基于人工神经网络的企业职工食品需求量估算
An Estimation of Personnel Food Demand Quantity for Businesses by Using Artificial Neural Networks
摘要：
今天，许多公共或私人机构为在自己组织工作的人员提供专业的食品服务。关于上述服务的规划，存在一些障碍，因为在这些机构工作的人员人数普遍很多，而且这些人员由于个人或机构原因离开机构。因此，很难确定每日的食品需求，这给机构造成了成本、时间和劳动力的损失。使用统计或探试法来消除或至少最小化这些损失。在这项研究中，提出了一个人工智能模型，它估计每天的食品需求量使用人工神经网络的企业。这些数据来自一家私人机构的食堂数据库，该机构可容纳110人，每天提供膳食，并在不同级别提供服务，涵盖了过去两年（2016-2018）。该模型使用MATLAB软件包程序创建。模型的性能由回归值、平均绝对百分比误差（MAPE）和均方误差（MSE）决定。在神经网络模型的训练中，采用前馈反向传播网络结构。作为实验结果获得的最佳模型是多层（8-10-10-1）结构，分别具有0.9948的训练R比、0.9830的测试R比和0.003783的出错率。实验结果表明，该模型具有较低的出错率，较高的性能和使用人工神经网络的需求估计的积极效果。
英文摘要：
Today, many public or private institutions provide professional food service for personnels working in their own organizations. Regarding the planning of the said service, there are some obstacles due to the fact that the number of the personnel working in the institutions is generally high and the personnel are out of the institution due to personal or institutional reasons. Because of this, it is difficult to determine the daily food demand, and this causes cost, time and labor loss for the institutions. Statistical or heuristic methods are used to remove or at least minimize these losses. In this study, an artificial intelligence model was proposed, which estimates the daily food demand quantity using artificial neural networks for businesses. The data are obtained from a refectory database of a private institution with a capacity of 110 people serving daily meals and serving at different levels, covering the last two years (2016-2018). The model was created using the MATLAB package program. The performance of the model was determinde by the Regression values, the Mean Absolute Percentage Error (MAPE) and the Mean Squared Error (MSE). In the training of the ANN model, feed forward back propagation network architecture is used. The best model obtained as a result of the experiments is a multi-layer (8-10-10-1) structure with a training R ratio of 0,9948, a testing R ratio of 0,9830 and an error rate of 0,003783, respectively. Experimental results demonstrated that the model has low error rate, high performance and positive effect of using artificial neural networks for demand estimating.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
