Advertisement

Annals of Operations Research

, Volume 229, Issue 1, pp 451–474 | Cite as

Using the gravitational emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in Small and Medium Enterprises

  • Ali Asghar Rahmani Hosseinabadi
  • Hajar Siar
  • Shahaboddin Shamshirband
  • Mohammad Shojafar
  • Mohd Hairul Nizam Md. Nasir
Article

Abstract

Scheduling problems are naturally dynamic. Increasing flexibility will help solve bottleneck issues, increase production, and improve performance and competitive advantage of Small Medium Enterprises (SMEs). Maximum make span, as well as the average workflow time and latency time of parts are considered the objectives of scheduling, which are compatible with the philosophy of on-time production and supply chain management goals. In this study, these objectives were selected to optimize the resource utilization, minimize inventory turnover, and improve commitment to customers; simultaneously controlling these objectives improved system performance. In the job-shop scheduling problem considered in this paper, the three objectives were to find the best total weight of the objectives, maximize the number of reserved jobs and improve job-shop performance. To realize these targets, a multi-parametric objective function was introduced with dynamic and flexible parameters. The other key accomplishment is the development of a new method called TIME_GELS that uses the gravitational emulation local search algorithm (GELS) for solving the multi-objective flexible dynamic job-shop scheduling problem. The proposed algorithm used two of the four parameters, namely velocity and gravity. The searching agents in this algorithm are a set of masses that interact with each other based on Newton’s laws of gravity and motion. The results of the proposed method are presented for slight, mediocre and complete flexibility stages. These provided average improvements of 6.61, 6.5 and 6.54 %. The results supported the efficiency of the proposed method for solving the job-shop scheduling problem particularly in improving SME’s productivity.

Keywords

Flexible job-shop Scheduling Makespan GELS Algorithm Newton’s law Small Medium Enterprises 

Notes

Acknowledgments

This research was supported financially by the University of Malaya Grant (no. RG316-14AFR).

