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Intelligent Decision-making Models for Production and Retail Operations de Zhaoxia Guo

Intelligent Decision-making Models for Production and Retail Operations

Autor Zhaoxia Guo
en Limba Engleză Hardback – 6 iul 2016
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 techniques to improve decision-making processes, since these processes have significant effects on the performance of relevant operations and the entire supply chain. In recent years, researchers have been increasingly focusing attention on using intelligent techniques to solve various decision-making problems. The opening chapters provide an introduction to several commonly used intelligent techniques, such as genetic algorithm, harmony search, neural network and extreme learning machine. The book then explores the use of these techniques for handling various production and retail decision-making problems, such as production planning and scheduling, assembly line balancing, and sales forecasting.
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Specificații

ISBN-13: 9783662526798
ISBN-10: 3662526794
Pagini: 300
Ilustrații: XI, 324 p. 84 illus.
Dimensiuni: 155 x 235 x 21 mm
Greutate: 0.69 kg
Ediția:1st ed. 2016
Editura: Springer Berlin, Heidelberg
Colecția Springer
Locul publicării:Berlin, Heidelberg, Germany

Cuprins

From the Contents: Introduction.- Fundamentals of intelligent decision-making techniques.- An intelligent decision-making framework for production planning and control.- An intelligent optimization model for order scheduling at factory level.

Recenzii

“The book aims at providing an overview of various models and approaches to support decision-making in production and retail operations. … this book results interesting for researchers and students who want to approach the topic of intelligent decision-making models for the first time … . is also a precious instrument for those researchers who are familiar with some of the proposed intelligent techniques and models and want to increase their knowledge about them and the other intelligent techniques … .” (Elisa Negri, Production Planning & Control, Vol. 28 (15), August, 2017)

Notă biografică

Zhaoxia Guo

Research Areas
Production scheduling and optimization, Sales forecasting, Decision support systems, Computational intelligence

Education
09/2004-08/2007, Ph.D , The Hong Kong Polytechnic University, Hong Kong
09/2000-03/2003, M.Sc. in Control Theory and Control Engineering, Donghua University, Shanghai, China
09/1996-06/2000, Bachelor in Automatic Control, Donghua University, Shanghai, China

Working Experience
2012.09- Present, Associate Professor, Business School, Sichuan University, China
2013.12- 2014.12, Honorary associate, University of Wisconsin-Madison, US
2008.12-2012.08, Postdoctoral fellow, The Hong Kong Polytechnic University, Hong Kong
2007.09-2008.11, IT/System In Charge, Genexy Company Limited, Hong Kong
2003.3-2004.8, Assistant lecturer, Donghua University, China

Awards
2nd Prize in IMB Innovation Award 2009 (Category: Research and Development), Awardedby The European Commission of the Industry Research, IMB 2009 World of Textile Processing, German, 3rd Awardee
Gold Award in Most Innovative Use of EPC/RFID, Hong Kong RFID Awards 2012, Hong Kong, 2nd Awardee

Professional Activities
Editorial member, International Journal of Engineering Mathematics, Advanced Engineering Technology and Application;
Member, International Institute of Forecasters, Association for Information Systems;
The 10th IEEE International Conference on Industrial Informatics (INDIN 2012), Session Chair;
Invited reviewer: Applied Mathematical Modeling, Applied Soft Computing, Computers & Industrial Engineering, European Journal of Operational Research, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Industrial Informatics, IEEE Transactions on Neural Networks, Information Sciences, International Journal of Advanced Manufacturing Technology, International Journal of Production Research, Neural Computing and Applications, Textile Research Journal, etc.

