Lunar cycle inspired PSO for single machine total weighted tardiness scheduling problem

Evolutionary Intelligence (2021)Cite this article

Abstract

In the large scale Single machine total weighted tardiness scheduling problem (SMTWTSP) is a group of not-interrelated tasks having different parameters to be executed on one machine. The problem’s objective is to identify the minimum total weighted tardiness using a newly developed variant of the particle swarm optimization (PSO) algorithm. In the past, the PSO algorithm has proved itself as an efficient swarm intelligence based strategy to solve complex combinatorial problems. Here, in this article, the lunar cycle inspired local search technique is assimilated into PSO, and the designed PSO variant is termed as lunar cycle inspired PSO (LCPSO). The performance of the designed LCPSO is tested over 25 large SMTWTSP instances of job size 1000. The reported results show that the designed LCPSO is a competitive PSO variant that can be applied to provide an effective solution for the SMTWTSP type combinatorial optimization problem.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

US$ 39.95

Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. 1.

    Jain S, Kumar S, Sharma VK, Poonia RC, Lamba NP (2020) Modified differential evolution algorithm for solving minimum spanning tree. J Inf Optim Sci 41(2):633–639

    MathSciNet  Google Scholar 

  2. 2.

    Pandey S, Kumar S (2013) Enhanced artificial bee colony algorithm and its application to travelling salesman problem. Int J Technol Innov Res 2:1–10

    Google Scholar 

  3. 3.

    Goyal A, Sharma VK, Kumar S, Kumar K (2020) Modified local link failure recovery multicast routing protocol for MANET. J Inf Optim Sci 41(2):669–677

    Google Scholar 

  4. 4.

    Jain S, Kumar S, Sharma VK, Poonia RC (2020) Peregrine preying pattern based differential evolution for robot path planning. J Interdiscip Math 23(2):555–562

    Article  Google Scholar 

  5. 5.

    Jain S, Sharma VK, Kumar S (2020) Robot path planning using differential evolution. In: Sharma H, Govindan K, Poonia RC, Kumar S, Wael M (eds) Advances in computing and intelligent systems. Springer, Berlin, pp 531–537

    Google Scholar 

  6. 6.

    Nayyar A, Nguyen NG, Kumari R, Kumar S (2020) Robot path planning using modified artificial bee colony algorithm. In: Frontiers in intelligent computing: theory and applications. Springer, Berlin, pp 25–36

  7. 7.

    Kumar S, Jadon P (2014) A novel hybrid algorithm for permutation flow shop scheduling. Int J Comput Sci Inf Technol 5(4):5057–5061

    Google Scholar 

  8. 8.

    Kumari R, Sharma VK, Kumar S (2014) Fuzzified job shop scheduling algorithm. Int J Technol Innov Res 7:1–20

    Google Scholar 

  9. 9.

    Manju SS, Kumar S, Nayyar A, Al-Turjman F, Mostarda L et al (2020) Proficient QoS-based target coverage problem in wireless sensor networks. IEEE Access 8:74315–74325

    Article  Google Scholar 

  10. 10.

    Lenstra JK, Kan AR, Brucker P (1977) Complexity of machine scheduling problems. Ann Discret Math 1:343–362

    MathSciNet  Article  Google Scholar 

  11. 11.

    Schrage L, Baker KR (1978) Dynamic programming solution of sequencing problems with precedence constraints. Oper Res 26(3):444–449

    Article  Google Scholar 

  12. 12.

    Jouglet A, Baptiste P, Carlier J (2002) Exact procedures for single machine total cost scheduling. In: 2002 IEEE international conference on systems, man and cybernetics. vol 6. IEEE, pp 4

  13. 13.

    Potts CN, Van Wassenhove LN (1985) A branch and bound algorithm for the total weighted tardiness problem. Oper Res 33(2):363–377

    Article  Google Scholar 

  14. 14.

    Tanaka S, Fujikuma S, Araki M (2009) An exact algorithm for single-machine scheduling without machine idle time. J Sched 12(6):575–593

    MathSciNet  Article  Google Scholar 

  15. 15.

    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, 1995. Proceedings, vol 4. IEEE, pp 1942–1948

  16. 16.

    Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes Univ Press, Erciyes

  17. 17.

    Passino KM (2010) Bacterial foraging optimization. Int J Swarm Intell Res IJSIR 1(1):1–16

    MathSciNet  Article  Google Scholar 

  18. 18.

    Sharma A, Chaturvedi R, Dwivedi U, Kumar S, Reddy S (2018) Firefly algorithm based effective gray scale image segmentation using multilevel thresholding and entropy function. Int J Pure Appl Math 118(5):437–443

    Google Scholar 

  19. 19.

