Today, with the advancement of technology in the production process of various products, the achievement of sustainable production and development has become one of the main concerns of factories and manufacturing organizations. In the same vein, many manufacturers try to select suppliers in their upstream supply chains that have the best performance in terms of sustainable development criteria. In this research, a new multi-criteria decision-making model for selecting suppliers and assigning orders in the green supply chain is presented with a fuzzy optimization approach. Due to uncertainty in supplier capacity as well as customer demand, the problem is formulated as a fuzzy multi-objective linear programming (FMOLP). The proposed model for the selection of suppliers of SAPCO Corporation is evaluated. Firstly, in order to select and rank suppliers in a green supply chain, a network structure of criteria has defined with five main criteria of cost, quality, delivery, technology and environmental benefits. Subsequently, using incomplete fuzzy linguistic relationships, pair-wise comparisons between the criteria and sub-criteria as well as the operation of the options will be assessed. The results of these comparisons rank the existing suppliers in terms of performance and determine the utility of them. The output of these calculations (utility index) is used in the optimization model. Subsequently, in the order allocation process, the two functions of the target cost of purchase and purchase value are optimized simultaneously. Finally, the order quantity is determined for each supplier in each period.

The topic of robots is gaining traction in both academic discourse and popular media due to their growing prevalence in the hospitality sector. The hospitality industry is likely to encounter practical challenges in adopting the increased use of robots. The primary objective of the present research was to conduct an empirical investigation into a theoretical framework that explores how employees view robot-caused unemployment, with particular attention given to their perceptions of robot adoption. The study utilised structural equation modelling to analyse data obtained from 401 service employees in Jordan through online questionnaires. The findings indicate that the perception of robot-induced unemployment among employees is significantly influenced by their social skills, perceived risk, awareness, and trust in using service robots. The present study established a theoretical framework for investigating user perceptions of robot-induced unemployment within the particular setting of hospitality robots. The findings offer valuable insights for guiding future development and research efforts in the hotel service robot industry as well as informing marketing strategies for hotel managers. Ultimately, these efforts may contribute to the sustainable growth of service robots and associated sectors, including the hotel service sector.