AIRCC's International Journal of Computer Science and Information Technology

Open Access Open Access  Restricted Access Subscription Access

An Improve Object-Oriented Approach for Multi-Objective Flexible Job-Shop Scheduling Problem (FJSP)


Affiliations
1 Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria
 

Flexible manufacturing systems are not easy to control and it is difficult to generate controlling systems for this problem domain. Flexible job-shop scheduling problem (FJSP) is one of the instances in this domain. It is a problem which acquires the job-shop scheduling problems (JSP). FJSP has additional routing subproblem in addition to JSP. In routing sub-problem each task is assigned to a machine out of a set of capable machines. In scheduling sub-problem, the sequence of assigned operations is obtained while optimizing the objective function(s). In this work an object-oriented (OO) approach with simulated annealing algorithm is used to simulate multi-objective FJSP. Solution approaches provided in the literature generally use two-string encoding scheme to represent this problem. However, OO analysis, design and programming methodology helps to present this problem on a single encoding scheme effectively which result in a practical integration of the problem solution to manufacturing control systems where OO paradigm is frequently used. Three parameters are considered in this paper: maximum completion time, workload of the most loaded machine and total workload of all machines which are the benchmark used to show the propose system achieve effective result.

Keywords

Object-oriented Manufacturing Control, Object-Oriented Design, Multi-Objective Flexible Job Shop Scheduling, Simulated Annealing Algorithm.
User
Notifications
Font Size

  • Abir, B., H., Mohamed, H., Marie, J., H., Pierre, L., (2010). Discrepancy search for the flexible job shop scheduling. Computers & operation research / Computers and operation research, Elseveir, 2010, 37 (12), p. 2192-2201.

  • Amit Kumar and Rajnesh Singh (2011), “Performance of Genetic Algorithm for Solving Flexible Job-Shop Scheduling Problem.”International Journal of Information Technology and Knowledge Management.VOl. 4, No. 1, pp. 105-108.

  • Anshulika, L., A., Bewoor, (2015). Application of genetic algorithm on job shop scheduling problem to minimizemakespan.International journal of science and research (IJSR).Vol. 5.No. 6.Pp 23197064.

  • Arash, M., L., Kamyar, S., L., Mahdi, H., (2010). Solving flexible job shop scheduling with multi objective approach.International journal of industrial engineering and production research.Vol.21, N0. 4. Pp.197-209

  • Baykasoglu, A., Ozbakir, L., &Sonmez, A. I. (2004). Using multiple objective tabu search and grammars to model and solve multi-objective flexible job-shop scheduling problems. Journal of Intelligent Manufacturing, 15(6), 777–785.

  • Brandimarte, P. (1993). Routing and scheduling in a flexible job shop by taboo search. Annals of Operations Research, 41(3), 157–183.

  • Binay, K., S., (2013). Obtain makespan of job shop scheduling operation.

  • Chen, H., Ihlow, J., & Lehmann, C. (1999). A genetic algorithm for flexible job-shop scheduling.In Proceedings of IEEE international conference on robotics and automation (pp.1120–1125).

  • Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. Chichester, UK: Wiley.

  • Dusan, N. Sormaz, JaikumarArumugam, Ramachandra S Harihara, Chintankumar Patel NarenderNeerukonda(2010). Integration of product design, process planning, scheduling and FMS control using XML data representation. Robotics and Computer-Integrated Manufacturing, 26: 583495.

  • D. Nagendra, A.K. Sood (2015), “Multi Objective Flexible Job Shop Scheduling Optimization Using BFOA.” International Journal of Emerging Technology and Advanced Engineering. Vol. 5, Issue 5, www.ijetae.com, ISSN: 2250-2459.

  • Garey, M. R., Johnson, D. S., &Sethi, R. (1976). The complexity of flow shop and jobshop scheduling. Mathematics of Operations Research, 1, 117–129.