Research Projects (Selected)
On Supply Chain Scheduling Integrating Production and Distribution Operations and its Hybrid Intelligent Decision-making Models, Sponsored by the National Natural Science Foundation of China (2014-2016), Principal Investigator
Modeling and Optimization for Integrated Scheduling of Production and Transportation in MTO supply chain, Sponsored by the Research Funds for Outstanding Youth Scholars of Sichuan University (2014-2016), Principal Investigator
Development of Index System for Low-carbon Logistics Evaluation, Sponsored by the Key Logistics Research Project of Sichuan Province (2014-2015), Co-investigator
Development of a Decision-making Model for Dynamic Supply Chain Network optimization, Sponsored by Fundamental Research Funds for the Central Universities of China (2013-2015), Principal Investigator.
Hybrid Intelligent System for Product demand Forecasting in Apparel Supply Chains, Sponsored by Postdoctoral Fellowship Fund of HKPolyU (2010-2012), Applicant.
Development of a Fashion Sales Forecasting Decision Support System for Retailing, Sponsored by HKRITA and 4 Apparel Retail Companies, (2008-2010), Project Manager

Publications (Selected)

Monographs
[1]Wong W.K., Guo Z.X., Leung S.Y.S. (2013), Optimizing decision making in the apparel supply chain using artificial intelligence (AI): From production to retail, Woodhead Publishing Limited, Cambridge, UK
[2]Wong W.K., Guo Z.X. (2014), Fashion supply chain management using radio frequency identification (RFID) technologies, Woodhead Publishing Limited, Cambridge, UK
Journal Papers
[3]Guo Z.X., Yang C., Wang, W. and Yang J.. (2015) "Harmony search-based multi-objective optimization model for multi-site order planning with multiple uncertainties and learning effects", Computers & Industrial Engineering, In Press, DOI: 10.1016/j.cie.2015.01.023.
[4]Guo Z.X., Ngai EWT, Yang C., and Liang XD. (2015) "An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment", International Journal of Production Economics. 159: 16-28.
[5]Guo Z. X., Wong W.K. and Guo C.  (2014). "A cloud-based intelligent decision-making system for order tracking and scheduling in apparel manufacturing". International Journal of Production Research. 52(4): 1100–1115.
[6]Wong W. K., Guo Z. X. and Leung S. Y. S. (2014). "Intelligent multi-objective decision-making model with RFID technology for production planning". International Journal of Production Economics. 147, Part C, 647–658
[7]Guo, Z.X., Wong W.K., and Leung S. Y. S. (2013). A hybrid intelligent model for order allocation planning in make-to-order manufacturing. Applied Soft Computing, 13(3): 1376–1390.
[8]Guo Z. X. and Wong W.K. (2013). A multivariate intelligent decision-making model for retail sales forecasting. Decision Support Systems. 55(1): 247-255. [9]Guo Z. X., Wong W.K., Zhi Li and Peiyu Ren (2013). Modeling and Pareto optimization of multi-objective order scheduling problems in production planning. Computers & Industrial Engineering, 64(4): 972-986.
[10]Guo, Z.X., Wong W.K., and Li Min (2012). Sparsely connected neural network-based time series forecasting. Information Sciences. 193(1): 54–71.
[11]Wong W.K., Leung S.Y.S., Guo Z.X. (2012). Feedback controlled particle swarm optimization and its application in time-series prediction. Expert Systems with Applications. 39(10): 8557-8572.
[12]Wong W.K., Leung S.Y.S., Guo Z.X., et al. (2012). Intelligent product cross-selling system with radio frequency identification technology for retailing.  International Journal of Production Economics 135(1): 308-319.
[13]Guo, Z.X., Wong W.K., Leung S.Y.S. and Li Min (2011). "Applications of artificial intelligence in the apparel industry: A review". Textile Research Journal 81(18): 1871-1892.
[14]Wong W.K., Guo Z.X. and Leung S.Y.S. (2010). "Partially Connected Feedforward Neural Networks on Apollonian Networks." Physica A 389(22): 5298-5307.
[15]Wong W.K. and Guo Z.X. (2010). "A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm." International Journal of Production Economics 128(2): 614-624.
[16]Wong W.K. and Guo Z.X. (2010). "A hybrid approach for packing irregular patterns using evolutionary strategies and neural network." International Journal of Production Research 48(20): 6061 – 6084.
[17]Guo, Z.X., Wong W.K., Leung S.Y.S. and Fan J.T. (2009). "Intelligent production control decision support system for flexible assembly lines." Expert Systems with Applications 36(3): 4268-4277.
[18]Guo Z.X., Wong W.K., et al. (2008). "Genetic optimization of order scheduling with multiple uncertainties.” Expert Systems with Applications 35:1788–1801.
[19]Guo Z.X., Wong W.K., et al. (2008). "A genetic algorithm based optimization model for solving the flexible assembly line balancing problem with work-sharing and workstation revisiting." IEEE Transactions on Systems Man and Cybernetics Part C - Applications and Reviews 38(2):218-228.
[20]Guo Z.X., Wong W.K., et al. (2008). "A genetic-algorithm-based optimization model for scheduling flexible assembly lines." International Journal of Advanced Manufacturing Technology 36(1-2):156-168.
[21]Guo Z.X., Wong W.K., et al. (2006). "Mathematical model and genetic optimization for the job shop scheduling problem in a mixed- and multi-product assembly environment: A case study based on the apparel industry". Computers & Industrial Engineering 50(3), 202-219.

