An efficient priority rule for flexible job shop scheduling problem
Keywords:
Flexible job shop scheduling problem, priority rules, simulation experiments
Abstract
In the classical planning approach, the production plan is made by a central planning unit and the production is expected to be made in accordance with the plan. But in real life; the existence of many dynamic factors such as machine failures, new order arrivals, order cancellations or changes cause the plans to be partially or completely incomplete. This reduces the confidence of the enterprises in the production planning function and even causes it to be perceived as an unnecessary activity. The increase in internet speed and developments in technologies for gathering information from the shop floor has provided the opportunity to closely follow the instant changes and to give the reaction in the most accurate way. These advances have led researchers studying in the field of operational research to agent-based approaches or dynamic sequencing rules. In this study, an effective composite priority rule has been developed for Cmax minimization of flexible job shop scheduling problem (FJSP). The composite rule, which is called the relativity rule, is compared with various combinations of priority rules, which are well known in the literature. The results show that the developed composite rule provides a clear dominance over other priority rules.
References
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[45] Karimi, H., Rahmati, S.H.A., Zandieh, M., 2012. An efficient knowledge-based algorithm for the flexible job shop scheduling problem. Knowledge-Based Systems, 36, 236-244.
[46] Koruca, H.İ., Aydemir, E., 2014. A Priority Rule Based Production Scheduling Module on Faborg-Sim Simulation Tool. Gazi University Journal of Science, 27(4), 1143-1155.
[47] Li, J-Q., Pan, Q-K., Suganthan, P.H., Chua, T.,J., 2011. A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem. The International Journal of Advanced Manufacturing Technology 52, 5–8, 683–697.
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[52] Ramasesh, R., 1990. Dynamic Job Shop Scheduling: A Survey of Simulation Research. OMEGA, 18, 43-57
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[55] Teekeng, W., Thammano, A., 2011. A Combination of Shuffled Frog Leaping and Fuzzy Logic for Flexible Job-Shop Scheduling Problems., Procedia Computer Science, 6, 69-75.
[56] Teekeng, W., Thammano, A. 2012. Modified Genetic Algorithm for Flexible Job-Shop Scheduling Problems. Procedia Computer Science, 12, 122-128.
[57] Teymourifar, A., Bahadir, O., Ozturk, G., 2018. Dynamic Priority Rule Selection for Solving Multi-objective Job Shop Scheduling Problems. Universal Journal of Industrial and Business Management,6 (1), 11-22.
[58] Xing, L-N., Chen, Y-W. Wang, P., Zhao, O-S., Xiong, J., 2010. A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems. Applied Soft Computing, 10, (3), 888-896.
[59] Wang (a), L., Zhou, G., Xu, Y., Wang, S., Liu, M., 2012. An effective artificial bee colony algorithm for the flexible job-shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 60, (1–4), 303–315.
[60] Wang (b), L., Wang, S., Xu, Y., Zhou, G., Liu, M., 2012. A bi-population based estimation of distribution algorithm for the flexible job-shop scheduling problem. Computers & Industrial Engineering, 62, (4), 917-926.
[61] Yazdani, M., Amiri, M., Zandieh, M., 2010. Flexible job-shop scheduling with parallel variable neighborhood search algorithm. Expert Systems with Applications, 37, (1), 678-687.
[62] Yuan, Y., Xu, H., 2012. HHS/LNS: An integrated search method for flexible job shop scheduling," IEEE Congress on Evolutionary Computation, Brisbane, QLD, 1-8.
[63] Yuan, Y., Xu, H., 2013. Flexible job shop scheduling using hybrid differential evolution algorithms., Computers & Industrial Engineering, 65, (2), 246-260.
[64] Zandieh, M., Mahdavi, I., Bagheri, A. 2008. Solving the Flexible Job-Shop Scheduling Problem by a Genetic Algorithm. Journal of Applied Sciences, 8 (24), 4650-4655.
[65] Zhang, G., Gao, L., Shi, Y., 2011. An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Systems with Applications, 38, 4, 3563-3573.
