Seeking Simulation-based Optimization of Job shop Scheduling in Small and Medium Enterprises to Minimize the Cost of Tardiness and Earliness of Activities

Document Type : Research Paper

Authors

1 Assistant Prof. in New Business Group, Faculty of Entrepreneurship, Tehran University, Tehran, Iran

2 Ph.D Candidate of Industrial Engineering, Faculty of Industrial Engineering, Tehran University, Tehran, Iran

Abstract

Determining the optimal sequence of jobs in job shop scheduling for small and medium enterprises, affect the machine productivity, earliness and tardiness costs of delivery. The deterministic variant of the problem is well-known to be NP-Hard. If random elements are introduced into the problem, the level of complexity goes higher. Hence, many priority rules have been developed to tackle stochastic job shop scheduling problem. However, to devise a better solution approach, simulation-optimization approach might be used. In this study, a mathematical model was developed for job shop scheduling with random process times and possible machine breakdowns. Then, a simulation-optimization model was applied to choose among a list of priority rules using Rockwell Arena 14. Finally, a numerical example was used to evaluate the quality of the model. Results showed that the rule Longest Processing Time (LPT) yields the lowest total earliness and tardiness costs. However, the total costs of the following rules are also acceptable: First in First out (FIFO), Last in First out (LIFO), Earliest Due Date (EDD) and Slack time (Slack).

Keywords

References

Alizadeh, L., Noorossana, R. & Raissi, S. (2015). Multi-objective optimization of criminal trial process using descrete event computer simulation and design of experiment. Industrial Management, 7(1), 65-82. (in Persian)
Azadivar, F. (1999). Simulation optimization methodologies, Presented at the Proceedings of the 31st conference on winter simulation: Simulation a bridge to the future - Volume 1, Phoenix, Arizona, United States.
Banks, J. & Nelson, B. L. (2010). Discrete-Event System Simulation: Prentice Hall.
Blackstone, J. H., Phillips, D. T. & Hogg, G. L. (1982). A state-of-the-art survey of dispatching rules for manufacturing job shop operations. International Journal of Production Research, 20(1), 27-45.
Chan, F. T. S., Chan, H. K., Lau, H. C. W. & Ip, R. W. L. (2003). Analysis of dynamic dispatching rules for a flexible manufacturing system. Journal of Materials Processing Technology, 138(1-3), 325-331.
Hashim, S. A. M. (2017). Simulation for reducing energy consumption of multi core low voltage power cable manufacturing system. Journal on Technical and Vocational Education, 1(2), 1-10.‏
Jain, A. & Meeran, S. (1999). A State-Of-The-Art­­­­­­ Review of Job-Shop Scheduling Techniques (113ed.) Available: http://citeseerx.ist.psu.edu/viewdoc/ summary?doi= 10.1.1.54.8522.
Klemmt, A., Horn, S., Beier, E. & Weigert, G. (2007). Investigation of modified heuristic algorithms for simulation-based ptimization. In Electronics Technology, 30th International Spring Seminar on, pp. 24-29.
Klemmt, A., Horn, Weigert, S. G. & Wolter, K.J. (2009). Simulation-based optimization vs.mathematical programming: A hybrid approach for optimizing scheduling problems. Robotics and Computer-Integrated Manufacturing, 25(6), 917-925.
Pinedo, M. L. (2012). Scheduling: Theory, Algorithms, and Systems: Springer.
Rane, A. B., Sunnapwar, V. K., Chari, N. R., Sharma, M. R. & Jorapur, V. S. (2017). Improving performance of lock assembly line using lean and simulation approach. International Journal of Business Performance Management, 18(1), 101-124.
Salimifard, K. & Ansari, M. (2016). Modeling and Simulation of Urban Traffic Network Using Colored Petri Nets. Industrial Management, 8(3), 381-404.  (in Persian)
Sharma, S. K., Suraj, B. V. & Routroy, S. (2017). Positioning of Inventory in Supply Chain Using Simulation Modeling.‏ The IUP Journal of Supply Chain Management, 13(2), 20-32.
Sajadi, S. M., Esfahani, M. M. S. & Sörensen, K. (2011). Production control in a failure-prone manufacturing network using discrete event simulation and automated response surface methodology. The International Journal of Advanced Manufacturing Technology, 53(1-4), 35-46.‏
Teles, J., Lopes, R. B. & Ramos, A. L. (2017). A Simulation-Based Analysis of a Cork Transformation System. In Engineering Systems and Networks (pp. 3-11). Springer, Cham Pinedo, M. L. (2012). Scheduling: Theory, Algorithms, and Systems, Springer.
Vieira,G. E., Herrmann, J. W. & Lin, E. (2000). Predicting the performance of escheduling strategies for parallel machine systems. Journal of Manufacturing Systems, 19(4), 256-266.
Weng, M. X. & Ren, H. (2006). An efficient priority rule for scheduling job shops to minimize mean tardiness. IIE Transactions, 38(9), 789-795.
Yan, Y. & Guoxin, W. (2007). A job shop scheduling approach based on simulation optimization, in Industrial Engineering and Engineering Management, IEEE International Conference on, pp. 1816-1822.
Zandieh, M. & Ahmadi, E. (2015). Robust and stable scheduling for FJSP under random machine breakdown by use of genetic algorithm and simulation. Industrial Management, 6(3), 511-534. (in Persian)
 
 
Industrial Management Journal
Volume 9, Issue 1
2017
Pages 129-146

Files

Share

How to cite

Statistics