Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-26T09:08:48.593Z Has data issue: false hasContentIssue false

Nonpermutation flow line scheduling by ant colony optimization

Published online by Cambridge University Press:  19 June 2013

Andrea Rossi
Affiliation:
Department of Civil and Industrial Engineering, University of Pisa, Pisa, Italy
Michele Lanzetta*
Affiliation:
Department of Civil and Industrial Engineering, University of Pisa, Pisa, Italy
*
Reprint requests to: Michele Lanzetta, Department of Civil and Industrial Engineering, University of Pisa, Largo Lazzarino, 56122 Pisa, Italy. E-mail: lanzetta@unipi.it

Abstract

A flow line is a conventional manufacturing system where all jobs must be processed on all machines with the same operation sequence. Line buffers allow nonpermutation flowshop scheduling and job sequences to be changed on different machines. A mixed-integer linear programming model for nonpermutation flowshop scheduling and the buffer requirement along with manufacturing implication is proposed. Ant colony optimization based heuristic is evaluated against Taillard's (1993) well-known flowshop benchmark instances, with 20 to 500 jobs to be processed on 5 to 20 machines (stages). Computation experiments show that the proposed algorithm is incumbent to the state-of-the-art ant colony optimization for flowshop with higher job to machine ratios, using the makespan as the optimization criterion.

Type
Regular Articles
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Amaya, J.E., Cotta, C., & Fernández-Leiva, A.J. (2012). Solving the tool switching problem with memetic algorithms. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 26(2), 221235.CrossRefGoogle Scholar
Blum, C., & Sampels, M. (2004). An ant colony optimization algorithm for shop scheduling problem. Journal of Mathematical Modelling and Algorithms 3, 285308.CrossRefGoogle Scholar
Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. New York: Oxford University Press.CrossRefGoogle Scholar
Brucker, P., Heitmann, S., & Hurink, J. (2003). Flow-shop problems with intermediate buffers. OR Spectrum 25, 549574.CrossRefGoogle Scholar
Elbeltagi, E., Hegazy, T., & Grierson, D. (2005). Comparison among five evolutionary-based optimization algorithms. Advanced Engineering Informatics 19(1), 4353.CrossRefGoogle Scholar
Färber, G., & Coves Moreno, A.M. (2006). Benchmark results of a genetic algorithm for non-permutation flowshops using constrained buffers. 10th Int. Research/Expert Conf., Trends in the Development of Machinery and Associated Technology, TMT 2006, Barcelona-Lloret de Mar, September 11–15.Google Scholar
Kumar, R., Tiwari, M.K., & Shankar, R. (2003). Scheduling of flexible manufacturing system: an ant colony optimization approach. Journal of Engineering Manufacture 217, 14431453.CrossRefGoogle Scholar
Nawaz, M., Enscore, E.E. Jr., & Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. OMEGA International Journal of Management Science 11(1), 9195.CrossRefGoogle Scholar
Nowicki, E., & Smutnicki, C. (1996). A fast taboo search algorithm for the job-shop problem. Management Science 42(6), 797813.CrossRefGoogle Scholar
Rajendran, C., & Ziegler, H. (2004). Ant-colony algorithms for permutation flowshop scheduling to minimize makespan/total flowtime of jobs. European Journal of Operational Research 155, 426438.CrossRefGoogle Scholar
Ribas, I., Leisten, R., & Framinan, J.M. (2010). Review and classification of hybrid flowshop scheduling problems from a production system and a solutions procedure perspective. Computers & Operations Research 37, 14391454.CrossRefGoogle Scholar
Rossi, A., & Dini, G. (2007). Flexible job-shop scheduling with routing flexibility and separable setup time using ant colony optimisation method. Robotics and Computer Integrated Manufacturing 23, 503516.CrossRefGoogle Scholar
Rossi, A., & Lanzetta, M. (2013 a). Native metaheuristics for non-permutation flowshop scheduling. Journal of Intelligent Manufacturing. Advance online publication. doi:10.1007/s10845-012-0724-8Google Scholar
Rossi, A., & Lanzetta, M. (2013 b). Scheduling flow lines with buffers by ant colony digraph. Expert Systems with Applications. Advance online publication. doi:10.1016/j.eswa.2012.12.041CrossRefGoogle Scholar
Rossi, A., Pandolfi, A., & Lanzetta, M. (in press). Dynamic setup rules for hybrid flowshop scheduling with parallel batching machines. International Journal of Production Research.Google Scholar
Rossi, A., Puppato, A., & Lanzetta, M. (2012). Heuristics for scheduling a two-stage hybrid flow shop with parallel batching machines: an application on hospital sterilization plant. International Journal of Production Research. Advance online publication. doi:10.1080/00207543.2012.737942Google Scholar
Ruiz, R., & Maroto, C. (2005). A comprehensive review and evaluation of permutation flowshop heuristics. European Journal of Operational Research 165, 479494.CrossRefGoogle Scholar
Sadjadi, S.J., Bouquard, J.L., & Ziaee, M. (2008). An ant colony algorithm for the flowshop scheduling problem. Journal of Applied Sciences 8(21), 39383944.CrossRefGoogle Scholar
Stuetzle, T. (1998). An ant approach to the flow shop problem. Proc. 6th European Congr. Intelligent Techniques and Soft Computing (EUFIT'98), pp. 1560–1564, Verlag Mainz, Wissenschaftsverlag, Aachen.Google Scholar
Taillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research 64, 278285.CrossRefGoogle Scholar
Wang, J.F., Liu, J.H., Li, S.Q., & Zhong, Y.F. (2003). Intelligent selective disassembly using the ant colony algorithm. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17(4), 325333.CrossRefGoogle Scholar
Ying, K.-C., & Lin, S.-W. (2007). Multi-heuristic desirability ant colony system heuristic for non-permutation flowshop scheduling problems. International Journal of Advanced Manufacturing Technology 33, 793802.CrossRefGoogle Scholar