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International Journal of Production Research Volume 45, 2007 - Issue 8
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Original Articles

Application of distributed intelligence to reduce the bullwhip effect

D. De La Fuente Escuela Politècnica Superior de Ingeniería, Campus de Viesques s/n 33204, Gijón, SpainCorrespondence[email protected]
&
J. Lozano Escuela Politècnica Superior de Ingeniería, Campus de Viesques s/n 33204, Gijón, Spain
Pages 1815-1833 | Received 01 Oct 2005, Published online: 09 Mar 2007

Citations (18)

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Ehsan Badakhshan, Paul Humphreys, Liam Maguire & Ronan McIvor. (2020) Using simulation-based system dynamics and genetic algorithms to reduce the cash flow bullwhip in the supply chain. International Journal of Production Research 58:17, pages 5253-5279.
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Mehrdokht Pournader, Yangyan Shi, Stefan Seuring & S.C. Lenny Koh. (2020) Blockchain applications in supply chains, transport and logistics: a systematic review of the literature. International Journal of Production Research 58:7, pages 2063-2081.
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Roberto Montanari, Gino Ferretti, Marta Rinaldi & Eleonora Bottani. (2015) Investigating the demand propagation in EOQ supply networks using a probabilistic model. International Journal of Production Research 53:5, pages 1307-1324.
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Florian Klug. (2013) The internal bullwhip effect in car manufacturing. International Journal of Production Research 51:1, pages 303-322.
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Matloub Hussain & Hussein Saber. (2012) Exploring the bullwhip effect using simulation and Taguchi experimental design. International Journal of Logistics Research and Applications 15:4, pages 231-249.
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Matteo Coppini, Chiara Rossignoli, Tommaso Rossi & Fernanda Strozzi. (2010) Bullwhip effect and inventory oscillations analysis using the beer game model. International Journal of Production Research 48:13, pages 3943-3956.
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Articles from other publishers (12)

Marc Wiedenmann & Andreas Größler. (2019) The impact of digital technologies on operational causes of the bullwhip effect – a literature review. Procedia CIRP 81, pages 552-557.
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André Jalbut & Jaime Simão Sichman. 2019. Multi-Agent-Based Simulation XIX. Multi-Agent-Based Simulation XIX 45 58 .
Zhang Wen, Zhang Ya-Ming, Chen Jinbo & Leng Kaijun. (2016) Research on a scheduling mechanism in a complex system based on TOC. Chaos, Solitons & Fractals 89, pages 335-347.
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José Costas, Borja Ponte, David de la Fuente, Raúl Pino & Julio Puche. (2015) Applying Goldratt’s Theory of Constraints to reduce the Bullwhip Effect through agent-based modeling. Expert Systems with Applications 42:4, pages 2049-2060.
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Francesco Costantino, Giulio Di Gravio, Ahmed Shaban & Massimo Tronci. (2014) The impact of information sharing and inventory control coordination on supply chain performances. Computers & Industrial Engineering 76, pages 292-306.
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Borja Ponte, Raúl Pino & David de la Fuente. 2014. Transactions on Computational Collective Intelligence XVII. Transactions on Computational Collective Intelligence XVII 1 21 .
Umut Tosun, Tansel Dokeroglu & Ahmet Cosar. (2013) A New Parallel Genetic Algorithm for Reducing the Bullwhip Effect in an Automotive Supply Chain. IFAC Proceedings Volumes 46:9, pages 70-74.
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Matloub Hussain, Paul R. Drake & Dong Myung Lee. (2012) Quantifying the impact of a supply chain's design parameters on the bullwhip effect using simulation and Taguchi design of experiments. International Journal of Physical Distribution & Logistics Management 42:10, pages 947-968.
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Matloub Hussain & Paul R. Drake. (2011) Analysis of the bullwhip effect with order batching in multi‐echelon supply chains. International Journal of Physical Distribution & Logistics Management 41:10, pages 972-990.
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Matloub Hussain & Paul R. Drake. (2011) Analysis of the bullwhip effect with order batching in multi‐echelon supply chains. International Journal of Physical Distribution & Logistics Management 41:8, pages 797-814.
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Ranjan Bhattacharya & Susmita Bandyopadhyay. (2010) A review of the causes of bullwhip effect in a supply chain. The International Journal of Advanced Manufacturing Technology 54:9-12, pages 1245-1261.
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Vassilios Vassiliadis & Giorgos Dounias. 2011. Supply Chain Optimization, Design, and Management. Supply Chain Optimization, Design, and Management 1 31 .

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