Abstract
The welding process which is one of the most important assembly processes is widespread in the modern manufacturing industry, including aerospace, automotive and engineering machinery. The welding shop scheduling greatly impacts the efficiency of whole production system. However, few studies on the welding shop scheduling problem (WSSP) were reported. In this paper, a mathematical model and an improved discrete artificial bee colony algorithm (DABC) are proposed for the WSSP. Firstly, it is defined where multi-machine can process one job at the same time in the WSSP. Secondly, the mathematical models of WSSP have been constructed. Thirdly, an effective DABC is proposed to solve the WSSP, considering job permutation and machine allocation simultaneously. To improve the performance of proposed DABC algorithm, the effective operators have been designed. Three instances with different scales are used to evaluate the effectiveness of proposed algorithm. The comparisons with other two algorithms including genetic algorithm and grey wolf optimizer are also provided. Experimental results show that the proposed model and algorithm achieve good performance. Finally, the proposed model and DABC algorithm are applied in a real-world girder welding shop from a crane company in China. The results show that proposed model and algorithm reduces 55.17% production time comparing with the traditional algorithm and the scheduled machine allocation provides more reasonable arrangements for workers and machine loads.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Velazquez K, Estrada E, Gonzalez A (2014) Statistical analysis for quality welding process: an aerospace industry case study. J Appl Sci 14(19):2285–2291
Li X, Lu C, Gao L, Xiao S, Wen L (2018) An effective multiobjective algorithm for energy-efficient scheduling in a real-life welding shop. IEEE T Ind Inform 14(12):5400–5409
Mohammad R, Kobti Z (2012) A memetic algorithm for job shop scheduling using a critical-path-based local search heuristic. Memetic Comp 4:231–245
Lu C, Gao L, Li X, Xiao S (2017) A hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industry. Eng Appl Artif Intell 57:61–79
Lin Q, Gao L, Li X, Zhang C (2015) A hybrid backtracking search algorithm for permutation flow-shop scheduling problem. Comput Ind Eng 85:437–446
Wang S, Liu M, Chu C (2015) A branch-and-bound algorithm for two-stage no-wait hybrid flow-shop scheduling. Int J Prod Res 53:1143–1167
Pan Q, Ruiz R (2013) A comprehensive review and evaluation of permutation flowshop heuristics to minimize flowtime. Comput Oper Res 40:117–128
Li X, Ma S (2017) Multiobjective discrete artificial bee colony algorithm for multiobjective permutation flow shop scheduling problem with sequence dependent setup times. IEEE T Eng Manag 64(2):149–165
Barkaoui M (2018) A co-evolutionary approach using information about future requests for dynamic vehicle routing problem with soft time windows. Memetic Comp 10:307–319
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Tech Rep, Comput Eng Dept, Erciyes Univ, Kayseri, Turkey, TR06
