Decision support model for the Federal District family farmer's tactical planning for the green basket program

The Public Calls for the purchase of Green Food Baskets from the Agricultural Production Acquisition Program were created by the Federal District government to serve people in a situation of food vulnerability and at the same time support family farmers who, as a result of the closure of free markets and restaurants for on-site consumption, were indirectly affected by the pandemic of COVID-19. The program's Green Baskets are made up of a selection of fruits, vegetables and greens, divided into groups, with each basket consisting of a minimum amount in kilograms of each group. Between 2020 and 2021 four public calls were made. It is expected to be relevant a decision support model for the farmer, using mathematical programming, through constraints of quantity demanded and quantity of available products, directing the tactical planning of the family farmer with respect to the best combination of food, aiming to minimize the costs of composition of the baskets and compliance with the rules of the public notice. Thus, this work proposes a mathematical model to support the decision of the farmer who wishes to join the Green Food Basket program, with application in Excel's Solver. The results of the simulations showed that the amounts paid by the government for Cestas Verdes have become less and less beneficial to the producers.

Deep Learning Approach in Predicting Property and Real Estate Indices

The real estate market is one of the most impacted sectors from the Corona Virus Disease 2019 (COVID-19) pandemic that happened in early 2020 globally. Here, we tried to apply an extension of the Long Short-Term Memory (LSTM) deep learning method, known as the Bidirectional LSTM (Bi-LSTM) networks for stock price prediction. Our focus is on six stocks that were included in the LiQuid45 (LQ45) property and real estate sectors. A simple three-layers Bi-LSTM network is proposed for predicting the stocks’ closing prices. We found that the prediction results fall in the reasonable prediction category, except for Pembangunan Perumahan Tbk (PTPP). Bumi Serpong Damai Tbk (BSDE) got the highest accuracy result with more than 90% score, while PTPP got the lowest score with less than 8% score. The proposed Bi-LSTM network could provide a baseline result for developing a good trading strategy. © Al-Zaytoonah University of Jordan (ZUJ).