Preventing food waste in subsidy-based university dining systems: An artificial neural network-aided model under uncertainty.

Food waste planning at universities is often a complex matter due to the large volume of food and variety of services. A major portion of university food waste arises from dining systems including meal booking and distribution. Although dining systems have a significant role in generating food wastes, few studies have designed prediction models that could control such wastes based on reservation data and behavior of students at meal delivery times. To fill this gap, analyzing meal booking systems at universities, the present study proposed a new model based on machine learning to reduce the food waste generated at major universities that provide food subsidies. Students' reservation and their presence or absence at the dining hall (show/no-show rate) at mealtime were incorporated in data analysis. Given the complexity of the relationship between the attributes and the uncertainty observed in user behavior, a model was designed to analyze definite and random components of demand. An artificial neural network-based model designed for demand prediction provided a two-step prediction approach to dealing with uncertainty in actual demand. In order to estimate the lowest total cost based on the cost of waste and the shortage penalty cost, an uncertainty-based analysis was conducted at the final step of the research. This study formed a framework that could reduce the food waste volume by up to 79% and control the penalty and waste cost in the case study. The model was investigated with cost analysis and the results proved its efficiency in reducing total cost.

Simulation of Quality Death Spirals based on Human Resources Dynamics.

Although the hospital managers always try to improve the quality of the medical services, sometimes their efforts might affect reversely and push the system in what is so commonly called as "the death spirals of quality." The most important reason of falling into these spirals is the lack of a systemic thought that considers the feedback relationships between the numerous effective variables in the system performance, such as human resources service capacity. In this regard, the purpose of the present research is to design and simulate a dynamic human resources service capacity-based model to demonstrate the death spirals of quality phenomenon based on the service time per service and the possibility of error generation along with identifying the policies to cope with them. The system dynamics simulation approach is used to show the dynamics of the capacity of service from the standpoint of human resources. A model is simulated for the services of a hospital clinic as a case study. The simulation results of the designed dynamic model express that applying the desired policies for the case study can provide a good basis for fighting these spirals in a dynamic situation.