A robust, resilience multi-criteria decision-making with risk approach: a case study for renewable energy location.

Regarding hard situations like war, the increasing cost of extraction and exploration of fossil fuels make governments move toward green and clear renewable energy (RE). As a result, we propose a novel multi-criteria decision-making (MCDM) method for RE location (REL) for the first time. This model suggests a Robust, Resilience MCDM with Risk approach (RRMCDMR) for REL. We propose a risk approach by adding a risk function in MCDM. A robust convex approach is used to tackle the uncertainty of the model for the real world. We compare the RRMCDMR problem in a wind farm location in Iran with different risk coefficient functions. As defined, Khaf, Nehbandan, and Esfarayan are in locations one to three in all modes. We changed the normalized risk function and suggested two other risk functions that can help risk-averse and risk-neutral decision-makers. We varied the robust convex coefficient and considered that by increasing the robust convex coefficient, the alternative score increased.

Viable closed-loop supply chain network by considering robustness and risk as a circular economy.

The viable closed-loop supply chain network (VCLSCND) is a new concept that integrates sustainability, resiliency, and agility into a circular economy. We suggest a hybrid robust stochastic optimization by minimizing the weighted expected, maximum, and entropic value at risk (EVaR) of the cost function for this problem. This form considers robustness against demand disruption. Finally, CLSC components are located, and quantity flows are determined in the automotive industry. The results show that the VCLSCND cost is less than not considering viability and has a - 0.44% gap. We analyze essential parameters. By increasing the conservative coefficient, confidence level, and the scale of the main model, decreasing the allowed maximum energy, the cost function, time solution, and energy consumption grow. We suggested applying the Fix-and-Optimize algorithm for producing an upper bound for 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%.

Resource-constrained time-cost-quality-energy-environment tradeoff problem by considering blockchain technology, risk and robustness: a case study of healthcare project.

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.

Viable medical waste chain network design by considering risk and robustness.

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.