Halil Ibrahim Koruca, Erdal Aydemir
3.151 861


This paper presents the development of a priority, rule-based, a production scheduling module for the Faborg-Sim simulation tool with ten priority rules. Faborg-Sim consists of three modules, i.e., modelling, simulation, and performance evaluation. In this study, a detailed conceptual framework was defined and a case study was modelled and evaluated for a machine parts manufacturing system by using Faborg-Sim. The simulations were run using only six selected priority rules for the information on customers’ orders in order to integrate the scheduling module in Faborg-Sim. Simulation models were run separately for each priority rule of scheduling to obtain the best performance of the production schedule. After repeating the simulations, performance measurement parameters were obtained and evaluated on a relative basis.   


Production scheduling, Priority rules, Dispatching rules, Simulation, Faborg-sim

Full Text:



Baker K. R., “Introduction to Sequencing &

Scheduling”. New York: John Wiley (1974) 6.

Al-Turki. U., Andijani A. and Arifulsalam S.,

“A new dispatching rule for the stochastic single-machine

Simulation, 80-3:165-170 (2004). problem” 7.

Baker K. R. and Trietsch D., “Principles of

sequencing & scheduling” New York: John Wiley (2009). 8.

Olafsson S. and Li X., “Learning effective new

single machine dispatching rules from optimal

scheduling data” International Journal of

Production Economics 128:118–126 (2010). 9.

Yang S., Wang D., Chai T. and Kendall G.,

“An improved constraint satisfaction adaptive

neural network for job-shop scheduling”

Journal of Scheduling 13:17–38 (2010).

Vinod V. and Sridharan R., “Simulation Modeling and Assignment Methods and Scheduling Decision Rules in a Dynamic Job Shop Production System” International Journal of Production Economics 129:127–146 (2011). Due-Date

Akkaya G. and Gokcen T., “Job shop scheduling design with artificial neural networks” Sigma Engineering and Natural Sciences 4:121-130 (2006).

Koruca H. I., Ozdemir G., Aydemir E. and “The Performance Measurement in an Evaluation Module for Faborg-Sim Simulation Software” Expert 12:8211-8220 (2010).

Simulation-Based System Applications 37

Li H., Li Z., Li L. X. and Hu B., “A production rescheduling European Journal of Operational Research 124:283-293 (2000). systems”

Allaoui H. and Artiba A., “Integrating simulation and optimization to schedule a hybrid flow shop with maintenance constraints” Computers and Industrial Engineering 47:431–450 (2004).

Gharbi A. and Kenne J. P., “Maintenance scheduling and production control of multiple machine manufacturing systems” Computers and (2005). 48:693-707

Yildirim M. B., Cakar T., Doguc U. and Meza J. C., “Machine number, priority rule, and due date determination in flexible manufacturing systems using artificial neural networks” Computers and Industrial Engineering 50:185–194 (2006).

Geiger C. D., Uzsoy R. and Aytug H., “Rapid Modelling Dispatching Rules: An Autonomous Learning Approach” Journal of Scheduling 9:7–34 (2006) of Priority

Holthaus O. and Ziegler H., “Improving job shop performance by coordinating dispatching rules” International Journal of Production Research 35-2:539-549 (1997).

Holthaus O. and Rajendran C., “New dispatching rules for scheduling in a job shop- an experimental study” International Journal of Advanced Manufacturing Technology 13:148-153 (1997).

Ozturkoglu, Y., “A Bi-Criteria Single Machine Scheduling Gazi University Journal of Science, 26(1), 97-106 (2013).

Turker, K. and Sel,Ç., “Scheduling Two Parallel Machines with Sequence Dependent Setups and A Single Server” Gazi University Journal of Science, 24(1),113-123 (2011).

Moghaddam R. T. and Mehr M. D., “A Computer Simulation Model for Job Shop Scheduling Problems Minimizing Makespan” Computers and Industrial Engineering 48:811–823 (2005).

Xing L. N., Chen Y. W. and Yang K. W., “Multi-objective flexible job shop schedule: Design modelling” Applied Soft Computing 9-1:362- 376 (2009). by simulation

Weng M. X. and Ren H., “An efficient priority rule for scheduling job shops to minimize mean tardiness” IIE Transactions 38-9:789-795 (2006).

