Cash flow prediction using artificial neural network and GA-EDA optimization

Cash flow models are one of the spotlights for evaluating a project. The actual data should be modeled then it could be used for the prediction process. In this paper, 996 airplane maintenance basis data are used as a database, and 119 similar data are chosen after clustering. The project is divided into 20 equal periods and first three periods are used for simulating the next point. The predicted data for each point is achieved by using of previous points from the beginning. The model is based on artificial neural network, and it is trained by three algorithms which are Genet-ic Algorithm (GA), Estimation of Distribution Algorithm (EDA), and hybrid GA-EDA method. Two dynamic ratios are used which are dividing the population into two halves, and the other is a ratio without dividing. The ratio would give a proportion to GA and EDA models in the hybrid algorithm, and then the hybrid algorithm could model the system more accurately. For each algorithm, three main errors are calculated which are mean absolute percentage error (MAPE), mean square error (MSE), and root means square error (RMSE). The best result is achieved for hybrid GA-EDA model without dividing the population and the MAPE, RMSE, and MSE values are %0.022, 28944.59 Dollars, and 837789503.79 Dollars, respectively

A Statistical Solution to Mitigate Functional Requirements Coupling Generated from Process (Manufacturing) Variables Integration-part I

AbstractUtilizing the Axiomatic Design (AD) principles to develop a perfect product, design of a manufacturing system with minimal complexity is required. For the purpose of reducing the manufacturing system complexity, theoretically, it is preferred to integrate multiple Process Variables (PVs) of the product into a single process unit. However, due to significant presence of some active noise factors, this integration practice may result in failing to maintain the independence among some of Functional Requirements (FRs) of the product. This event is the result of statistical causal relationships unintentionally developed among a subset of the integrated PVs. In such a condition, the AD's Independence Axiom cannot be successfully satisfied and reaching a system with minimal complexity is inconceivable, even though an uncoupled or decoupled system design is apparently presented. To mitigate this kind of FRs coupling generated from the PVs integration, this study proposes partial & semi-partial correlation analysis as a statistical solution to identify the most appropriate integration choices where integrating a subset of the PVs is inevitable. Furthermore, based on the Taguchi's loss function, a quantitative criterion is established to fairly compare any two non-ideal manufacturing system designs and choose the one with relatively lower loss. The proposed approach explained in this study is verified based on hypothetical data