[2] Zhou, Y., Yang, J-J, Zheng, L-Y., 2018. Hyper-Heuristic Coevolution of Machine Assignment and Job Sequencing Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling, IEEE Access, vol. 7, pp. 68-88, 2019. doi: 10.1109/ACCESS.2018.2883802
[3] Haupt, R., 1989, “A Survey of Priority Rule-Based Scheduling,” OR Spektrum, 11, 3-16.
[4] Branke J., Hildebrandt, T., Scholz-Reiter, B. Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations. Evolutionary Computation, 2015; 23(2), 249-277.
[5] Xiong, H., Fan, H., Jiang G., Li, G. A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints. European Journal of Operational Research. 2017; 257: 13-24.
[6] Sabuncuoglu, I. A study of scheduling rules of flexible manufacturing systems: A simulation approach. Int. Journal of Production Research 1998; 36(2): 527–546.
[7] Kaweegitbundit, P., Eguchi, T. Job Shop Scheduling with Alternative Machines Using a Genetic Algorithm Incorporating Heuristic Rules-Effectiveness of Due-date Related Information. IFIP Advances in Information and Communication Technology, AICT-459 (Part I), 2016; 439-446.
[8] Dooley KJ., Mahmoodi, F. Identification of Robust Scheduling Heuristics: Application of Taguchi Methods in Simulation Studuies, 1992.
[9] Na, H., Park, J. Multi-level job scheduling in a flexible job shop environment. Int. Journal of Production Research 2014; 52(13): 3877-3887.
[10] Mei, Y., Zhang, M., Nyugen, S. Feature Selection in Evolving Job Shop Dispatching Rules with Genetic Programming. GECCO’16, July 20-24, Denver, Colorado, USA, 2016.
[11] Conway, RW., Maxwell, WL., Miller, LW. Theory of scheduling. Addison-Wesley Publishing Company, Reading, Massachusetts, 1967.
[12] Kaban, AK., Othman, Z., Rohmah, DS. Comparison of dispatching rules in job-shop scheduling problem using simulation: a case study. Int. Journal of Simulation Modeling 2012; 11(3): 129-140.
[13] Rajendran, C., Holthaus, O. A comparative study of dispatching rules in dynamic flow shops and job shops, European Journal of Operational Research, 1999; 116: 156-170.
[14] Jaya, NB. Evaluation of different dispatching rules in computer integrated manufacturing using design of experiment techniques. Ms.Thesis, Faculty of Mechanical and Manufacturing Engineering, University Tun Hussein Onn, Malaysia, 2015.
[15] Mouelhi-Chibani, W., Pierreval, H. Training a neural network to select dispatching rules in real time. Computers & Industrial Engineering, 2010; 58(2): 249–256.
[16] Vepsalainen, A.P.J., 1984. State Dependent Priority Rules for Scheduling. Graduate School of Industrial Administration. The Robotics Institute Carnegie-Mellon University Pittsburgh, Pennsylvania 15213.
[17] Land, MJ. Workload control in job shops, grasping the tap. s.n, 2004.
[18] Baker, K.R. Introduction to sequencing and scheduling. Wiley, New York, 1974.
[19] Trietsch, D., Baker, KR. Principles of Sequencing and Scheduling. Wiley & Sons, Hoboken, New Jersey, 2009.
[20] Alharkan I.M. Algorithms for sequencing and scheduling, Industrial Engineering Department, College of Engineering, King Saud University., Riyadh, Saudi Arabia, 2005.
[21] Panwalkar, SS., Iskander, W. A Survey of Scheduling Rules. Operation Research 1977; 25(1): 45-61.
[22] Blackstone, JH., Phillips, DT., Hogg, GL. A state-of-the-art survey of dispatching rules for manufacturing job shop operations. Int. J. of Production Research, 1982; 20(1): 27-45.
[23] Horng, HC. Comparing Steady-state Performance of Dispatching Rule-pairs in Open Shops. Int. Journal of Applied Science and Engineering 2006; 4(3): 259-273.
[24] Brandimarte, P. Routing and scheduling in a flexible job shop by tabu search. Annals of Operations Research 1993; 41: 157-183.
[25] Scrich, CR., Armentano AV., Laguna, M. Tardiness minimization in a flexible job shop A Tabu Seaarch Approach. Journal of intelligent manufacturing. 2004; 15: 103-115.
