Volume 1, 2014
|Number of page(s)||13|
|Published online||09 September 2014|
- S.M. Johnson, Optimal two and three-stage production schedules with set up times included, Naval Research Logistics Quarterly 1 (1954) 61–68. [CrossRef]
- W.H. Yang, A study on the intelligent neural network training using the electromagnetism algorithm, Unpublished Master Thesis, Dept. of Industrial Engineering and Management, I-Shou University, Kaohsiung County, Taiwan, 2002.
- R. Ruiz, T. Stützle, An Iterated Greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives, European Journal of Operational Research 187, 3 (2008) 1143–1159. [CrossRef]
- M. Khalili, M.J. Tarokh, B. Naderi, Using electromagnetism algorithm for determining the number of kanbans in a multi-stage supply chain system, Journal of Industrial Engineering 6 (2010) 63–72.
- P. Wu, W.-H. Yang, N.-C. Wei, An electromagnetism algorithm of neural network analysis – an application to textile retail operation, Journal of the Chinese Institute of Industrial Engineers 21 (2004) 59–67. [CrossRef]
- M.S. Salvador, A solution to a special case of flow shop scheduling problems, in: S.E. Elmaghraby (Ed.), Symposium of the Theory of Scheduling and its Applications, Springer, New York, 1973, pp. 83–91. [CrossRef]
- R. Linn, W. Zhang, Hybrid flow shop scheduling: a survey, Computers & Industrial Engineering 37, 1–2 (1999) 57–61. [CrossRef]
- H. Wang, Flexible flowshop scheduling: optimum, heuristics, and artificial intelligence solutions, Expert Systems 22, 2 (2005) 78–85. [CrossRef]
- O. Moursli, Y. Pochet, A branch-and-bound algorithm for the hybrid flowshop, International Journal of Production Economics 64, 1–3 (2000) 113–125. [CrossRef]
- C. Sriskandarajah, S.P. Sethi, Scheduling algorithms for flexible flowshops: worst and average case performance, European Journal of Operational Research 43, 2 (1989) 143–160. [CrossRef] [MathSciNet]
- A. Guinet, M.M. Solomon, P.K. Kedia, A. Dussauchoy, A computational study of heuristics for two-stage flexible flowshops, International Journal of Production Research 34, 5 (1996) 1399–1415. [CrossRef]
- E. Nowicki, C. Smutnicki, The flow shop with parallel machines: a tabu search approach, European Journal of Operational Research 106, 2–3 (1998) 226–253. [CrossRef]
- M. Gourgand, N. Grangeon, S. Norre, Metaheuristics for the deterministic hybrid flow shop problem, Proceeding of the International Conference on Industrial Engineering and Production Management (IEPM’99), Glasgow, United Kingdom, July 12–15 (1999), pp. 136–145.
- R. Zhang, C. Wu, A simulated annealing algorithm based on block properties for the job shop scheduling problem with total weighted tardiness objective, Computers & Operations Research 38 5 (2011) 854–867. [CrossRef] [MathSciNet]
- C.R. Reeves, A genetic algorithm for flowshop sequencing, Computers & Operations Research 22, 1 (1995) 5–13. [CrossRef]
- R. Tavakkoli-Moghaddam, N. Safaei, F. Sassani, A memetic algorithm for the flexible flow line scheduling problem with processor blocking, Computers & Operations Research 36 (2009) 402–414. [CrossRef] [MathSciNet]
- R. Cheng, M. Gen, M. Tozawa, Minmax earliness/tardiness scheduling in identical parallel machine system using genetic algorithms, Computers & Industrial Engineering 29, 1–4 (1995) 513–517. [CrossRef]
- J. Yang, Minimizing total completion time in two-stage hybrid flow shop with dedicated machines, Computers & Operations Research 38, 7 (2011) 1045–1053. [CrossRef] [MathSciNet]
- M. Khalili, R. Tavakoli-Moghadam, A multi-objective electromagnetism algorithm for a bi-objective flowshop scheduling problem, Journal of Manufacturing Systems 31 (2012) 232–239. [CrossRef]
- C. Chen, R. Neppalli, Genetic algorithms applied to the continuous flow shop problem, Computers & Industrial Engineering 30, 4 (1996) 919–929. [CrossRef]
- T. Aldowaisan, A. Allahverdi, New heuristics for m-machine no-wait flowshop to minimize total completion time, Omega 32, 5 (2004) 345–352. [CrossRef]
- Q.K. Pan, M.F. Tasgetiren, Y.C. Liang, A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem, Computers & Operations Research 35 (2008) 2807–2839. [CrossRef] [MathSciNet]
- A. Fink, S. Voß, Solving the continuous flow-shop scheduling problem by metaheuristics, European Journal of Operational Research 151 (2003) 400–414. [CrossRef] [MathSciNet]
- S.J. Shyu, B.M.T. Lin, P.Y. Yin, Application of ant colony optimization for no-wait flowshop scheduling problem to minimize the total completion time, Computers & Industrial Engineering 47 (2004) 181–193. [CrossRef]
- J. Grabowski, J. Pempera, Some local search algorithms for no-wait flow-shop problem with makespan criterion, Computers & Operations Research 32 (2005) 2197–2212. [CrossRef] [MathSciNet]
- M. Khalili, An iterated local search algorithm for flexible flow lines with sequence dependent setup times to minimize total weighted completion, International Journal of Management Science and Engineering Management 7, 1 (2012) 63–66.
