Titlebook: Intelligent Decision-making Models for Production and Retail Operations; Zhaoxia Guo Book 2016 Springer-Verlag Berlin Heidelberg 2016 Comp

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An Extreme Learning Machine-Based Intelligent Decision-Making Model for Multivariate Sales Forecastl is developed to provide effective forecasts for this problem by integrating a data preparation and preprocessing (DPP) module, a harmony search-wrapper-based variable selection (HWVS) module and a multivariate intelligent forecaster (MIF) module. The HWVS module selects out the optimal input varia

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An Intelligent Optimization Model for Order Scheduling at Plant Level,chromosome, is developed to find the optimal order scheduling solution. Extensive experiments are conducted to evaluate the proposed approach based on real-world production data. The experimental results demonstrate the effectiveness of the proposed approach.

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application of intelligent decision-making techniques.Offer.This book provides an overview of intelligent decision-making techniques and discusses their application in production and retail operations. Manufacturing and retail enterprises have stringent standards for using advanced and reliable tec

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A Bilevel Multi-parent Genetic Optimization Model for Flexible Assembly Line Balancing with Work-Shoduct to an appropriate workstation when it needs to be processed. A series of experiments are conducted based on industrial data to validate the proposed optimization approach. The experimental results demonstrate the effectiveness of the proposed approach to solve the FALB problem.

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An Evolution Strategy-Based Multi-objective Optimization Model for Order Planning with Multiple Proinvestigated production environment. Experiments based on industrial data are conducted to evaluate the effectiveness of the proposed approach. Experimental results show that the proposed approach can effectively solve the investigated problem by providing production planning solutions superior to industrial solutions.

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A Harmony Search-Based Hybrid Intelligent Optimization Model for Order Planning with Learning Effec effectiveness of the proposed approach based on industrial data. Results demonstrate that (1) the proposed approach can handle the problem investigated effectively; and (2) the HSPO process can generate the optimization performance superior to those generated by a multi-objective genetic algorithm (NSGA-II)-based process and an industrial method.