Abstract
Flow-shop scheduling is one of the major problems in many manufacturing systems. Canned fruit is one of the industries in which the flow-shop scheduling has already been used. In this paper, an aggregated artificial neural network and simulation modeling approach are proposed to find optimal solution for such cases. Therefore, artificial neural network and simulation (ANNS) approach is introduced and used as a new approach to solve a certain flow-shop scheduling problem with the objective of minimizing total cost. In this research, flow-shop scheduling problem with parallel identical machines is investigated. The proposed approach is compared with the previous works, and the performance of the proposed approached is studied on a test problem. Experimental results show the superiority of the presented approach over conventional simulation approaches.
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References
Al-Turki U, Andijani A, Arifulsalam S (2004) A new dispatching rule for the stochastic single-machine scheduling problem. Simulation 80(3):165–170
Azadeh A, Maleki-Shoja B, Moghaddam M, Akbari A, Asadzadeh SM (2012) A hybrid artificial neural network-computer simulation algorithm for optimization of job shop scheduling problems to minimize makespan. Int J Adv Manuf Technol 50(2):551–566
Azadeh A, Sheikhalishahi M, Firoozi M, Khalili SM (2013) An integrated multi-criteria Taguchi computer simulation-DEA approach for optimum maintenance policy and planning by incorporating learning effects. Int J Prod Res 51(18):5374–5385
Azadeh A, Sheikhalishahi M, Khalili SM, Firoozi M (2014) An integrated fuzzy simulation–fuzzy data envelopment analysis approach for optimum maintenance planning. Int J Comput Integr Manuf 27(2):181–199
Azadeh A, Ghaderi SF, Sheikhalishahi M, Nokhandan BP (2014) Optimization of short load forecasting in electricity market of Iran using artificial neural networks. Optim Eng 15(2):485–508
Azadeh A, Sheikhalishahi M, Boostani A (2014) A flexible neuro-fuzzy approach for improvement of seasonal housing price estimation in uncertain and non-linear environments. S Afr J Econ. doi:10.1111/saje.12047
Biskup D, Feldmann M (2001) Benchmarks for scheduling on a single-machine against restrictive and unrestrictive common due dates. Comput Oper Res 28(8):787–801
Chen WY, Sheen GJ (2007) Single-machine scheduling with multiple performance measures: minimizing job-dependent earliness and tardiness subject to the number of tardy jobs. Int J Prod Econ 109(1–2):214–229
Gupta AK, Sivakumar AI (2005) Multi-objective scheduling of two-job families on a single machine. Omega 33(5):399–405
Heragu SS (1997) Facilities design. PWS Publishing Company, Boston
Hornik K, Stinchcombe M, White H (1989) Multilayer feed-forward networks are universal approximators. Neural Netw 2(5):359–366
Lian Z, Jiao B, Gu X (2006) A similar particle swarm optimization algorithm for job-shop scheduling to minimize makespan. Appl Math Comput 183:1008–1017
Naidu JT (2003) A note on a well-known dispatching rule to minimize total tardiness. Omega 31(2):137–140
Osman IH, Potts CN (1989) Simulated annealing for permutation flow-shop scheduling. Omega 17(6):551–557
Parthanadeea P, Buddhakulsomsirib J (2010) Simulation modeling and analysis for production scheduling using real-time dispatching rules: a case study in canned fruit industry. Comput Electron Agric 70:245–255
Pritsker AAB, O’Reilly JJ (1999) Simulation with visual SLAM and AweSim. John Wiley and Sons, Inc, New York
Rajendran C, Holthaus O (1999) A comparative study of dispatching rules in dynamic flow-shops and job-shops. Eur J Oper Res 116(1):156–170
Sabuncuoglu I, Gurgun B (1996) A neural network model for scheduling problems. Eur J Oper Res 93:288–299
Svozil D, Kvasnicka V, Pospichal J (1997) Introduction to multi-layer feed-forward neural networks. Chemom Intell Lab Syst 39(1):43–62
Tavakkoli-Moghaddam R, Daneshmand-Mehr M (2005) A computer simulation model for job shop scheduling problems minimizing makespan. Comput Ind Eng 48:811–823
Vinod V, Sridharan R (2008) Dynamic job-shop scheduling with sequence-dependent setup times: simulation modeling and analysis. Int J Adv Manuf Technol 36:355–372
Yang S, Wang D (2001) A new adaptive neural network and heuristics hybrid approach for job shop scheduling. Comput Oper Res 28:955–971
Yu H, Liang W (2001) Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling. Comput Ind Eng 39:337–356
Zobel CW, Keeling KB (2008) Neural network-based simulation metamodels for predicting probability distributions. Comput Ind Eng 54(4):879–888
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Azadeh, A., Maleki-Shoja, B., Sheikhalishahi, M. et al. A simulation optimization approach for flow-shop scheduling problem: a canned fruit industry. Int J Adv Manuf Technol 77, 751–761 (2015). https://doi.org/10.1007/s00170-014-6488-x
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DOI: https://doi.org/10.1007/s00170-014-6488-x