Chen (Gary) S. J. and Lin L., “Reducing total tardiness cost in manufacturing cell scheduling by a multi-factor priority rule” International Journal of Production Research 37-13:2939- 2956 (1999).

Penn M. and Raviv T., “An Algorithm for The Maximum European Journal of Operational Research 193-2:437-450 (2009). Problem”

Thiagarajan S. and Rajendran C., “Scheduling in dynamic assembly job-shops to minimize the sum of weighted earliness, weighted tardiness and weighted flowtime of jobs” Computers and Industrial Engineering 49:463–503 (2005).

Natarajan K., Mohanasundaram K. M., Babu B. S., Suresh S., Raj K. A. A. D., Rajendran C., “Performance evaluation of priority dispatching rules in multi-level assembly job shops with jobs having weights for flowtime and tardiness” International Journal of Advanced Manufacturing (2007). 31:751-761

Weigert G. and Henlich T., “Simulation-based scheduling International Integrated (2009). operations” Computer 22-4:325-333 Manufacturing,

Reeja M. K. and Rajendran C., “Dispatching rules for scheduling in assembly jobshops - Part 1” International Journal of Production Research 38-9:2051-2066 (2000).

Dominic P. D. D., Kaliyamoorthy S. and Kumar M. S., “Efficient dispatching rules for dynamic job shop scheduling” International Journal Technology 24:70-75 (2004). Manufacturing

Yang W. H. and Liao C. J., “Survey of scheduling research involving setup times” International Journal of Systems Science 30- 2:143-155 (1999).

Vinod V. and Sridharan R., “Simulation-based meta models for scheduling a dynamic job shop with International Research 47-6:1425-1447 (2009). times” Journal of Production

Balas E., Simonetti N. and Vazacopoulos A., “Job Shop Scheduling with Setup Times, Deadlines Journal of Scheduling, 11:253–262 (2008). Constraints”

Cakar T., Yıldırım M. B. and Barut M., “A Neuro-Genetic Approach to Design and Planning of a Manufacturing Cell” Journal of Intelligent (2005). 16:453–462

Sabuncuoglu I., “A study of scheduling rules of flexible manufacturing systems: A simulation approach” Production Research, 36-2:527-546 (1998). Journal of

VDI, “Richtlinie -3633” Düsseldorf, VDI Verlag (1983).

Witte T., “Lexikon der Wirtschaftsinformatik” Hrsg. Mertens P., Berlin, Springer Verlag, 2.Auflage (1990).Law A. M. and Kelton W. D., “Simulation Modeling and Analysis” McGraw- Hill, 2nd Edition, New York (1991).

Schmittbetz M., “Simulation wird zum Triebwerk für Innovation” VDI-Nachrichten, 52:18-24 (1998).

Zülch G., Bogus T. and Fischer J., “Integrated Simulation and Workforce Assignment for the Evaluation of Flexible Working Time Models” in: Chen, Z. et al., System Simulation and Scientific Academic Publishers, Beijing, 1:353-357 (2002). International 41. Schuh G.,

“Produktionsmanagements I”

WZL/FIR, Acchen Universitat (2008).

Frantzen M., Ng A. H. C. and Moore P., “A simulation-based Scheduling System for Real- time Optimization and Decision Making Support” Robotics and Computer-Integrated Manufacturing 27-4:696-705 (2011).

Kapanoglu M. and Alikalfa M., “Learning IF– THEN Priority Rules for Dynamic Job Shops Using Genetic Algorithms” Robotics and Computer-Integrated Manufacturing 27:47– 55 (2011).

Aydemir E., “Optimization of Job Shop Scheduling Problems With Priority Rule Based Genetic Algorithms By Simulation Method” MSc. Dissertation in Industrial Engineering at Suleyman Demirel University, Isparta, Turkey (2009).

Panwalkar S. S. and Iskander W., “A survey of scheduling rules” Operations Research 25- 1:45-61 (1977).

Brinkmeier B., “Prozeßorientieries Prototyping von Produktionsbereich” PhD. Dissertation der Ingenieurwissenschaftes Universitat, Karlsruhe, Germany (1998). im in Karlsruhe