Nowadays, using Blockchain Technology (BCT) is growing faster in each country. It is essential to apply BCT in Supply Chain Network Design (SCND) and is considered by the designer and manager of SC. This research indicates Viable Supply Chain Network Design (VSCND) by applying BCT. A new form of two-stage robust optimization is suggested. Facility locations and activation BCT for VSCND is the first stage of decisions; finally, we determine flow transshipment between components in the next stage. The GAMS-CPLEX is used for solving the model. The results show that running BCT will decrease 0.99% in costs. There is an economic justification for using BCT when demand is high. A fix-and-optimize and Lagrange relaxation (LR) generate lower and upper bound to estimate large scale in minimum time. The gap between the main model and fix-and-optimize is better than the LR algorithm. Finally, this research suggests equipping VSCND by BCT that becomes more resilient against demand fluctuation, sustainable, and agile.

Medical waste management (MWM) is an important and necessary problem in the COVID-19 situation for treatment staff. When the number of infectious patients grows up, the amount of MWMs increases day by day. We present medical waste chain network design (MWCND) that contains health center (HC), waste segregation (WS), waste purchase contractor (WPC), and landfill. We propose to locate WS to decrease waste and recover them and send them to the WPC. Recovering medical waste like metal and plastic can help the environment and return to the production cycle. Therefore, we proposed a novel viable MWCND by a novel two-stage robust stochastic programming that considers resiliency (flexibility and network complexity) and sustainable (energy and environment) requirements. Therefore, we try to consider risks by conditional value at risk (CVaR) and improve robustness and agility to demand fluctuation and network. We utilize and solve it by GAMS CPLEX solver. The results show that by increasing the conservative coefficient, the confidence level of CVaR and waste recovery coefficient increases cost function and population risk. Moreover, increasing demand and scale of the problem makes to increase the cost function.

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The Viable Closed-loop Supply Chain Network (VCLSCND) is a new concept that integrates sustainability, resiliency, and agility in a circular economy. We suggest a new form of robust stochastic optimization for this problem by minimizing the weighted expected, maximum and Entropic Value at Risk (EVaR). We proposed this form to increase robustness against demand disruption and energy productivity. Finally, we located CLSC components and assigned flow in the automotive industry. The results show that the cost of VCLSCND is less than without viable and has -0.44% gap. By increasing the conservative coefficient and confidence level, decreasing the allowed maximum energy and increasing the scale of the main model, the cost function, time solution and energy consumption grow. We suggested applying the Fix-and-Optimize algorithm for producing upper bound of large-scale. As can be seen, the gap between this algorithm and the main problem for cost, energy and time solution is approximately 6.10%, -8.28%, and 75.01%.

Blockchain Technology (BCT) is expanding day by day and is used in all pillars of life and projects. In this research, we survey applicable BCT in project management for the first time. We presented a Resource-Constrained Time–Cost-Quality-Energy-Environment Tradeoff Problem by considering BCT, Risk and Robustness (RCTCQEETPBCTRR) in project scheduling. We utilize hybrid robust stochastic programming, worst case, and Conditional Value at Risk (CVaR) to cope with uncertainty and risks. This type of robustification and risk-averse is presented in this research. A real case study is presented in a healthcare project. We utilize GAMS-CPLEX to solve the model. Finally, we analyze finish time, conservative coefficient, the confidence level of CVaR, and the number of scenarios. The most important research result is that applying BCT decreases cost, energy, and pollution and increases quality. Moreover, the total gap between RCTCQEETPBCTRR and without BCT is approximately 2.6%. When compacting finish time happens or if the conservative coefficient increases to 100%, costs, energy, and pollution environment increase, but quality decreases. If the confidence level of CVaR increases, the cost, energy, and environment function functions grow up, and quality is approximately not changed.