- M. Khalili, Multi-objective no-wait hybrid flowshop scheduling problem with transportation times, Journal International Journal of Computational Science and Engineering 7, 2 (2012) 147–153.
- J. Hurink, S. Knust, Makespan minimization for flow-shop problems with transportation times and a single robot, Discrete Applied Mathematics 112 (2001) 199–216. [CrossRef] [MathSciNet]
- A. Soukhal, A. Oulamara, P. Martineau, Complexity of flow shop scheduling problems with transportation constraints, European Journal of Operational Research 161 (2005) 32–41. [CrossRef] [MathSciNet]
- R. Ruiz, C.J. Garica-Diaz, C. Maroto, Considering scheduling and preventive maintenance in the flowshop sequencing problem, Computers & Operations Research 34 (2007) 3314–3330. [CrossRef]
- G. Schmidt, Scheduling with limited machine availability, European Journal of Operational Research 121 (2000) 1–15. [CrossRef] [MathSciNet]
- C.Y. Lee, Minimizing the makespan in the two-machine flowshop scheduling problem with an availability constraint, Operations Research Letters 20 (1997) 129–139. [CrossRef] [MathSciNet]
- J. Blazewicz, J. Breit, P. Formanowicz, W. Kubiak, G. Schmidt, Heuristic algorithms for the two-machine flowshop problem with limited machine availability, Omega 29 (2001) 599–608. [CrossRef]
- J. Breit, A polynomial-time approximation scheme for the two-machine flow shop scheduling problem with an availability constraint, Computers & Operations Research 33 (2006) 2143–2153. [CrossRef] [MathSciNet]
- H. Allaoui, A. Artiba, Integrating simulation and optimization to scheduling a hybrid flow shop with maintenance constraints, Computers & Industrial Engineering 47 (2004) 431–450. [CrossRef]
- V. Cerny, Thermodynamical approach to the travelling salesman problem: an efficient simulation algorithm, JOTA 45 (1985) 41–51. [CrossRef] [MathSciNet]
- I. Birbil, S.C. Fang, An electromagnetism-like mechanism for global optimization, Journal of Global Optimization 25 (2003) 263–282. [CrossRef] [MathSciNet]
- D. Debels, B.D. Reyck, R. Leus, M. Vanhoucke, A hybrid scatter search/electromagnetism meta-heuristic for project scheduling, European Journal of Operational Research 169 (2006) 638–653. [CrossRef] [MathSciNet]
- E. Taillard, Benchmarks for basic scheduling problems, European Journal of Operational Research 64, 2 (1993) 278–285. [CrossRef]
- R. Ruiz, A. Allahverdi, Some effective heuristics for no-wait flowshops with setup times to minimize total completion time, Annals of Operation Research 156 (2007) 143–171. [CrossRef]
- B. Naderi, M. Mousakhani, M. Khalili, Scheduling multi-objective open shop scheduling using a hybrid immune algorithm, The International Journal of Advanced Manufacturing Technology 66, 5–8 (2013) 895–905. [CrossRef]
- D.C. Montgomery, Design and Analysis of Experiments, Fifth edition, John Wiley & Sons, 2000.
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