An efficient job shop scheduling algorithm based on artificial bee colony

Scientific Research and Essays

Volumn 6, Issue 12, 2011, Pages 2578-2596

  Yin, Minghao ^a,b   Li, Xiangtao ^a   Zhou, Junping ^a  

^a NORTHEAST NORMAL UNIVERSITY   (China)

^b JILIN UNIVERSITY   (China)

Author keywords

Artificial bee colony; Job shop scheduling; Local search; Memetic algorithm

Indexed keywords

APOIDEA;

EID: 79960083429     PISSN: None     EISSN: 19922248     Source Type: Journal    
DOI: None     Document Type: Article

References (45)

1. Aarts EHL, Laarhoven Van PJM, Lenstra JK, Ulder NLJ (1994). A computational study of local search algorithms for job shop scheduling. ORSA J. Comput., 6: 118-125.
2. Aiex RM, Binato S, Resende MGC (2003). Parallel GRASP with path-relinking for job shop scheduling. Parallel Comput., 29: 393-430.
3. Aldowaisan T, Allahverdi A (2003). New heuristics for no-wait flowshops to minimize makespan. Comput. Oper. Res., 30: 1219-1231.
4. Amirthagadeswaran KS, Arunachalam VP (2006). Improved solutions for job shop scheduling problems through genetic algorithm with a different method of schedule deduction. Int. J. Adv. Manuf. Technol., 28: 532-540.
5. Beasley JE (1990). OR-Library: Distributing test problems by electronic mail. J. Oper. Res. Soc., 41: 1069-1072.
6. Binato S, Hery WJ, Loewenstern DM, Resende MGC (2001). A GRASP for job shop scheduling, in Essays and Surveys in Metaheuristics. Boston, MA: Kluwer, pp. 59-80.
7. Coello CAC, Rivera DC, Cortes NC (2003). Use of an artificial immune system for job shop scheduling. in Proc. 2nd Int. Conf. Artif. Immune Syst., 2787: 1-10.
8. Croce FD, Tadei R, Volta G (1995). A genetic algorithm for the job shop problem. Comput. Oper. Res., 22: 15-24.
9. Dauzere PS, Paulli J (1997). An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search. Ann. Oper. Res., 70: 281-306.
10. David HW, William GM (1997). No Free Lunch Theorems for Optimization, IEEE trans. Evolut. Comput., 1: 67-82.
11. Dorndorf U, Pesch E (1995). Evolution based learning in a job shop scheduling environment. Comput. Oper. Res., 22: 25-40.
12. Gang X, Wu ZM (2004). Deadlock-free scheduling strategy for automated production cell. IEEE Trans. Syst., Man, Cybern. A, Syst. Humans. 34(1): 113-122.
13. Garey MR, Johnson DS, Sethi R (1976). The complexity of flow shop and job shop scheduling. Math. Oper. Res., 1: 117-129.
14. Ge HW, Sun L, Liang YC, Qian F (2008). An Effective PSO and AIS-Based Hybrid Intelligent Algorithm for Job-Shop Scheduling. IEEE Trans. Syst., Man, Cybern. A, Syst. Humans. 38: 358-368.
15. Geyik F, Cedimoglu IH (2004). The strategies and parameters of tabu search for job-shop scheduling. J. Intell. Manuf., 15: 439-448.
16. Goncalves JF, Mendes JJDM, Resende MGC (2005). A hybrid genetic algorithm for the job shop scheduling problem. Eur. J. Oper. Res., 167: 77-95.
17. Hajri S, Liouane N, Hammadi S, Borne P (2000). A controlled genetic algorithm by fuzzy logic and belief functions for job shop scheduling. IEEE Trans. Syst., Man, Cybern. B, Cybern., 30(5): 812-818.
18. Karaboga D (2005). An idea based on honey bee swarm for numerical optimization, technical report-tr06,Erciyes University, Engineering Faculty, Computer Engineering Department.
19. Karaboga D, Akay B (2009). A Comparative Study of Artificial Bee Colony Algorithm, Appl. Math. Comput., 214: 108-132.
20. Karaboga D, Basturk B (2007). A powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm. J. Glo. Opti., 39: 459-171.
21. Karaboga D, Basturk B (2008). On The Performance of Artificial Bee Colony (ABC) Algorithm. Appli. Soft. Com., 8: 687-697.
