pattern search algorithm

A significant challenge in the university meal booking is the high No-Show rate that leads to considerable food waste in consequence of facing low price of nutrition system and government subsidizing. This study aims to prevent food waste in university dining halls via predicting actual demand.

To model and solve the problem, an Artificial Neural Network has been used that was performed by weighting the error function with Generalized Pattern Search (GPS). Date, the day of the week, the price level of Food, total number of reservations, total number of reservations by undergraduate students, Masters' students, PhD students and dormitory students and the parallel food have been considered as inputs of the model. The output is the actual demands based on Show's number.

The seven-year data of the meal booking system of a large university in Iran has been examined. This data demonstrated that the food waste rate is close to 10% of the total food reservations. An artificial neural network including weighted error function under GPS optimization was obtained to predict actual demand. Finally, the results of training indicated over 80% waste reduction in surplus daily food production.

The proposed model has the potential to provide an estimation of actual demand. Although adding indicators that influence demand estimation, the proposed model is able to change the actual demand prediction at various levels of risk expected by the university. To avoid food waste and prevent the loss of government subsidies, this precautionary approach can control overproduction.

Index tracking is an important issue in portfolio theory. Index tracking is a passive approach in the portfolio optimization problem based on which finite stock should be selected to track the benchmark index. Enhanced index tracking is a selection of the portfolio with limited stock so that its return is maximized and track error is minimized without buying all stock in benchmark portfolio. The main aim of this paper was to propose a two-stage mixed integer model for enhancing portfolio performance. In order to show the approach performance, top 50 companies were traced. Return, tracking error, excess return and information ratio were used as Portfolio performance measurement. Genetic Algorithm and Pattern Search Algorithm were also used to solve the models.  The findings showed that the two-stage model was better than one stage model. Likewise, pattern search enjoyed higher performance than Genetic Algorithm in the two-stage model. Therefore, two-stage model had higher performance during pattern search algorithm compared to one stage model or Genetic algorithm.