[26] Alvarez-Valdes, R., Fuertes, A., Tamarit, J.M., Gimenez, G., Ramos, R. A heuristic to schedule flexible job-shop in a glass factory. European Journal of Operational Research, 2005; 165: 525–534.
[27] Pezzella, F., Morganti, G., Ciaschetti, G.A genetic algorithm for the Flexible Job-shop Scheduling Problem. Computers & Operations Research 2008; 35: 3202 - 3212.
[28] Baykasoğlu A. Linguistic-based meta-heuristic optimization model for flexible job shop scheduling. International Journal of Production Research, 2002; 40(17): 4523-4543.
[29] Zhang, L., Tang, Q., Wu, Z., Wang, F. Mathematical modeling and evolutionary generation of rule sets for energy-efficient flexible job shops. Energy 2017; 138: 210-227.
[30] Tay, J.C., Ho, N.B. Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computers & Industrial Engineering, 2008; 54: 453–473.
[31] Doh, HH., Yu, JM., Kim, JS., Lee, DH., Nam, SH. A priority scheduling approach for flexible job shops with multiple process plans. International Journal of Production Research, 2013; 51(12), 3748-3764.
[32] Barman, S. Simple priority rule combinations: An approach to improve both flow time and tardiness. International Journal of Production Research, 1997; 35(10): 2857-2870.
[33] Vepsalainen, APJ., Morton, TE. Priority Rules for Job Shops with Weighted Tardiness Costs. Management Science 1987; 33(8): 1035-1047.
[34] Fattahi, P., Mehrebad, MS., Jolai, F. Mathematical modeling and heuristic approaches to flexible job shop scheduling problems, J. Intell. Manuf. 2007; 18: 331–342.
[35] Amiri, M., Zandieh, M., Yadani, M., Bagheri, A. A variable neighbourhood search algorithm for the flexible job-shop scheduling problem. International Journal of Production Research 2010; 48(19): 5671-5689.
[36] Bagheri, A., Zandieh, M., Mahdavi, I., Yazdani, M. An artificial immune algorithm for the flexible job-shop scheduling problem. Future Generation Computer Systems, 2010; 26 (4): 533-541.
[37] Ennigrou, M., Ghédira, K., 2008. New local diversification techniques for flexible job shop scheduling problem with a multi-agent approach. Autonomous Agents and Multi-Agent Systems, 17(2), 270–287.
[38] Gao, L., Peng, C., Chi Zhou, and Li, P. 2006. Solving Flexible Job-shop Scheduling Problem Using General Particle Swarm Optimization. Proceedings of the 36th CIE Conference on Computers & Industrial Engineering.
[39] Gutiérrez, C., García-Magariño, I., 2011. Modular design of a hybrid genetic algorithm for a flexible job–shop scheduling problem. Knowledge-Based Systems, 24, 1, 102-112.
[40] Himida, A.B., Haouari, M., Huguet, M-J., Lopez, P., 2010. Discrepancy search for the flexible job shop scheduling problem. Computers & Operations Research, 37, (12), 2192-2201.
[41] Ho, N.B., and Tay, J.C., 2004. "GENACE: an efficient cultural algorithm for solving the flexible job-shop problem," Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), Portland, OR, USA, pp. 1759-1766 Vol.2.
[42] Ho, N.B., Tay, J.C., Lai, E, M.-K. 2007. An effective architecture for learning and evolving flexible job-shop schedules. European Journal of Operational Research, 179(2), 316-333.
[43] Holthaus, O. and Rajendran, C. 1997. Efficient dispatching rules for scheduling in a job shop, International Journal of Production Economics, 48: 87-105.
[44] Ida, K. and Oka, K. 2011. Flexible Job-Shop Scheduling Problem by Genetic Algorithm. Electrical Engineering in Japan,177,(3), 505-511.
[45] Karimi, H., Rahmati, S.H.A., Zandieh, M., 2012. An efficient knowledge-based algorithm for the flexible job shop scheduling problem. Knowledge-Based Systems, 36, 236-244.
[46] Koruca, H.İ., Aydemir, E., 2014. A Priority Rule Based Production Scheduling Module on Faborg-Sim Simulation Tool. Gazi University Journal of Science, 27(4), 1143-1155.