Li J, Sang H, Han Y, Wang C, Gao K (2018) Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions. J Clean Prod 181:584–598
Li X, Gao L, Pan Q, Wan L, Chao K (2018) An effective hybrid genetic algorithm and variable neighborhood search for integrated process planning and scheduling in a packaging machine workshop. IEEE T Syst Man Cy-S. https://doi.org/10.1109/tsmc.2018.2881686
Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 24(1):108–132
Tasgetiren M, Pan Q, Suganthan P, Chen A (2010) A discrete artificial bee colony algorithm for the permutation flow shop scheduling problem with total flowtime criterion. In: Proc IEEE congress on evolutionary computation, pp 1–8
Bai J, Liu H (2016) Multi-objective artificial bee algorithm based on decomposition by PBI method. Appl Intell 45(4):976–991
Gao K, Zhang Y, Zhang Y, Su R, Suganthan P (2018) Meta-heuristics for bi-objective urban traffic light scheduling problems. IEEE T Intell Transp. https://doi.org/10.1109/tits.2018.2868728
Gao K, Suganthan P, Pan Q, Tasgetiren M, Sadollah A (2016) Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion. Knowl-Based Syst 109:1–16
Gao K, Suganthan P, Pan Q, Chua T, Chong C, Cai T (2016) An improved artificial bee colony algorithm for multi-objective flexible job shop scheduling problem with fuzzy processing time. Expert Syst Appl 65:52–67
Gong D, Han Y, Sun J (2018) A novel hybrid multi-objective artificial bee colony algorithm for blocking lot-streaming flow shop scheduling problems. Knowl Base Syst 148:115–130
Peng K, Pan Q, Gao L, Zhang B, Pang X (2018) An improved artificial bee colony algorithm for real-world hybrid flowshop rescheduling in steelmaking-refining-continuous casting process. Comput Ind Eng 122:235–250
Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42:1–37
Mirjalili S, Mirjalili S, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
Brucker P (2007) Multiprocessor tasks scheduling algorithms, 5th edn. Springer-Verlag, Berlin
Lopes M, Carvalho J (2007) A branch-and-price algorithm for scheduling parallel machines with sequence dependent setup times. Eur J Oper Res 176:1508–1527
Kalczynski J (2007) On the NEH heuristic for minimizing the makespan in permutation flow shops. OMEGA-Int J Manage S 35(1):53–60
Tasgetiren M, Pan Q, Suganthan P, Oner A (2013) A discrete artificial bee colony algorithm for the no-idle permutation flowshop scheduling problem with the total tardiness criterion. Appl Math Model 37:6758–6779
Ruiz R, Stutzle T (2007) A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. Eur J Oper Res 177:2033–2049
Wang R, Purshouse R, Fleming P (2013) Preference-inspired co-evolutionary algorithms for many-objective optimization. IEEE T Evolut Comput 17:474–494
Wang R, Ishibuchi H, Zhou Z, Liao T, Zhang T (2018) Localized weighted sum method for many-objective optimization. IEEE T Evolut Comput 22:3–18
Li K, Wang R, Zhang T, Ishibuchi H (2018) Evolutionary many-objective optimization: a comparative study of the state-of-the-art. IEEE Access 6:26194–26214
