Optimized Warehouse Management of Perishable Goods

Abstract

In recent years, food wastage becomes the major problem of the world and researchers indicate that 20-60% of the total production is lost in the food supply chain.[1] Due to perishable nature and the cost of the products fresh food companies face more challenges throughout the supply chains. An order proposal is generated for all the products for a time period of a week by the integration of Machine Learning and loud and also taking into supply chain with some barriers such as supplier delivery times and also the maximum and minimum number of orders. The whole process of prediction is done using Random Forest Regression algorithm. This paper focuses particularly on perishable goods and analyzed based on the accuracy of the training and testing data.

Designing an Energy-Efficient Transportation Network to Transport Perishable Crops: An Aggregated VRP and X-means Clustering Approach

“…Kumar et al [ 12 ] also optimized the warehouse management of perishable goods for retailers by generating an order proposal through Machine Learning using Random Forest Regression Algorithm by considering barriers such as supplier delivery times, and maximum and minimum number of orders. The benefits mentioned were reduction of wastages, reduction of loss, no shortages of goods, quality of food products, and enhanced sales.…”