Journal of Quality Engineering and Management

Abstract As time goes on and crises increase in societies, organizations are increasingly exposed to disruption. These crises can be of natural (such as earthquakes, floods, and fires) or human (such as terrorist attacks, infectious diseases, and intentional or inadvertent employee errors). Therefore, organizations need to be resilient to protect themselves from harmful consequences. The basic aspect of resilience involves the ability of an element to return to normal after disruption and resource allocation. Obviously, in any organization, the primary goal is to allocate the least resources to recover operations and to bring activities back to the tolerance threshold so that destructive events do not stop vital activities. In this paper, a quantitative model for resource allocation is presented, which minimizes the lack of resilience. The problem has a basic assumption, that there is a shortage of resources in at least one of the available resources due to excessive demand. After solving the model by numerical experiment, the results of the model were described and it was found that destructive events were retrieved before the tolerance threshold.

Abstract Process analysis is closely related to process optimization and in the optimization, the discussion of resources plays a major role. Since the optimal set of resources, especially in the event of unexpected events will have variable effects on risk, process performance and efficiency can be similar to the optimal stock portfolio selection model. The purpose of this paper is to provide a mathematical model for allocating human resources using the Markowitz model to the days of the week based on skills and costs, which the objective function minimizes the risk and maximizes the efficiency of resources. The proposed model includes a combination of risk and return assessments to find the best resource portfolio in critical situations. The study also used the Epsilon constraint method. The innovations of this research is the application of Markowitz model to optimize resource allocation and risk-based allocation of resources in the process. The proposed model has been used in a case study of a urology subspecialty kidney center. Numerical results show that using the designed model, resources can be allocated in such a way that it has optimal returns and risk, in order to produce the maximum amount of output in critical situations.