Shop orders scheduling: Dispatching rules and genetic algorithms based approaches

20th European Modeling and Simulation Symposium, EMSS 2008

Volumn , Issue , 2008, Pages 817-823

  Cimino, Antonio ^a   Longo, Francesco ^a   Mirabelli, Giovanni ^a   Papoff, Enrico ^a  

^a UNIVERSITY OF CALABRIA   (Italy)

Author keywords

Genetic algorithms; Manufacturing systems; Modeling simulation; Production planning; Shop order scheduling

Indexed keywords

DISPATCHING RULES; ORDER SCHEDULING; PRODUCTION PLANNING; PRODUCTION SYSTEM; SCHEDULING PROBLEM; SHORT PERIODS;

COMPUTER SIMULATION; INDUSTRIAL APPLICATIONS; INDUSTRIAL RESEARCH; MANUFACTURE; PLANNING; PRODUCTION CONTROL; PRODUCTION ENGINEERING; SCHEDULING;

GENETIC ALGORITHMS;

EID: 84871315573     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Aytug, H., Khouja, M., Vergara, F.E., 2003. Use of genetic algorithms to solve production and operations management problems: A review, International Journal of Production Research 41 (17), 3955-4009.
2. Balci, O., 1998. Verification, Validation and Testing. In Handbook of Simulation, edited by J. Banks, pp. 335-393, New York: Wiley Interscience
3. Banks, J., 1998. Handbook of simulation, New York: Wiley Interscience.
4. Brah, S. A., Hunsucker, J. L., 1991. Branch and bound algorithm for the flow shop with multiple processors. European Journal of Operational Research, 51, 88-99.
5. Bruzzone, A.G., Bocca, E., Longo, F., Massei, M., Logistics Node Design and Control over the Whole Life Cycle based on Web Based Simulation, International Journal of Internet Manufacturing and Services, I(1), pp. 32-50.
6. rd, Calgary Canada
7. Curcio, D., Longo, F., Mirabelli, G., 2007. Manufacturing Process Management Using A Flexible Modeling & Simulation Approach. Proceedings of Winter Simulation Conference, 9-12 December, Washington D.C., USA.
8. De Sensi, G., Longo, F., Mirabelli, G., Inventory policies analysis under demand patterns and lead times constraints in a real supply chain, International Journal of Production Research, doi: 10.1080/00207540701528776
9. Demirkol, E., Uszoy, R., 2000. Decomposition methods for reentrant flow shops with sequence dependent setup-times, Journal of Scheduling 3, 155-177.
10. Department of Defense, Deputy under Secretary of Defence, DoD modelling and simulation (M&S) management, 1994. DoD Directive 5000.59.
11. Jones, A., Rabelo, L., Yih, Y., 1995. A hybrid approach for real-time sequencing and scheduling. International Journal of Computer Integrated Manufacturing, 8(2), 145-154.
12. Longo, F., Mirabelli, G., An Advanced Supply Chain Management Tool Based on Modeling & Simulation, Computer and Industrial Engineering, 54(3), pp 570-588.
13. Longo, F., Mirabelli, G., Papoff, E., 2006. Material Flow Analysis and Plant Lay-Out Optimization of a Manufacturing System, International Journal of Computing, 5(1), pp. 107-116.
14. rd, Calgary Canada
15. Monch, L., Rose, O., Sturm, R., 2003. A simulation framework for performance assessment of shop-.oor control systems. Simulation: Transactions of the Society of Modeling and Computer Simulation International 79 (3), 163-170.
16. Montgomery, D. C., Runger, G. C., 2006. Applied Statistics and Probability for Engineers. New York: Wiley Edition.
17. Park BJ, Choi HR, Kim HS, 2003. A hybrid genetic algorithm for the job shop scheduling problems. Computer and Industrial Engineering 45, 597-613.
18. Pinedo, M.L., 2002. Scheduling: Theory, Algorithms, and Systems, second ed., Englewood Cliffs: Prentice-Hall.
19. Sim, S.K., Yeo, K.T., Lee, W.H., 1994. An expert neural network system for dynamic job shop scheduling. International Journal of Production Research, 32(8), 1759-1773.