views into asset pricing process. Since the investor’s view about future asset return is always subjective and imprecise, we can represent it by using fuzzy numbers and the resulting model is multi-objective linear programming. Therefore, the proposed model is analyzed through fuzzy compromise programming approach using appropriate membership function. For this purpose, we introduce the fuzzy ideal solution concept based on investor preference and indifference relationships using canonical representation of proposed fuzzy numbers by means of their corresponding?-cuts. A real world numerical example is presented in which MSCI (Morgan Stanley Capital International Index) is chosen as the target index. The results are reported for a portfolio consisting of the six national indices. The performance of the proposed models is compared using several financial criteria.

This paper presents a systematic approach for evaluating the performance of a project based organization using a two level fuzzy data envelopment analysis (DEA) technique in project based organizations. In order to determine the required inputs and outputs, important indicators are selected using both expert judgments and statistical analysis and a two-level DEA model is adapted. In this model, by considering different inputs and outputs through a hierarchical process, a large number of sub indicators are provided and rolled up to a higher level. Since inputs and outputs are combinations of qualitative and quantitative indicators, fuzzy logic is also included through the modeling procedure. In addition, since the exact amount cannot be attributed to the indicators, the proposed model uses interval values for the project life cycle. Finally, some of the projects are evaluated throughout the approach proposed in this paper.

1-(2,5-Dichlorophenyl)hydrazine was converted via Fischer synthesis with isopropylmethylketone into 4,7-dichloro-2,3,3-trimethyl-3H-indole. Exposure of the indolenine to the vilsmeier reagent produced amino methylene malondialdehyde which reacted with hydrazine, arylhydrazine, urea, cyanoacetamide and thiourea to give pyrazols, pyrimidones and thiopyrimidone, respectively.

This research presents a new bi-level bi-objective production-routing-inventory model for a medi-cine supply chain. In this case, the production is executed by multi-separated producers in a multi-production line for different kinds of medicines which will be saved in stores for delivering to cus-tomers. The capacitated vehicle routing problem is considered in designing a distribution system from stores to customers. The goal of this model is to make a suitable trade-off between the customer satisfaction and the budget cost. This problem has been formulated in a bi-level form where the first objective function is the minimization of the budget during the scheduled time and the second one is the minimization of the shortage amount associated with the lost sale of medicine demands delivering to drug stores. Uncertainty is considered as a nature of the main parameters of the problem. Then the robust approach was used to handle the associated uncertainty of related parameters and the resulted problem is solved by Benders decomposition algorithm. The results indi-cate that the model make an improvement in medicine supply chain.

Buckling is one of the most complicated concepts in mechanical engineering. Buckling often happens by compressive loads on thin structures. Thermal gradient between two ends of a column may cause a deflection in it. This will add an extra deformation to the one provided by compressive loads on the column. This phenomenon occurs when two ends of the column are at different temperatures, which can be seen at various structures. Because of the considered temperature gradients, the critical load of the column will decrease. In the current paper, various columns are modeled and the effect of thermal gradient and compressive load and other parameters on the Bi-material columns are studied. In other words, influences of compressive load and temperature gradient on critical load of Bi-material columns with interface crack are investigated. Effect of change in each parameter on critical load of column and crack opening was investigated. First, the thermal gradient was only applied to the model and in the next step; only the effect of mechanical loading was studied. Furthermore, artificial neural network (ANN) was used to extend the results to a bigger range of temperature conditions through the columns. Based on the results, ANN and finite element results are in a good agreement and the thermal effects may have a significant role in buckling of the column.