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Supply chain network design using an integrated neuro-fuzzy and MILP approach: A comparative design study

机译:使用集成的神经模糊和MILP方法进行供应链网络设计:一项比较设计研究

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摘要

In this study, an integrated supply chain (SC) design model is developed and a SC network design case is examined for a reputable multinational company in alcohol free beverage sector. Here, a three echelon SC network is considered under demand uncertainty and the proposed integrated neuro-fuzzy and mixed integer linear programming (MILP) approach is applied to this network to realize the design effectively. Matlab 7.0 is used for neuro-fuzzy demand forecasting and, the MILP model is solved using Lingo 10.0. Then Matlab 7.0 is used for artificial neural network (ANN) simulation to supply a comparative study and to show the applicability and efficiency of ANN simulation for this type of problem. By evaluating the output data, the SC network for this case is designed, and the optimal product flow between the factories, warehouses and distributors are calculated. Also it is proved that the ANN simulation can be used instead of analytical computations because of ensuring a simplified representation for this method and time saving.
机译:在这项研究中,开发了一个集成的供应链(SC)设计模型,并研究了无酒精饮料领域知名跨国公司的SC网络设计案例。这里,在需求不确定的情况下考虑了三级SC网络,并将所提出的集成神经模糊和混合整数线性规划(MILP)方法应用于该网络以有效地实现设计。 Matlab 7.0用于神经模糊需求预测,而MILP模型使用Lingo 10.0求解。然后将Matlab 7.0用于人工神经网络(ANN)仿真,以进行比较研究,并证明ANN模拟在此类问题上的适用性和效率。通过评估输出数据,设计出针对这种情况的SC网络,并计算出工厂,仓库和分销商之间的最佳产品流量。还证明了可以使用ANN模拟代替分析计算,因为可以确保该方法的简化表示并节省时间。

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