22. Karaboga D, Ozturk C (2010). Fuzzy clustering with artificial bee colony algorithm. Sci. Res. Essays, 5(14): 1899-1902.
23. Kolonko M (1999). Some new results on simulated annealing applied to the job shop scheduling problem. Eur. J. Oper. Res., 113: 123-136.
24. Lian ZG, Jiao B, Gu XS (2006). A similar particle swarm optimization algorithm for job-shop scheduling to minimize makespan. Appl. Math. Comput., 183: 1008-1017.
25. Lin TL, Horng SJ, Kao TW, Chen YH, Run RS, Chen RJ, Lai JL, Kuo IH (2010). An efficient job-shop scheduling algorithm based on particle swarm optimization. Exp. Syst. Appl., 37: 2629-2636.
26. Liu TK, Tsai JT, Chou JH (2006). Improved genetic algorithm for the job-shop scheduling problem. Int. J. Adv. Manuf. Technol., 27: 1021-1029
27. Low C, Yeh JY, Huang KI(2004). A robust simulated annealing heuristic for flow shop scheduling problems. Int. J. Adv. Manuf. Technol., 23: 762-767.
28. Nowicki E, Smutnicki C (1996). A fast taboo search algorithm for the job shop problem. Manage. Sci., 42: 797-813.
29. Nuijten WPW, Aarts EHL (1996). Computational study of constraint satisfaction for multiple capacitated job shop scheduling. Eur. J. Oper. Res., 90: 269-284.
30. Ombuki BM, Ventresca M (2004). Local search genetic algorithms for the job shop scheduling problem Appl. Intell., 21: 99-109.
31. Pan QK, Tasgetiren MF, Suganthan PN, Chua TJ (2010). A Discrete Artificial Bee Colony Algorithm for the Lot-streaming Flow Shop Scheduling Problem. Infor. Sci. doi:10.1016/j.ins.2009.12.025.
32. Ponnambalam SG, Aravindan P, Rajesh SV (2000). A tabu search algorithm for job shop scheduling. Int. J. Adv. Manuf. Technol., 16: 765-771.
33. Sabuncuoglu I, Bayiz M (1999). Job shop scheduling with beam search. Eur. J. Oper. Res., 118: 390-412.
34. Stadtler H (2005). Supply chain management and advanced planning basics, overview and challenges. Eur. J. Oper. Res., 163: 575-588.
35. Storer RH, Wu SD, Park I (1992). Genetic Algorithms in Problem Space for Sequencing Problems, Pro.a Joint US-German Conf. on Oper. Res. Prod. Plan. Con., pp. 584-597.
36. Suresh RK, Mohanasundaram KM (2006). Pareto archived simulated annealing for job shop scheduling with multiple objectives. Int. J. Adv. Manuf. Technol., 29: 184-196.
37. Wang L, Zheng DZ (2002). A modified genetic algorithm for job shop scheduling. Int. J. Adv. Manuf. Technol., 20: 72-76.
38. Wang TY, Wu KB (2000). A revised simulated annealing algorithm for obtaining the minimum total tardiness in job shop scheduling problems. Int. J. Syst. Sci., 31: 537-542.
39. Watson JP, Beck JC, Howe AE (2003). Problem difficulty for tabu search in job-shop scheduling. Artif. Intell., 143: 189-217.
40. Xia WJ, Wu ZM (2006). A hybrid particle swarm optimization approach for the job-shop scheduling problem. Int. J. Adv. Manuf. Technol., 29: 360-366.
41. Yang S, Wang D (2000). Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling. IEEE Trans. Neural Netw. 11: 474-486.
42. Yang S, Wang D (2001). A new adaptive neural network and heuristics hybrid approach for job-shop scheduling. Comput. Oper. Res., 28: 955-971.
43. Yin MH, Zou TT, Gu WX (2010). Reverse bridge theorem undder constraint partition. Math. Pro. Eng., Article ID 617398, doi:10.1155/2010/617398.
44. Yu HB, Liang W (2001). Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling. Comput. Ind. Eng., 39: 337-356.
45. Zhang J, Hu XM, Tan X, Zhong JH, Huang Q (2006). Implementation of an ant colony optimization technique for job shop scheduling problem. Trans. Inst. Meas. Control., 28: 93-108.