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Modeling Vegetable Food Supply Chain by the Prediction of Yield and Demand for a City to Reduce Food Waste

Affiliations

  • Department of Industrial Engineering and Management, R V College of Engineering, Bengaluru – 560 059, Karnataka, India

Abstract


Tons of vegetables go to waste as they are produced in excess than the current demand and go to waste in storage units without any customer to buy them. The objective of the study is to develop a model that helps in effective distribution of vegetable yield demand. Prediction model was developed using regression analysis to find out the future yield so that the farmer can plan the distribution of his produce. Second phase of the study was aimed at reducing transportation using cost integer programming from distributors to retailers while meeting the demand at the same time. The developed model can predict the future yield value which will be closer to the actual yield value. Future demand can also be determined which will be closer to the actual demand. Based on the yield generated and demand comparison, decision can be incorporated in the distribution of vegetables by the farmer to different regions. If the demand is more than yield, tapping of other sources is done. If the demand is lesser, the part of the yield can be sent to other towns and cities where demand has not been met. Food waste problem can be tackled at the source itself by using this model. Wastage can be reduced considerably which is one of the final benefits that will contribute to the societal welfare and development. The integer programming model done for optimizing the cost of distribution provides a feasible solution to the distributors and retailers and satisfying the demand at the same time. Thus, total cost can be minimized without affecting the efficiency of distribution. Distributors and retailers can optimize their distribution effectively while minimizing the transportation cost as much as possible without affecting the demand.

Keywords

Food Supply Chain, Integer Programming, Linear Regression, Winter’s Demand Forecast Model, Yield Prediction Model.

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References


  • Daofang C, Jinfeng Z, Danping L. Cold chain logistics distribution network planning subjected to cost constraints.International Journal of Advanced Science and Technology.2015; 75:1–10.
  • Lyons AC, Maaram A. An examination of multi-tier supply chain strategy alignment in the food industry, International Journal of Production Research. 2015; 52:1911–25.
  • Rong A, Akkerman R, Grunow M. An optimization approach for managing fresh food quality throughout the supply chain. International Journal of Production Economics.2011; 131:421–9.
  • Hsu CI, Hung SF, Li HC. Vehicle routing problem with time-windows for perishable food delivery. Journal of Food Engineering. 2007; 80:465–75.
  • Akkerman R, Farahani P, Grunow M. Quality safety and sustainability in food distribution: a review of quantitative operations management approaches and challenges. OR Spectrum. 2010; 32:863–904.
  • Roghanian E, Sheykhan A, Abendankashi ES. An application of fuzzy TOPSIS to improve the process of SCM in the food industries: A case study of protein products manufacturing company. Decision Science Letters. 2014; 3:17–26.
  • Villarreal B, Garcia D, Rosas I. Eliminating transportation waste in food distribution: A case study. Transportation Journal. 2009; 48(4):72.
  • Bosona T, Gebresenbet G, Nordmark I, Ljungberg D.Integrated logistics network for the supply chain of locally produced food, part I: location and route optimization analyses. Journal of Service Science and Management.2011; 4:174–83.
  • Cheshmberah M, Zahedia MR, Hadizadeh A, Tofighi SM.
  • A mathematical model for optimum single-commodity distribution in the network of chain stores: a case study of food industry. Management Science Letters. 2011; 1:575–82.

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