Integrated model for the batch sequencing problem in a multi-stage supply chain: An artificial immune system based approach

International Journal of Production Research

Volumn 47, Issue 4, 2009, Pages 1015-1037

  Shukla, M ^a   Shukla, N ^a   Tiwari, M K ^a   Chan, F T S ^b  

^a NATIONAL INSTITUTE OF FOUNDRY AND FORGE TECHNOLOGY   (India)

^b UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Artificial immune system (AIS); Lead time; Multistage supply chain

Indexed keywords

GENETIC ALGORITHMS; IMMUNOLOGY; SUPPLY CHAIN MANAGEMENT; SUPPLY CHAINS;

ARTIFICIAL IMMUNE SYSTEM (AIS); ARTIFICIAL IMMUNE SYSTEMS; BATCH SEQUENCING PROBLEMS; BLOCKING TIMES; COMPARATIVE ANALYSIS; DUE DATES; INTEGRATED MODELS; LEAD TIME; LEAD TIMES; LOT SIZES; METAHEURISTIC; MULTISTAGE SUPPLY CHAIN; OPERATION TIMES; OPTIMAL SOLUTIONS; SEQUENCE DEPENDENTS; SETUP TIMES; UNDERLYING PROBLEMS; VARIABLE TIMES;

SIMULATED ANNEALING;

EID: 57049119660     PISSN: 00207543     EISSN: 1366588X     Source Type: Journal    
DOI: 10.1080/00207540601158807     Document Type: Article

References (31)

1. Agnetis, A., Detti, P., Meloni, C. and Pacciarelli, D., Set-up coordination between two stages of a supply chain. Ann. Oper. Res., 2001, 107, 1532.
2. Back, T., Evolutionary Algorithms in Theory and Practice, 1996 (Oxford University Press: New York, NY).
3. Bagga, P.C. and Khurana, K., Two machine flow shop with separated sequence-independent setup times: mean completion time criterion. Indian J. Manage. Syst., 1986, 2, 47-57.
4. Binaco, L., Ricciardelli, S., Rinaldi, G. and Sassano, A., Scheduling tasks with sequencedependent processing times. Naval Res. Logist., 1998, 35, 177-184.
5. Birtan, G.R. and Gilbert, S.M., Sequencing production on parallel machines with two magnitudes of sequence-dependent setup cost. J. Manuf. Oper. Manage., 1998, 3, 24-52.
6. Chan, K.C. and Tansri, H., A study of genetic crossover operation on the layout problem. Computers & Indust. Eng., 1994, 26, 537-550.
7. Chen, D., Luh, P.B., Thakur, L.S. and Moreno Jr, J., Optimization-based manufacturing scheduling with multiple resources, setup requirements, and transfer lots. IIE Trans., 2003, 35, 973-985.
8. Chul, J.H., Kim, Y. and Kim, Y.K., A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines. Computers Oper. Res., 1998, 25, 675-690.
9. De Castro, L.N. and von Zuben, F.J., Learning and optimization using the clonal selection principle. IEEE Trans. Evol. Computation, 2002, 6, 239-251 (Special Issue on Artificial Immune System).
10. Drucker, P.F., Management's new paradigms. Forbes, October 1998, 152-177.
11. Gen, M. and Cheng, R., Genetic Algorithm and Engineering Design, 1997 (Wiley: New York).
12. Gonzalez, T. and Sahni, S., Flow shop and job shop scheduling. Oper. Res., 1978, 26, 36-52.
13. Grabowski, J., Skubalska, E. and Smutnicki, C., On flow shop scheduling with release and due dates to minimize maximum lateness. J. Oper. Res. Soc., 1983, 34, 615-620.
14. Hall, N.G. and Sriskandrajah, C., A survey of machine scheduling problem with blocking and no-wait in process. Oper. Res., 1996, 44, 510-525.
15. Ho, D.C.K., Au, K.F. and Newton, E., Empirical research on supply chain management: a critical review and recommendations. Int. J. Prod. Res., 2002, 40, 4415-4430.
16. Kim, S.C. and Bobrowski, P.M., Impact of sequence-dependent setup-times on job shop scheduling performance. Int. J. Prod. Res., 1994, 32, 1503-1520.
17. Kirkpatrick, S., Gelatt, C.D. and Vecchi, M.P., Optimization by simulated annealing. Science, 1983, 220, 671-678.
18. Lambert, D.M. and Copper, M.C., Issue in supply chain. Indust. Market. Manage., 2000, 29, 65-83.
19. Mansouri, S.A., Coordination of set-ups between two stages of a supply chain using multiobjective genetic algorithm. Int. J. Prod. Res., 2005a, 43, 3163-3180.
20. Mansouri, S.A., A multi-objective genetic algorithm for mixed-model sequencing on JIT assembly lines. Euro. J. Oper. Res., 2005b, 167, 696-716.
21. McMullen, P.R., Tarasewich, P. and Frazier, G.V., Using genetic algorithms to solve the multi-product JIT sequencing problem with set-ups. Int. J. Prod. Res., 2000, 38, 2653-2670.
22. Min, H. and Zhou, G., Supply chain modeling: past, present and future. Computer & Indust. Eng., 2002, 43, 231-249.
23. Nowicki, E., The permutation flow shop with buffers: a tabu search approach. Euro. J. Oper. Res., 1999, 166, 205-219.
24. Papadimitriou, C. and Kanellakis, P., Flow-shop scheduling with limited temporary storage. J. Assoe. Computing Much., 1980, 27, 533-549.
25. Rajendra, C. and Ziegler, H., Heuristics for scheduling in a flow shop with setup, processing and removal times separated. Prod. Plan. & Cont., 1997, 8, 568-576.
26. Reddi, S.S. and Ramamoorthy, C.V., On the flow shop sequencing problem with no-wait in process. Oper. Res. Quart., 1972, 23, 323-330.
27. Schaller, J.E., Gupta, J.N.D. and Vakharia, A.J., Scheduling a flow line manufacturing cell with sequence dependent family setup times. Euro. J. Oper. Res., 2000, 125, 324-339.
28. Skjoett Larsen, T., European logistics beyond 2000. Int. J. Phys. Distrib. Logist. Manage., 2000, 30, 377-387.
29. Srikar, B.N. and Ghosh, S., A MILP model for the n-job, m-stage flow shop, with sequencedependent setup times. Int. J. Prod. Res., 1986, 24, 1459-1472.
30. Tiwari, M.K., Prakash Kumar, A. and Mileham, A.R., Determination of an optimal assembly sequence using the Psychoclonal algorithm. IMechE J. Eng. Munuf., Part B, 2005, 219, 137-149.
31. Uzsoy, R., Lee, C. and Martin-Vega, A., Scheduling semiconductor test operations: minimising maximum lateness and number of tardy jobs on a single machine. Naval Res. Logist., 1992, 39, 369-388.