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. 51775216, 51435009 and 51711530038), the Natural Science Foundation of Hubei Province (Grant No. 2018CFA078) and the program for HUST Academic Frontier Youth Team (Grant No. 2017QYTD04).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Instance 1 (10 × 5)
Stage 1 | Stage 2 | Stage 3 | Stage 4 | Stage 5 | |||||
---|---|---|---|---|---|---|---|---|---|
Setup/processing | Setup/processing | Setup/processing | Setup/processing | Setup/processing | |||||
5 | 9 | 4 | 5 | 6 | 24 | 7 | 5 | 4 | 12 |
8 | 13 | 11 | 14 | 7 | 28 | 10 | 7 | 5 | 18 |
10 | 17 | 18 | 22 | 8 | 33 | 12 | 8 | 7 | 24 |
12 | 20 | 25 | 31 | 9 | 35 | 15 | 10 | 8 | 29 |
15 | 25 | 32 | 40 | 10 | 40 | 18 | 12 | 10 | 35 |
18 | 30 | 39 | 49 | 11 | 45 | 21 | 14 | 12 | 41 |
21 | 36 | 46 | 58 | 13 | 51 | 24 | 16 | 13 | 47 |
24 | 40 | 51 | 63 | 14 | 56 | 26 | 18 | 15 | 52 |
8 | 12 | 7 | 8 | 9 | 27 | 10 | 8 | 7 | 15 |
12 | 17 | 15 | 18 | 11 | 32 | 14 | 11 | 9 | 22 |
Instance 2 (30 × 5)
Stage 1 | Stage 2 | Stage 3 | Stage 4 | Stage 5 | |||||
---|---|---|---|---|---|---|---|---|---|
Setup/processing | Setup/processing | Setup/processing | Setup/processing | Setup/processing | |||||
13 | 20 | 21 | 25 | 11 | 36 | 15 | 11 | 10 | 27 |
15 | 24 | 28 | 35 | 12 | 39 | 18 | 14 | 11 | 33 |
19 | 29 | 36 | 44 | 14 | 44 | 22 | 16 | 14 | 39 |
21 | 33 | 42 | 52 | 14 | 48 | 24 | 17 | 15 | 44 |
25 | 39 | 50 | 61 | 17 | 54 | 28 | 19 | 17 | 50 |
30 | 48 | 57 | 71 | 20 | 64 | 32 | 26 | 21 | 60 |
13 | 17 | 12 | 13 | 14 | 32 | 15 | 13 | 12 | 20 |
14 | 21 | 17 | 22 | 13 | 36 | 16 | 15 | 11 | 26 |
18 | 23 | 26 | 28 | 16 | 39 | 20 | 14 | 15 | 30 |
20 | 28 | 33 | 39 | 17 | 43 | 23 | 18 | 16 | 37 |
23 | 33 | 40 | 48 | 18 | 48 | 26 | 20 | 18 | 43 |
26 | 36 | 47 | 55 | 19 | 51 | 29 | 20 | 20 | 47 |
29 | 44 | 54 | 66 | 21 | 59 | 32 | 24 | 21 | 55 |
33 | 49 | 60 | 72 | 23 | 65 | 35 | 27 | 24 | 61 |
14 | 18 | 13 | 14 | 15 | 33 | 16 | 14 | 13 | 21 |
17 | 25 | 20 | 26 | 16 | 40 | 19 | 19 | 14 | 30 |
22 | 29 | 30 | 34 | 20 | 45 | 24 | 20 | 19 | 36 |
21 | 32 | 34 | 43 | 18 | 47 | 24 | 22 | 17 | 41 |
27 | 37 | 44 | 52 | 22 | 52 | 30 | 24 | 22 | 47 |
27 | 39 | 48 | 58 | 20 | 54 | 30 | 23 | 21 | 50 |
33 | 48 | 58 | 70 | 25 | 63 | 36 | 28 | 25 | 59 |
36 | 56 | 63 | 79 | 26 | 72 | 38 | 34 | 27 | 68 |
17 | 25 | 16 | 21 | 18 | 40 | 19 | 21 | 16 | 28 |
20 | 29 | 23 | 30 | 19 | 44 | 22 | 23 | 17 | 34 |
26 | 33 | 34 | 38 | 24 | 49 | 28 | 24 | 23 | 40 |
24 | 32 | 37 | 43 | 21 | 47 | 27 | 22 | 20 | 41 |
27 | 41 | 44 | 56 | 22 | 56 | 30 | 28 | 22 | 51 |
34 | 42 | 55 | 61 | 27 | 57 | 37 | 26 | 28 | 53 |
37 | 52 | 62 | 74 | 29 | 67 | 40 | 32 | 29 | 63 |
44 | 55 | 71 | 78 | 34 | 71 | 46 | 33 | 35 | 67 |
Instance 3 (60 × 5)
Stage 1 | Stage 2 | Stage 3 | Stage 4 | Stage 5 | |||||
---|---|---|---|---|---|---|---|---|---|
Setup/processing | Setup/processing | Setup/processing | Setup/processing | Setup/processing | |||||
5 | 9 | 4 | 5 | 6 | 24 | 7 | 5 | 4 | 12 |
8 | 13 | 11 | 14 | 7 | 28 | 10 | 7 | 5 | 18 |
10 | 17 | 18 | 22 | 8 | 33 | 12 | 8 | 7 | 24 |
12 | 20 | 25 | 31 | 9 | 35 | 15 | 10 | 8 | 29 |
15 | 25 | 32 | 40 | 10 | 40 | 18 | 12 | 10 | 35 |
