Predictive models for rail-wagon detention in food grain logistics: a technological intervention

IF 0.5 Q4 MANAGEMENT
N. Sawant, V. V. Panicker, Anoop Kezhe Perumpadappu
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引用次数: 1

Abstract

This work deals with the movement of food grains in India undertaken by a food grain procurement and storage organisation. The movement is primarily achieved through the railway network, followed by the road network. The scope of the work is confined to the movement of food grains in Kerala region through railway network. This work applies machine learning algorithms to predict the occurrence of rail-wagon detention in the warehouses. Classification models are developed to predict the occurrence of detention at warehouses, and regression models are developed to predict the detention hours, based on the historical data. Popular algorithms used in this work are logistic regression, k-Nearest Neighbour, Naive Bayes, decision tree, random forest, support vector machine and multiple linear regressions. Various performance parameters are used to evaluate the different models, and the best model is chosen for further prediction.
粮食物流中铁路货车滞留预测模型的技术干预
这项工作涉及粮食采购和储存组织在印度的粮食流动。这一运动主要通过铁路网实现,其次是公路网。这项工作的范围仅限于喀拉拉邦地区粮食通过铁路网的运输。这项工作应用机器学习算法来预测仓库中铁路货车滞留的发生。根据历史数据,开发了分类模型来预测仓库滞留的发生情况,并开发了回归模型来预测滞留时间。这项工作中使用的流行算法有逻辑回归、k近邻、朴素贝叶斯、决策树、随机森林、支持向量机和多元线性回归。使用各种性能参数来评估不同的模型,并选择最佳模型进行进一步预测。
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来源期刊
CiteScore
1.20
自引率
12.50%
发文量
17
期刊介绍: Today"s businesses have become extremely complex. The interplay of the three Cs, viz. consumers, competition and convergence, has thrown up new challenges for organisations all over the world. Sensitivity of economies to the external environment coupled with the turbulent process of globalisation has added the highest degree of uncertainty and unpredictability to business processes. To top it all, the effect of globalisation has shifted the balance of power in favour of the customer, though it may have opened a plethora of opportunities for all, in the form of variety and choice. For a variety of reasons, the pressures of competitive forces have enhanced product changes, supercharged by shortening product and technology development lifecycles.
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