References

  1. Bagrezai, A., Makki, S. V. A.-D., & Rostami, A. S. (2013). A new energy consumption algorithm with active sensor selection using GELS in target coverage WSN. International Journal of Computer Science Issues, 10(4), 11–18.Google Scholar
  2. Baker, K. R. (1974). Introduction to sequencing and scheduling. New York: Wiley.Google Scholar
  3. Balachandar, S. R., & Kannan, K. (2010). A meta-heuristic algorithm for Set covering problem based on gravity. International Journal of Computational and Mathematical Sciences, 4, 223–228.Google Scholar
  4. Bennett, K. P., & Parrado-Hernández, E. (2006). The interplay of optimization and machine learning research. Journal of Machine Learning Research, 7, 1265–1281.Google Scholar
  5. Brandimarte, P. (1993). Routing and scheduling in a flexible job shop by tabu search. Annals of Operations Research, 41, 157–183.CrossRefGoogle Scholar
  6. Chunlin, L., Xiu, Z. J., & Layuan, L. (2009). Resource scheduling with conflicting objectives in grid environments: Model and evaluation. Journal of Network and Computer Applications, 32(3), 760–769.Google Scholar
  7. Dorigo, M. & Stützle, T. (2004). Bradford Book: Ant colony optimization.Google Scholar
  8. Frutos, M., Olivera, A. C., & Tohmé, F. (2010). A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem. Annals of Operations Research, 181, 745–765.CrossRefGoogle Scholar
  9. Goldberg, D. E. (1989). Genetic algorithm in search: Optimization and machine learning.Google Scholar
  10. González-Mendoza, M., Ibarra Orozco, R. E., García Gamboa, A. L., Hernández-Gress, N., Mora-Vargas, J., & Carlos López-Pimentel, J. (2014). Quadratic optimization fine tuning for the support vector machines learning phase. Expert Systems with Applications, 41(3), 886–892.CrossRefGoogle Scholar
  11. Grobler, J., Engelbrecht, A. P., Kok, S., & Yadavalli, S. (2010). Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time. Annals of Operations Research, 180(1), 165–196.CrossRefGoogle Scholar
  12. Halliday, D., Resnick, R., & Walker, J. (2010). Fundamentals of physics. New York: Wiley.Google Scholar
  13. Hosseinabadi, A. R., Farahabadi, A. B., Rostami, M. S., & Lateran, A. F. (2013). Presentation of a new and beneficial method through problem solving timing of open shop by random algorithm gravitational emulation local search. International Journal of Computer Science Issues, 10(1), 745–752.Google Scholar
  14. Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection. Bradford.Google Scholar
  15. Kurz, M. E., & Askin, R. G. (2003). Comparing scheduling rules for flexible flow lines. International Journal of Production Economics, 85, 371–388.CrossRefGoogle Scholar
  16. Kyparisis, G. J., & Koulamas, C. (2004). A note on weighted completion time minimization in a flexible flow shop. Operations Research Letters, 29, 5–11.CrossRefGoogle Scholar
  17. Kyparisis, G. J., & Koulamas, C. (2006). Flexible flow shop scheduling with uniform parallel machines. European Journal of Operational Research, 168, 985–997.CrossRefGoogle Scholar
  18. Lee, Y. H., Jeong, C. S., & Moon, C. (2002). Advanced planning and scheduling with outsourcing in manufacturing supply chain. Computer & Industrial Engineering, 43, 351–374.CrossRefGoogle Scholar
  19. Li, X., & Yin, M. (2013). An opposition-based differential evolution algorithm for permutation flow shop scheduling based on diversity measure. Advances in Engineering Software, 55(0), 10–31. doi: 10.1016/j.advengsoft.2012.09.003.CrossRefGoogle Scholar
  20. Low, C. (2005). Simulated annealing heuristic for flow shop scheduling problem with unrelated parallel machines. Computer & operation Research, 32, 2013–2025.CrossRefGoogle Scholar
  21. Luu, H. V., & Tang, X. (2014). An efficient algorithm for scheduling sensor data collection through multi-path routing structures. Journal of Network and Computer Applications, 38, 150–162.CrossRefGoogle Scholar
  22. Mansouri, N., Dastghaibyfard, G. H., & Mansouri, E. (2013). Combination of data replication and scheduling algorithm for improving data availability in data grids. Journal of Network and Computer Applications, 13, 711–722.CrossRefGoogle Scholar
  23. Nowicki, E., & Smutniciki, C. (1998). The flow shop with parallel machines: A tabu search approach. European Journal of Operational Research, 106, 226–253.CrossRefGoogle Scholar
  24. Paternina-Arboleda, C. D., Montoya-Torres, J. R., Acero-Dominguez, M. J., & Herrera-Hernandez, M. C. (2008). Scheduling jobs on a k-stage flexible flow-shop. Annals of Operations Research, 164(1), 29–40.CrossRefGoogle Scholar
  25. Pooranian, Z., Shojafar, M., Abawajy, J., & Abraham, A. (2013). An efficient meta-heuristic algorithm for grid computing. Journal of Combinatorial Optimization, 1–22. doi: 10.1007/s10878-013-9644-6.
  26. Rezaeian, J., Seidgar, H., & Kiani, M. (2013). Scheduling of a hybrid flow shop with multiprocessor tasks by a hybrid approach based on genetic and imperialist competitive algorithms. Journal of Optimization in Industrial Engineering, 6(13), 1–11.Google Scholar
  27. Riane, F., Artiba, A., & Elmaghraby, S. E. (1998). A hybrid three-stage flow shop problem: Efficient heuristics to minimize makespan. European Journal of Operational Research, 109, 321–329.CrossRefGoogle Scholar
  28. Saffari-Aman, S., Akbarzadeh-T, M. R. & Shamshirband, S. (2008). Load balancing in switching network with multi-ACO. Paper presented at the In Proceedings of the 9th conference on intelligent systems and 2nd joint congress on fuzzy and intelligent systems, Tehran.Google Scholar
  29. Scrich, C. A., Armentano, V. A., & Laguna, M. (2004). Tardiness minimization in a flexible job shop: A tabu search approach. Journal of Intelligent Manufacturing, 15, 103–115.CrossRefGoogle Scholar
  30. Shamshirband, S., Anuar, N. B., Laiha Mat Kiah, M., & Patel, A. (2013). An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique. Engineering Applications of Artificial Intelligence, 26(9), 2105–2127.CrossRefGoogle Scholar
  31. Son, C. (2014). Intelligent jamming region division with machine learning and fuzzy optimization for control of robot’s part micro-manipulative task. Information Sciences, 256, 211–224.CrossRefGoogle Scholar
  32. Su, L. H., Chang, P. C., & Lee, E. S. (1998). A heuristic for scheduling general Job shops to minimize maximum lateness. Mathematical and Computer Modelling, 27, 1–15.Google Scholar
  33. Tay, J. C., & Wibowo, D. (2004). An effective chromosome representation for evolving flexible job-shop scheduling. Genetic and Evolutionary Computation Conference, 3103, 210–221.Google Scholar
  34. Tkindt, V., & Billaut, J. C. (2002). Multi criteria scheduling Theory: Models and algorithms. Berlin: Springer.CrossRefGoogle Scholar
  35. Tvay, J. C. & Ho, N. B. (2007). Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computer & Industrial Engineering.Google Scholar
  36. Ventura, J., & Yoon, S.-H. (2013). A new genetic algorithm for lot-streaming flow shop scheduling with limited capacity buffers. Journal of Intelligent Manufacturing, 24(6), 1185–1196. doi: 10.1007/s10845-012-0650-9.CrossRefGoogle Scholar
  37. Voudouris, C. & Tsang, E. (1995). Guided local search. European Journal of Operational Research, 1–18.Google Scholar
  38. Webster, B. (2004). Solving combinatorial optimization problems using a new algorithm based on gravitational attraction. Melbourne.Google Scholar
  39. Wong, K. I., Wong, P. K., Shun Cheung, C., & Man Vong, C. (2013). Modeling and optimization of biodiesel engine performance using advanced machine learning methods. Energy, 55, 519–528.CrossRefGoogle Scholar
  40. Xia, W., & Wu, Z. (2005). An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering, 48, 409–425.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Ali Asghar Rahmani Hosseinabadi
    • 1
  • Hajar Siar
    • 2
  • Shahaboddin Shamshirband
    • 3
  • Mohammad Shojafar
    • 4
  • Mohd Hairul Nizam Md. Nasir
    • 5
  1. 1.Young Research ClubBehshahr Branch, Islamic Azad UniversityBehshahrIran
  2. 2.Department of Electrical and Computer EngineeringSemnan UniversitySemnanIran
  3. 3.Department of Computer System and Technology, Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  4. 4.Department of Information Engineering Electronics and Telecommunications (DIET)Sapienza University of RomeRomeItaly
  5. 5.Department of Software Engineering, Faculty of Computer Science and Information TechnologyUniversity of Malaya (UM)Kuala LumpurMalaysia

Personalised recommendations