Conference Papers
[22]Yang C., Guo Z.X., and Liu L. (2014). Comparison Study on Algorithms for Vehicle Routing Problem With Time Windows. The 21st International Conference on Industrial Engineering and Engineering Management 2014 (IEEM 2014), Zhuhai, China.
[23]Yang Can, Ngai E.W.T., Guo Z.X.*, Luo, Li, An Information System Framework and Prototype for Collaborative and Standardized Chinese Liquor Production, 20th Americas Conference on Information Systems, AMCIS 2014, Savannah, GA, USA, 2014
[24]Guo Z.X., Chen L., and Yang J. (2014). An intelligent multi-objective optimization approach for multi-site order planning in MTO manufacturing. the World Congress on Engineering and Computer Science 2014, WCECS 2014, San Francisco, USA.
[25]Guo Z.X., Guo Chunxiang (2014),  A Cloud-based Decision Support System Framework for Order Planning and Tracking, International Conference on Management Science and Engineering Management, Proceedings of the Seventh International Conference on Management Science and Engineering Management, Lecture Notes in Electrical Engineering  241, pp 85-98
[26]Guo Z.X., Yang Can (2013), Development ofproduction tracking and scheduling system: A cloud-based architecture, 2013 International Conference on Cloud Computing and Big Data (CloudCom-Asia), 2013, Fuzhou, China
[27]Guo Z.X. (2013), An Intelligent Decision-making Model for Multivariate Demand Forecasting, Presented at the 33rd International Symposium on Forecasting, 23-26 June, Souel, Korea.
[28]Guo Z.X., Li M., Wong W.K. (2012). Intelligent multivariate sales forecasting using wrapper approach and neural networks, IEEE INDIN2012 - IEEE International Conference on Industrial Informatics, Beijing, China.
[29]Wong W.K., Guo Z.X. and Mok P.Y. (2012) A hybrid intelligent model for order allocation planning in make-to-order manufacturing. Proceedings of World Academy of Science, Engineering and Technology 2012, Paris, France.
[30]Guo, Z.X., Wong W.K. and Leung S.Y.S. (2011). "RFID-based intelligent production planning systems for apparel manufacturing", The Fiber Society 2011 Spring Conference, pp: 68-69. Hong Kong, China.

Textul de pe ultima copertă

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 techniques to improve decision-making processes, since these processes have significant effects on the performance of relevant operations and the entire supply chain. In recent years, researchers have been increasingly focusing attention on using intelligent techniques to solve various decision-making problems. The opening chapters provide an introduction to several commonly used intelligent techniques, such as genetic algorithm, harmony search, neural network and extreme learning machine. The book then explores the use of these techniques for handling various production and retail decision-making problems, such as production planning and scheduling, assembly line balancing, and sales forecasting.

Caracteristici

Presents applications of intelligent techniques in production and retail operations Brings together up-to-date information on the theory and application of intelligent decision-making techniques Offers an exposition of the state of the art in intelligent techniques for complicated production and retail decision-making problems with realistic features Includes real industrial data and detailed experimental results that validate the methodologies presented Includes supplementary material: sn.pub/extras