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International Journal of Production Research Volume 43, 2005 - Issue 10
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Original Articles

Comparing deterministic, robust and online scheduling using entropy

H.-S. Gan
&
A. Wirth * Department of Mechanical and Manufacturing Engineering, The University of Melbourne, VIC 3010, Australia
Pages 2113-2134 | Received 01 Nov 2004, Published online: 22 Feb 2007

Citations (8)

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Read on this site (2)

César Martínez-Olvera, Yasser Davizón-Castillo & Jaime Mora-Vargas. (2016) Entropy-based quantification of a product’s BOM blocking effect. Production & Manufacturing Research 4:1, pages 175-189.
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Y R Wu, L H Huatuco, G Frizelle & J Smart. (2013) A method for analysing operational complexity in supply chains. Journal of the Operational Research Society 64:5, pages 654-667.
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Articles from other publishers (6)

Stephen Fox, Tapio Heikkilä, Eric Halbach & Samuli Soutukorva. (2023) Bio-Inspired Intelligent Systems: Negotiations between Minimum Manifest Task Entropy and Maximum Latent System Entropy in Changing Environments. Entropy 25:11, pages 1541.
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Umair Sajid Hashmi, Sunila Akbar, Raviraj Adve, Peter W. Moo & Jack Ding. (2022) Artificial intelligence meets radar resource management: A comprehensive background and literature review. IET Radar, Sonar & Navigation 17:2, pages 153-178.
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Daniel Mueller, Carina Mieth & Michael Henke. (2018) Quantification of Sequencing Flexibility Based on Precedence Graphs for Autonomous Control Methods. Quantification of Sequencing Flexibility Based on Precedence Graphs for Autonomous Control Methods.
Huankai Chen, Frank Z. Wang & Na Helian. (2018) Entropy4Cloud: Using Entropy-Based Complexity to Optimize Cloud Service Resource Management. IEEE Transactions on Emerging Topics in Computational Intelligence 2:1, pages 13-24.
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Donya Rahmani & Reza Ramezanian. (2016) A stable reactive approach in dynamic flexible flow shop scheduling with unexpected disruptions: A case study. Computers & Industrial Engineering 98, pages 360-372.
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Huankai Chen & Frank Z Wang. (2015) Spark on entropy: A reliable & efficient scheduler for low-latency parallel jobs in heterogeneous cloud. Spark on entropy: A reliable & efficient scheduler for low-latency parallel jobs in heterogeneous cloud.

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