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International Journal of Production Research Volume 50, 2012 - Issue 10
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Original Articles

Comparisons of bi-objective genetic algorithms for hybrid flowshop scheduling with sequence-dependent setup times

S.M. Mousavi Department of Technical and Engineering, Faculty of Industrial Engineering, Islamic Azad University, Noshahr Branch, Iran
,
M. Zandieh Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, IranCorrespondence[email protected]
&
M. Amiri Department of Industrial Management, Management and Accounting Faculty, Allameh Tabatabaei University, Tehran, Iran
Pages 2570-2591 | Received 03 Apr 2009, Accepted 10 Nov 2010, Published online: 04 Aug 2011

Citations (7)

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Read on this site (2)

Kan Wu. (2014) Classification of queueing models for a workstation with interruptions: a review. International Journal of Production Research 52:3, pages 902-917.
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Shaokuan Chen, Tinkin Ho & Baohua Mao. (2013) Maintenance schedule optimisation for a railway power supply system. International Journal of Production Research 51:16, pages 4896-4910.
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Articles from other publishers (5)

Janis S. Neufeld, Sven Schulz & Udo Buscher. (2023) A systematic review of multi-objective hybrid flow shop scheduling. European Journal of Operational Research 309:1, pages 1-23.
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Edzard Weber, Anselm Tiefenbacher & Norbert Gronau. (2019) Need for Standardization and Systematization of Test Data for Job-Shop Scheduling. Data 4:1, pages 32.
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S. M. Mousavi, I. Mahdavi, J. Rezaeian & M. Zandieh. (2016) An efficient bi-objective algorithm to solve re-entrant hybrid flow shop scheduling with learning effect and setup times. Operational Research 18:1, pages 123-158.
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Ali Allahverdi. (2015) The third comprehensive survey on scheduling problems with setup times/costs. European Journal of Operational Research 246:2, pages 345-378.
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Shao-kuan Chen, Tin-kin Ho, Bao-hua Mao & Yun Bai. (2014) A bi-objective maintenance scheduling for power feeding substations in electrified railways. Transportation Research Part C: Emerging Technologies 44, pages 350-362.
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