Abstract
Unstable environment of industrial systems is a source of various uncertainties in production features such as processing times. Moreover, selecting appropriate dispatching rules is a complex and significant issue in practical problems under uncertainty. Most previous studies have pointed out that using a single dispatching rule does not necessarily result in an optimal schedule. This study proposes a novel hybrid algorithm based on computer simulation and adaptive neuro-fuzzy inference system (ANFIS) to select optimal dispatching rule for each machine in job shop scheduling problems (JSSPs) under uncertain conditions so that makespan is minimized. It captures uncertainty using fuzzy set theory and assumes that processing times are in the form of fuzzy numbers. This algorithm contributes to the previous works in two important ways. First, the inherent uncertainty of JSSPs is reflected in fuzzy processing times. Second, this is the first study that develops an approach based on computer simulation and ANFIS for selecting the optimal dispatching rules and minimizing the makespan in JSSPs under uncertainty. The computational results demonstrate the superiority of this algorithm over the previous studies in the literature.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Alvarez-Valdes R, Fuertes A, Tamarit JM, Giménez G, Ramos R (2005) A heuristic to schedule flexible job-shop in glass factory. Eur J Oper Res 165:525–534
Azadeh A, Moghaddam M, Geranmayeh P, Naghavi A (2010) A flexible artificial neural network–fuzzy simulation algorithm for scheduling a flow shop with multiple processors. Int J Adv Manuf Technol 50:699–715
Azadeh A, Negahban A, Moghaddam M (2012) A hybrid computer simulation-artificial neural network algorithm for optimisation of dispatching rule selection in stochastic job shop scheduling problems. Int J Prod Res 50(2):551–566
Bagheri A, Zandieh M, Mahdavi I, Yazdani M (2010) An artificial immune algorithm for the flexible job-shop scheduling problem. Futur Gener Comput Syst 26(4):533–541
Cakmakci M (2007) Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge:Bioprocess and. Biosyst Eng 30(5):349–357
Chang FJ, Chang YT (2006) Adaptive neuro fuzzy inference system for prediction of water level in reservoir. Adv Water Resour 29(1):1–10
Chau KW, Wu CL, Li YS (2005) Comparison of several flood forecasting models in Yangtze River. J Hydrol Eng 10(6):485–491
Chen T (2012) A fuzzy fluctuation smoothing rule for job dispatching in a wafer fabrication factory: a simulation study. Int J Fuzzy Syst Appl (IJFSA) 2(4):47–63
Dong M, Liu M (2011) An ANFIS based dispatching rule for complex fuzzy job shop scheduling problem: Proceedings of the IEEE International Conference on Information Science and Technology (ICIST) 263–266
Dong M, Liu M, Wu C (2005) An ANFIS based adaptive algorithm for job shop scheduling problem with parallel machines: control engineering of China
Garey M, Johnson D, Sethi R (1976) The complexity of flow shop and job shop scheduling. Math Oper Res 1(2):117–129
Hasan SK, Sarker R, Essam D, Cornforth D (2009) Memetic algorithms for solving job-shop scheduling problems. Memet Comput 1(1):69–83
Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system:Systems, Man and Cybernetics. IEEE Trans 23(3):665–685
Jurisch B (1992) Scheduling jobs in shops with multi-purpose machines, PhD thesis. University of Osnabrük, Germany
Kadipasaoglu SN, Xiang W, Khumawala BM (1997) A comparison of sequencing rules in static and dynamic hybrid flow systems. Int J Prod Res 35(5):1359–1384
Lei D (2010) Fuzzy job shop scheduling problem with availability constraints. Comput Ind Eng 58(4):610–617
Lei D (2010) A genetic algorithm for flexible job shop scheduling with fuzzy processing time. Int J Prod Res 48(10):2995–3013
Lejmi T, Sabuncuoglu I (2002) Effect of load, processing time and due date variation on the effectiveness of scheduling rules. Int J Prod Res 40(4):945–974
Lin JT, Wang FK, Yen PY (2001) Simulation analysis of dispatching rules for an automated interbay material handling system in wafer fab. Int J Prod Res 39(6):1221–1238
Muhuri PK, Shukla KK (2009) Real-time scheduling of periodic tasks with processing times and deadlines as parametric fuzzy numbers. Appl Soft Comput 9:936–946
Orides M, Castro PAD, Kato ER, Camargo HA (2006) A genetic fuzzy system for defining a reactive dispatching rule for AGVs: In Systems, Man and Cybernetics, 2006. SMC'06. IEEE Int Conf 1:56–61
Pan JC-H, Huang H-C (2009) A hybrid genetic algorithm for no-wait job shop scheduling problems. Expert Syst Appl 36(3):5800–5806
Pritsker AAB, O’Reilly JJ (1999) Simulation with Visual SLAM® and AweSim®. John Wiley and Sons, New York
Rego C, Duarte R (2009) A filter-and-fan approach to the job shop scheduling problem. Eur J Oper Res 194(3):650–662
Sha D, Lin H-H (2010) A multi-objective PSO for job-shop scheduling problems. Expert Syst Appl 37(2):1065–1070
Shafaei R, Rabiee M, Mirzaeyan M (2011) An adaptive neuro fuzzy inference system for makespan estimation in multiprocessor no-wait two stage flow shop. Int J Comput Integr Manuf 24(10):888–899
Tavakkoli-Moghaddam R, Daneshmand-Mehr M (2005) A computer simulation model for job shop scheduling problems minimizing makespan. Comput Ind Eng 48(4):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(3–4):355–372
Vinod V, Sridharan R (2009) Simulation-based meta-models for scheduling a dynamic job shop with sequence-dependent setup times. Int J Prod Res 47(6):1425–1447
Wang S, Yu J (2010) An effective heuristic for flexible job-shop scheduling problem with maintenance activities. Comput Ind Eng 59(3):436–447
Wang L, Zhou G, Xu Y, Wang S, Liu M (2012) An effective artificial bee colony algorithm for the flexible job-shop scheduling problem. Int J Adv Manuf Technol 60(1–4):303–315
Xing L-N, Chen Y-W, Wang P, Zhao Q-S, Xiong J (2010) A knowledge-based ant colony optimization for flexible job shop scheduling problems. Appl Soft Comput 10(3):888–896
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353
Zhang H, Jiang Z, Guo C (2009) Simulation-based optimization of dispatching rules for semiconductor wafer fabrication system scheduling by the response surface methodology. Int J Adv Manuf Technol 41(1–2):110–121
Zhang G, Shao X, Li P, Gao L (2009) An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Comput Ind Eng 56(4):1309–1318
Zhou H, Cheung W, Leung LC (2009) Minimizing weighted tardiness of job-shop scheduling using a hybrid genetic algorithm. Eur J Oper Res 194(3):637–649
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Azadeh, A., Hosseini, N., Abdolhossein Zadeh, S. et al. A hybrid computer simulation-adaptive neuro-fuzzy inference system algorithm for optimization of dispatching rule selection in job shop scheduling problems under uncertainty. Int J Adv Manuf Technol 79, 135–145 (2015). https://doi.org/10.1007/s00170-015-6795-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-015-6795-x