  • Gaurav Kumar, Trilok Singh Bisoniya (2015), “Flexible job shop scheduling operation using genetic algorithm.” International Journal of Innovation in Engineering and Technology (IJIET). Vol. 5 ISSN: 2319-1058

  • Glover, F., & Laguna, M. (1997). Tabu search. Kluwer Academic Publishers.

  • HabibehNazif (2015), “Solving Job Shop Scheduling Problem Using an Ant Colony Algorithm.” Journal of Asian Scientific Research. Vol. 5(5):261-268. ISSN (e): 2226-1331/ISSN (p): 2226-5724.

  • Hongbo Liu, Ajith Abraham, Zuwen Wang, "A Multi-swarm Approach to Multi-objective Flexible Job- shop Scheduling Problems", School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China, Fundamental Informaticae,IOS Press, 2009.

  • Hsu, T., Dupas, R., Jolly, D., &Goncalves, G. (2002). Evaluation of mutation heuristics for the solving of multi-objective flexible job shop by an evolutionary algorithm.In Proceedings of the 2002 IEEE international conference on systems, man and cybernetics (Vol. 5, pp. 655–660).

  • Hurink, E., Jurisch, B., &Thole, M. (1994). Tabu search for the job shop scheduling problem with multi-purpose machine. Operations Research Spektrum, 15, 205–215.

  • Kacem, I., Hammadi, S., & Borne, P. (2002a). Approach by localization and multi-objective evolutionary optimization for flexible job-shop scheduling problems.IEEE transactions on systems, man, and cybernetics, Part C, 32(1), 1–13.

  • Kacem, I., Hammadi, S., & Borne, P. (2002b). Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic. Mathematics and Computers in Simulation, 60, 245–276.

  • Kennedy, J. (1997). Particle swarm: social adaptation of knowledge. In Proceedings of the 1997 IEEE international conference on evolutionary computation (pp. 303–308).USA: Indianapolis.

  • Kennedy, J., &Eberhart, R. (1995). Particle swarm optimization.In Proceedings of the 1995 IEEE international conference on neural network (Vol. IV, No. 4, pp. 1942– 1948).

  • Kong, L., Li, T., Wang, K., Zhu, H., Takano, M., Yu, B., (2015). An improved shuffled frog- leaping algorithms for flexible job shop scheduling.www.mdpi.com/journal/algorithms.

  • Lawler, E. l., Lenstra, J. K., RinnooyKan, A. H. G., &Shmoys, D. B. (1993). Sequencing and scheduling: Algorithms and complexity. In S. C. Graves et al. (Eds.), Logistics of production and inventory (pp. 445–522). Amsterdam: North Holland.

  • L. Ba, Y. Li, M.S Yang, X.Q Gao, Y. Liu (2016), “Modeling and Simulation Of A Multi-Resource Flexible Job Shop Scheduling” int j simul model, Vol. 15, ISSN: 1726-4529.

  • Mastrolilli, M., & Gambardella, L. M. (2000). Effective neighborhood functions for the flexible job shop problem. Journal of Scheduling, 3(1), 3–20.

  • Mesghouni, K., Hammadi, S., & Borne, P. (1997). Evolution programs for job-shop scheduling. In Proceedings of the 1997 IEEE international conference on systems, man and cybernetics (Vol. 1, pp. 720–725).

  • Mohsen, Z., (2014). An efficient heuristic algorithm for flexible job shop scheduling with maintenance constraint.Journal of applied mathematics and science (MathsSJ), Vol.1, 1.

  • Moreno Reyna, A., Gomez Ortega, A., Siera Romero. N., Armando Diaz, D., Fernandez Murillo, S. E, Felix Zarate, G. A., Narvaez Granados, S. L (2012) object oriented programming as an alternative to industrial control, New York: IEEE.

  • M. Lan, T. Xu and L peng (2010), “Solving Flexible Multi-Objective JSP Problem Using An Improved Genetic Algorithm” journal of software, Vol.5, No. 10.