[47] Li, J-Q., Pan, Q-K., Suganthan, P.H., Chua, T.,J., 2011. A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem. The International Journal of Advanced Manufacturing Technology 52, 5–8, 683–697.
[48] Najid, N.N., Dauzere-Peres, S. and Zaidat, A. 2002. "A modified simulated annealing method for flexible job shop scheduling problem," IEEE International Conference on Systems, Man and Cybernetics, Yasmine Hammamet, Tunisia, pp. 6 pp. vol.5
[49] Ong, Z.X., Tay, J.C., Kwoh, C.K., 2005. Applying the Clonal Selection Principle to Find Flexible Job-Shop Schedules. International Conference on Artificial Immune Systems ICARIS 2005: Artificial Immune Systems, 442-455.
[50] Raghu, T. S. and Rajendran, C. 1993. An efficient dynamic dispatching rule for scheduling in a job shop, International Journal of Production Economics, 32: 301-313.
[51] Rahmati, S.H.A., Zandieh, M., 2012. A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 58, (9–12), 1115–1129.
[52] Ramasesh, R., 1990. Dynamic Job Shop Scheduling: A Survey of Simulation Research. OMEGA, 18, 43-57
[53] Sargent, R.G., 2010. Verification and Validation of Simulation Models, Proceedings of the 2010 IEEE Winter Simulation Conference, 166-183.
[54] Tang, J., Zhang, G., Lin, B., Zhang, B., 2011. A Hybrid Algorithm for Flexible Job-Shop Scheduling Problem. Procedia Engineering, 5, 3678-3683.
[55] Teekeng, W., Thammano, A., 2011. A Combination of Shuffled Frog Leaping and Fuzzy Logic for Flexible Job-Shop Scheduling Problems., Procedia Computer Science, 6, 69-75.
[56] Teekeng, W., Thammano, A. 2012. Modified Genetic Algorithm for Flexible Job-Shop Scheduling Problems. Procedia Computer Science, 12, 122-128.
[57] Teymourifar, A., Bahadir, O., Ozturk, G., 2018. Dynamic Priority Rule Selection for Solving Multi-objective Job Shop Scheduling Problems. Universal Journal of Industrial and Business Management,6 (1), 11-22.
[58] Xing, L-N., Chen, Y-W. Wang, P., Zhao, O-S., Xiong, J., 2010. A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems. Applied Soft Computing, 10, (3), 888-896.
[59] Wang (a), L., Zhou, G., Xu, Y., Wang, S., Liu, M., 2012. An effective artificial bee colony algorithm for the flexible job-shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 60, (1–4), 303–315.
[60] Wang (b), L., Wang, S., Xu, Y., Zhou, G., Liu, M., 2012. A bi-population based estimation of distribution algorithm for the flexible job-shop scheduling problem. Computers & Industrial Engineering, 62, (4), 917-926.
[61] Yazdani, M., Amiri, M., Zandieh, M., 2010. Flexible job-shop scheduling with parallel variable neighborhood search algorithm. Expert Systems with Applications, 37, (1), 678-687.
[62] Yuan, Y., Xu, H., 2012. HHS/LNS: An integrated search method for flexible job shop scheduling," IEEE Congress on Evolutionary Computation, Brisbane, QLD, 1-8.
[63] Yuan, Y., Xu, H., 2013. Flexible job shop scheduling using hybrid differential evolution algorithms., Computers & Industrial Engineering, 65, (2), 246-260.
[64] Zandieh, M., Mahdavi, I., Bagheri, A. 2008. Solving the Flexible Job-Shop Scheduling Problem by a Genetic Algorithm. Journal of Applied Sciences, 8 (24), 4650-4655.
[65] Zhang, G., Gao, L., Shi, Y., 2011. An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Systems with Applications, 38, 4, 3563-3573.
Published
2021-12-31
How to Cite
Demir, Y., & Yilmaz, H. (2021). An efficient priority rule for flexible job shop scheduling problem. Journal of Engineering Research and Applied Science, 10(2), 1906-1918. Retrieved from http://www.journaleras.com/index.php/jeras/article/view/269
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Articles
