Mohamad Afshar

In challenging circumstances such as war, governments are shifting their focus towards Solar Energy (SE) as a Renewable Energy option through PhotoVoltaic panels due to the rising costs associated with fossil fuel extraction and exploration. This model recommends a SE Location (SEL) that prioritizes Robustness, Resilience, and Risk awareness which is called 3RSEL. As a result, a Bi-Level Programming (BLP) is proposed to solve this problem for the first time. A heuristic approach is defined for a BLP mathematical model. This research generates a lower and upper bound to solve the model quickly. The results show that Yazd and Kerman are the optimal location for SEL. The main problem is compared to a situation where risk and robustness are not considered. It can be observed that the supplier’s profit and energy production are lower than without risk and robustness, with a gap of −4.4%. The variability of the conservatism coefficient, discount rate, confidence level of Conditional Value at Risk, and problem scale are considered. Increasing the conservatism coefficient decreases the supplier’s profit function and energy output. Alternatively, increasing the discount rate decreases the supplier’s profit function without affecting the energy output. Conversely, boosting the confidence level does not alter suppliers’ profit function but results in declining energy output. Finally, as stated, it can be observed that the computation time increases with an increase in the scale of the problem.

Background: Supply chain networks (SCNs) have been interrupted by the COVID-19 pandemic, leaving them open to financial losses. SCs have been impacted by the pandemic, necessitating the adoption of sustainable practices and dynamic capacities to ensure resilience and performance. Several studies have focused on this subject, offering insights into the importance of sustainable supply-chain management, corporate governance, big data management activities, and digital technology in minimising the consequences of the pandemic and fostering sustainability. Methods: This study suggests an analytical framework for assessing environmentally friendly procedures and dynamic capacities to assure performance in a disruptive environment. Results: The following are some of the important details and contributions in this article: (1) developed a conceptual framework for assessing dynamic capacities and sustainable behaviours considering COVID-19, (2) concentrates on financial ratios during COVID-19, and (3) established drivers for sustainable practices and competencies during disruption and unpredictable business settings. Conclusions: The suggested model can assist practitioners in creating and implementing sustainable supply chain (SC) activities and tracking and assessing their effects on the sustainability of businesses. So, the proposed model can assist managers in creating and implementing sustainable supply-chain activities and tracking and analysing their effects on the sustainability of businesses.

This research suggests an Antifragile, Sustainable and Agile Supply Chain Network Design (ASASCND) as a new network design that integrates these concepts considering resiliency, robustness, risk, and environmental requirements. The cost function combines a novel method with robust stochastic optimization and Entropic Value at Risk (EVaR). This model combines expected value, maximum and EVaR of cost as an objective function. This research adds antifragility by the effect of learning on variable parameters, sustainability by considering the environmental and social issues, resiliency and agility by flexible capacity, and multi-resource and demand satisfaction constraints to the model. The case study is in the automotive industry. This model compares the main problem by considering antifragility without thinking about antifragility. The ASASCND cost is − 0.3% less than without considering antifragility. In addition, when the conservatism coefficient grows, the cost function increase. In addition, the antifragility coefficient and the confidence level affect positively, and the agility coefficient negatively affects the cost function. Expanding the model scale changes the cost function and time computation because the antifragility coefficient changes variable cost. Finally, managerial insights and practical implications are explained.

This research proposes a new framework for agri-food capacity production by considering resiliency and robustness and paying attention to disruption and risk for the first time. It is applied robust stochastic optimization by adding robustness to the constraint's objective function and resiliency situation. This research minimizes the mean absolute deviation and coefficient of standard deviation errors by linear function in the agri-food capacity production. This study suggests agri-food managers and decision-makers use this mathematical method to forecast and improve production management. The results of this research lead to better decision-making and are compared with other sine functions. The main model's Robust and Resiliency Mean Absolute Deviation (RRMAD) value is 1.28% lower than other sine-type functions. The conservativity coefficient, confidence level, weight factor, resiliency coefficient, and probability of the scenario vary. The main model's RRMAD value is 1.28% lower than other sine-type functions. Growing the weight factor will result in an increase in RRMAD and a smooth decline inR-squared. Additionally, as the resilience coefficient rises, the RRMAD function increases while theR-squareddeclines. By altering the probability of the scenario, the RRMAD function drops, and theR-squaredgoes up.