  • ParvizFattahi (2009), “A hybrid multi objective algorithm for flexible job shop scheduling.” World Academy of Science, Engineering and Technology.Vol. 26.

  • P. Wojakowski, D. Warzolek (2013), “Research Study of State Of The Art Algorithms for Flexible Job Shop Scheduling Problem”

  • Parsopoulos, K. E., &Vrahatis, M. N. (2002). Recent approaches to global optimization problems through particle swarm optimization. Natural Computing, 1(2-3), 235–306.

  • Rigao, C. (2004). Tardiness minimization in a flexible job shop: A tabu search approach. Journal of Intelligent Manufacturing, 15(1), 103–115.

  • Shantanu, K., Zanwar, D., R., (2013). Scheduling in job process industry.Journal of mechanical and civil engineering (IOSR-JMCE). ISSN: 2278-1684. Vol. 5. N0.1. pp 01-17.

  • Soad, M., Bestma C., F., (2015). Multi objective flexible job shop scheduling using a modified invasive weed optimization. International Journal on soft computing (IJSC) Vol.6 No1.

  • Sonmez, A. I., &Baykasoglu, A. (1998). A new dynamic programming formulation of (n * m) flow shop sequencing problems with due dates. International Journal of Production Research, 36, 2269–2283.

  • Shi-xin, L, Meng-guang, W, (2000). An object-oriented methodology for solving the RCPSPs with heuristic and meta-heuristics.Production Planning & control. 11(5), 434-442. doi: 10.1080/09537280050051933.

  • T. Ning, C Guo, R. Chen and H. Jin (2016), “A Novel Hybrid Method for Solving Flexible Job Shop Scheduling Problem.”The Open Cybernetic and Systemic Journal, DOI:10.2174/1874110X01610010013.

  • Xia, W. J., & Wu, Z. M. (2005). An effective hybrid optimization approach for multi objective flexible job-shop scheduling problems.Computers and Industrial Engineering, 48(2), 409–425.

  • Y. Yuan, H. Xu (2013), “A Memetic Algorithm for the Multi-Objective Flexible Job Shop Scheduling Problem.”

  • Zohreh, D., Mohammad-R, A., (2014). A novel quantum immune algorithm for multi objective flexible job shop scheduling. International journal of artificial intelligence and application for smart devices.Vol.2, No.1, pp. 57-72.


Abstract Views: 218

PDF Views: 101




  • An Improve Object-Oriented Approach for Multi-Objective Flexible Job-Shop Scheduling Problem (FJSP)

Abstract Views: 218  |  PDF Views: 101

Authors

Bamaiyi Sule
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria
Ibrahim Lawal
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria

Abstract


Flexible manufacturing systems are not easy to control and it is difficult to generate controlling systems for this problem domain. Flexible job-shop scheduling problem (FJSP) is one of the instances in this domain. It is a problem which acquires the job-shop scheduling problems (JSP). FJSP has additional routing subproblem in addition to JSP. In routing sub-problem each task is assigned to a machine out of a set of capable machines. In scheduling sub-problem, the sequence of assigned operations is obtained while optimizing the objective function(s). In this work an object-oriented (OO) approach with simulated annealing algorithm is used to simulate multi-objective FJSP. Solution approaches provided in the literature generally use two-string encoding scheme to represent this problem. However, OO analysis, design and programming methodology helps to present this problem on a single encoding scheme effectively which result in a practical integration of the problem solution to manufacturing control systems where OO paradigm is frequently used. Three parameters are considered in this paper: maximum completion time, workload of the most loaded machine and total workload of all machines which are the benchmark used to show the propose system achieve effective result.

Keywords


Object-oriented Manufacturing Control, Object-Oriented Design, Multi-Objective Flexible Job Shop Scheduling, Simulated Annealing Algorithm.

References