Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2020/r/munzur/6805
  Humanitarian Logistics Management After A Disaster: An Earthquake Case

  Kuvvetli, Yusuf; Göçmen, Elifcan

  Post-disaster relief logistics includes logistics activities in the shortest time. Inventory routing problemshave decided routing decisions considering inventory levels during the planning horizon at minimumcost. In this study, an inventory routing problem for the distribution after the disaster has been proposed.The problem aims the distribution of the supplies needed considering available inventory levels atminimum time. Distribution amount and the routes under the constraints of routing and inventoryamounts for affected people have been decided. An integer programming approach to solve the problemhas been proposed and solved. The proposed model is solved for a case of Van earthquake whichoccurred at 2011 in Turkey. The results from the case study show us that determining the inventory androuting decisions simultaneously can provide less logistic activities. In addition to this, holding inventoryin the central depot can ensure to respond the emergency needs safely and quickly.

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  AtıfOpenAlex7·TR Dizin1

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• MakaleYalnız künye2020/r/munzur/6699
  Prediction of Citrus Fruits Production using Artificial Neural Networks and Linear Regression Analysis

  Kuvvetli, Yusuf; Göçmen, Elifcan

  Accurate and timely prediction of fruits production plays a significant role in the agriculture industry. Therefore, it is very important to predict of citrus fruits production. In this study, prediction the production amount of different citrus fruits for a city of Turkey (Adana) is aimed. Orange, mandarin and bitter orange are included as citrus products and the production amounts of ten years are used as dataset. Artificial neural network (ANN) and linear regression analysis are performed for predicting the production amounts. A feed forward neural network is proposed with regarding some inputs such as districts of Adana, product types, product specific plant area, average yield per tree, number of fruitless trees, number of fruit trees, total number of trees, population, inflation rate, total fruit area, temperature, average rainfall. The obtained results in which the R2 values are greater than 0.98 for all datasets show us that the proposed method can predict the production amount accurately regarding the input parameters.

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  AtıfOpenAlex3

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