Utilizing Model Knowledge for Design Developed Genetic Algorithm to Solving Problem


  • hamidreza salmani mojaveri Science and Research Branch, Islamic Azad University




Optimization, Parallel Machines, Genetic Algorithm, Dynamic and Adjustment of controlling parameters


One of the discussed topics in scheduling problems is Dynamic Flexible Job Shop with Parallel Machines (FDJSPM). Surveys show that this problem because of its concave and nonlinear nature usually has several local optimums. Some of the scheduling problems researchers think that genetic algorithms (GA) are appropriate approach to solve optimization problems of this kind. But researches show that one of the disadvantages of classical genetic algorithms is premature convergence and the probability of trap into the local optimum. Considering these facts, in present research, represented a developed genetic algorithm that its controlling parameters change during algorithm implementation and optimization process. This approach decreases the probability of premature convergence and trap into the local optimum. The several experiments were done show that the priority of proposed procedure of solving in field of the quality of obtained solution and convergence speed toward other present procedure.


Download data is not yet available.

Author Biography

hamidreza salmani mojaveri, Science and Research Branch, Islamic Azad University

department of technology management


- Tay J.C. and Wibowo D., “An Effective Chromosome Representation for Evolving Flexible Job-Shop Scheduling”, Genetic and Evolutionary Computation Conference, (2004).

- Tay J.C. and Ho N.B., “Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems”, Computer & Industrial engineering, (2007), Available from .

- Kim Y.K., Park K. and Ko J., “A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling”, Computers & Operations Research, 30, (2004), 1151–1171.

- Bruker, P., Jurisch, B. and Sievers, B., “Discrete Applied Mathematics”, 49, (1994), 107-127.

- Carlier, J., and Pinson, E., Management Science, 35, (1989), 164-176.

- Gen, M. and Cheng, R., "Genetic Algorithms and Engineering Design", John Wiley & Sons, (1997).

- Brandimarte P. "Theory and Methodology, Exploiting process plan flexibility in production scheduling: A multi-objective approach". European Journal of Operational Research, 114, (1999), 59-71.

- Ghedjati, F., “Genetic algorithms for the job-shop scheduling problem with unrelated parallel constraints: heuristic mixing method machines and precedence”, Computer & Industrial Engineering, 37, (1999), 39-42.

- Kacem, I., Hammadi, S. and Borne, P., “Approach by localization and multi objective evolutionary optimization for Flexible Job-Shop scheduling problems”. IEEE Transaction on Systems, Man and Cybernetics, 32(1), (2002), 1–13.

- Lee, Y.H., Jeong, C.S. and Moon, C., “Advanced planning and scheduling with outsourcing in manufacturing supply chain”, Computer & Industrial Engineering, 43, (2002), 351-374.

- Gen M. and Cheng R. "Genetic Algorithm in Search, Optimization and Machine Learning”, Addition-Wesley, Reading, MA, (2004).

- Kurz, M.E. and Askin, R.G., “Scheduling flexible flow-lines with sequence-dependent setup times”, European Journal of Operational Research, 159, (2004), 66-82.

- Kurz, M.E. and Askin, R.G., “Comparing scheduling rules for flexible flow-lines”, Int. J. Production Economics, 85, (2003), 371-388.

- Ho, N.B., Tay, J.C. and Lai, E. “An effective architecture for learning and evolving Flexible Job Shop schedules”. European Journal of Operational Research, 179, (2007), 316–333.

- Reeves Coline R., "Modern Heuristic Techniques for Combinatorial Problems", John Wiley & Sons, (1993).

- Dagli, C.H. and Sittisathanchai, S., “Genetic neuro-schedular: a new approach for job shop scheduling”, International Journal of Production Economics, 41, (1995), 135-145.

- Gao J., Gen M. and Sun L., "Scheduling jobs and maintenances in flexible job shop with a hybrid genetic algorithm", Jornal Intelligent Manufacturing, 17, (2006), 493–507.

- Abbasian, M. and Nahavandi, N., "Minimization Flow Time in a Flexible Dynamic Job Shop with Parallel Machines", Tehran, Tarbiat Modares University, Engineering Department of Industrial Engineering, Master of Science Thesis, (2008).

- Abbasian, M. and Nahavandi, N., "Minimization Flow Time in a Flexible Dynamic Job Shop with Parallel Machines", Tehran, 6th International Industrial Engineering Conference, (2009).

- Amiri, M., Jamshidi, S.F., and Sadeghiani J.S. “A Genetic Algorithm Approach for Statistical Multi-Response Models Optimization: A Case Study”, Journal of Science & Technology, 49, pp. 131-137, (2009), Available from .

- Moreno-Torres, J.G., Llora, X., and Goldberg D.E. “Binary Representation in Gene Expression Programming: Towards a Better Scalability”, IlliGAL Report No. 2009003, (2009), Available from .

- Verma, A., Llora, X., Venkataraman, S., Goldberg, D.E. and Campbell R.H. “Scaling eCGA Model Building via Data-Intensive Computing”, IlliGAL Report No. 2010001, (2010).

- Jansen K. "APPROXIMATION ALGORITHMS FOR FLEXIBLE JOB SHOP PROBLEMS", International Journal of Foundations of Computer Science, (2001).

- Low, C., “Simulated annealing heuristic for flow shop scheduling problem with unrelated parallel machines”, Computer & operation Research, 32, (2005), 2013-2025.




How to Cite

salmani mojaveri, hamidreza. (2018). Utilizing Model Knowledge for Design Developed Genetic Algorithm to Solving Problem. International Journal of Professional Business Review, 3(2), 172–186. https://doi.org/10.26668/businessreview/2018.v3i2.49