18 | 30 | 39 | 49 | 11 | 45 | 21 | 14 | 12 | 41 |
21 | 36 | 46 | 58 | 13 | 51 | 24 | 16 | 13 | 47 |
24 | 40 | 51 | 63 | 14 | 56 | 26 | 18 | 15 | 52 |
9 | 13 | 8 | 9 | 10 | 28 | 11 | 9 | 8 | 16 |
11 | 17 | 14 | 18 | 10 | 32 | 13 | 11 | 8 | 22 |
13 | 20 | 21 | 25 | 11 | 36 | 15 | 11 | 10 | 27 |
15 | 24 | 28 | 35 | 12 | 39 | 18 | 14 | 11 | 33 |
19 | 29 | 36 | 44 | 14 | 44 | 22 | 16 | 14 | 39 |
21 | 33 | 42 | 52 | 14 | 48 | 24 | 17 | 15 | 44 |
25 | 39 | 50 | 61 | 17 | 54 | 28 | 19 | 17 | 50 |
30 | 48 | 57 | 71 | 20 | 64 | 32 | 26 | 21 | 60 |
13 | 17 | 12 | 13 | 14 | 32 | 15 | 13 | 12 | 20 |
14 | 21 | 17 | 22 | 13 | 36 | 16 | 15 | 11 | 26 |
18 | 23 | 26 | 28 | 16 | 39 | 20 | 14 | 15 | 30 |
20 | 28 | 33 | 39 | 17 | 43 | 23 | 18 | 16 | 37 |
23 | 33 | 40 | 48 | 18 | 48 | 26 | 20 | 18 | 43 |
26 | 36 | 47 | 55 | 19 | 51 | 29 | 20 | 20 | 47 |
29 | 44 | 54 | 66 | 21 | 59 | 32 | 24 | 21 | 55 |
33 | 49 | 60 | 72 | 23 | 65 | 35 | 27 | 24 | 61 |
14 | 18 | 13 | 14 | 15 | 33 | 16 | 14 | 13 | 21 |
17 | 25 | 20 | 26 | 16 | 40 | 19 | 19 | 14 | 30 |
22 | 29 | 30 | 34 | 20 | 45 | 24 | 20 | 19 | 36 |
21 | 32 | 34 | 43 | 18 | 47 | 24 | 22 | 17 | 41 |
27 | 37 | 44 | 52 | 22 | 52 | 30 | 24 | 22 | 47 |
27 | 39 | 48 | 58 | 20 | 54 | 30 | 23 | 21 | 50 |
33 | 48 | 58 | 70 | 25 | 63 | 36 | 28 | 25 | 59 |
36 | 56 | 63 | 79 | 26 | 72 | 38 | 34 | 27 | 68 |
17 | 25 | 16 | 21 | 18 | 40 | 19 | 21 | 16 | 28 |
20 | 29 | 23 | 30 | 19 | 44 | 22 | 23 | 17 | 34 |
26 | 33 | 34 | 38 | 24 | 49 | 28 | 24 | 23 | 40 |
24 | 32 | 37 | 43 | 21 | 47 | 27 | 22 | 20 | 41 |
27 | 41 | 44 | 56 | 22 | 56 | 30 | 28 | 22 | 51 |
34 | 42 | 55 | 61 | 27 | 57 | 37 | 26 | 28 | 53 |
37 | 52 | 62 | 74 | 29 | 67 | 40 | 32 | 29 | 63 |
44 | 55 | 71 | 78 | 34 | 71 | 46 | 33 | 35 | 67 |
25 | 24 | 24 | 20 | 26 | 39 | 27 | 20 | 24 | 27 |
23 | 28 | 26 | 29 | 22 | 43 | 25 | 22 | 20 | 33 |
30 | 32 | 38 | 37 | 28 | 48 | 32 | 23 | 27 | 39 |
32 | 40 | 45 | 51 | 29 | 55 | 35 | 30 | 28 | 49 |
30 | 45 | 47 | 60 | 25 | 60 | 33 | 32 | 25 | 55 |
38 | 45 | 59 | 64 | 31 | 60 | 41 | 29 | 32 | 56 |
36 | 56 | 61 | 78 | 28 | 71 | 39 | 36 | 28 | 67 |
42 | 58 | 69 | 81 | 32 | 74 | 44 | 36 | 33 | 70 |
29 | 27 | 28 | 23 | 30 | 42 | 31 | 23 | 28 | 30 |
32 | 31 | 35 | 32 | 31 | 46 | 34 | 25 | 29 | 36 |
34 | 41 | 42 | 46 | 32 | 57 | 36 | 32 | 31 | 48 |
30 | 44 | 43 | 55 | 27 | 59 | 33 | 34 | 26 | 53 |
39 | 43 | 56 | 58 | 34 | 58 | 42 | 30 | 34 | 53 |
36 | 48 | 57 | 67 | 29 | 63 | 39 | 32 | 30 | 59 |
39 | 54 | 64 | 76 | 31 | 69 | 42 | 34 | 31 | 65 |
45 | 68 | 72 | 91 | 35 | 84 | 47 | 46 | 36 | 80 |
26 | 37 | 25 | 33 | 27 | 52 | 28 | 33 | 25 | 40 |
36 | 41 | 39 | 42 | 35 | 56 | 38 | 35 | 33 | 46 |
31 | 38 | 39 | 43 | 29 | 54 | 33 | 29 | 28 | 45 |
40 | 48 | 53 | 59 | 37 | 63 | 43 | 38 | 36 | 57 |
Rights and permissions
About this article
Cite this article
Li, X., Xiao, S., Wang, C. et al. Mathematical modeling and a discrete artificial bee colony algorithm for the welding shop scheduling problem. Memetic Comp. 11, 371–389 (2019). https://doi.org/10.1007/s12293-019-00283-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12293-019-00283-4