    Yang XS et al (2008) Firefly algorithm. Nat Inspir Metaheur Algorithms 20:79–90

    Google Scholar 

  20. 20.

    Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: 2009, World congress on nature and biologically inspired computing (NaBIC). IEEE, pp 210–214

  21. 21.

    Kumar S, Kumari R (2018) Artificial bee colony, firefly swarm optimization, and bat algorithms. In: Advances in swarm intelligence for optimizing problems in computer science. pp 145–182

  22. 22.

    Yang XS (2011) Bat algorithm for multi-objective optimisation. Int J Bio Inspir Comput 3(5):267–274

    Article  Google Scholar 

  23. 23.

    Bhambu P, Kumar S (2016) Levy flight based animal migration optimization algorithm. In: 2016 international conference on recent advances and innovations in engineering (ICRAIE). IEEE, pp 1–5

  24. 24.

    Li X, Zhang J, Yin M (2014) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24(7–8):1867–1877

    Article  Google Scholar 

  25. 25.

    Bansal JC, Sharma H, Jadon SS, Clerc M (2014) Spider monkey optimization algorithm for numerical optimization. Memet Comput 6(1):31–47

    Article  Google Scholar 

  26. 26.

    Kumar S, Kumari R, Sharma VK (2015) Fitness based position update in spider monney optimization algorithm. Procedia Comput Sci 62:442–449

    Article  Google Scholar 

  27. 27.

    Sharma H, Bansal JC, Arya K (2013) Diversity measures in artificial bee colony. In: Proceedings of seventh international conference on bio-inspired computing: theories and applications (BIC-TA 2012). Springer, Berlin, pp 299–314

  28. 28.

    Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-wesley, New York

    Google Scholar 

  29. 29.

    Kumar S, Jain S, Sharma H (2018) Genetic algorithms. In: Advances in swarm intelligence for optimizing problems in computer science. pp 27–52

  30. 30.

    Sharma P, Sharma H, Kumar S, Bansal JC (2019) A review on scale factor strategies in differential evolution algorithm. In: Bansal JC, Das KN, Nagar A, Deep K, Ojha AK (eds) Soft computing for problem solving. Springer, Berlin, pp 925–943

    Google Scholar 

  31. 31.

    Sharma P, Sharma H, Kumar S, Sharma K (2019) Black-hole gbest differential evolution algorithm for solving robot path planning problem. In: Dhiman G, Kumar V (eds) Harmony search and nature inspired optimization algorithms. Springer, Berlin, pp 1009–1022

    Google Scholar 

  32. 32.

    Storn R, Price K (1995) Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. International Computer Science Institute, New York

    Google Scholar 

  33. 33.

    Rana PS, Sharma H, Bhattacharya M, Shukla A (2012) Guided reproduction in differential evolution. In: Asia-pacific conference on simulated evolution and learning. Springer, Berlin, pp 117–127

  34. 34.

    Tasgetiren MF, Sevkli M, Liang YC, Gencyilmaz G (2004) Particle swarm optimization algorithm for single machine total weighted tardiness problem. In: Evolutionary computation, 2004. CEC2004. Congress on. vol 2. IEEE, pp 1412–1419

  35. 35.

    Cheng TE, Ng C, Yuan J, Liu Z (2005) Single machine scheduling to minimize total weighted tardiness. Eur J Oper Res 165(2):423–443

    MathSciNet  Article  Google Scholar 

  36. 36.

    Kolliopoulos SG, Steiner G (2006) Approximation algorithms for minimizing the total weighted tardiness on a single machine. Theoret Comput Sci 355(3):261–273

    MathSciNet  Article  Google Scholar 

  37. 37.

    Ferrolho A, Crisóstomo M (2007) Single machine total weighted tardiness problem with genetic algorithms. In: Computer systems and applications, 2007. AICCSA’07. IEEE/ACS international conference on. IEEE, pp 1–8

  38. 38.

    Tasgetiren MF, Pan QK, Liang YC (2009) A discrete differential evolution algorithm for the single machine total weighted tardiness problem with sequence dependent setup times. Comput Oper Res 36(6):1900–1915

    Article  Google Scholar 

  39. 39.

    Ahmadizar F, Hosseini L (2011) A novel ant colony algorithm for the single-machine total weighted tardiness problem with sequence dependent setup times. Int J Comput Intell Syst 4(4):456–466

    Article  Google Scholar 

  40. 40.

    Yin Y, Wu CC, Wu WH, Cheng SR (2012) The single-machine total weighted tardiness scheduling problem with position-based learning effects. Comput Oper Res 39(5):1109–1116

    MathSciNet  Article  Google Scholar 

  41. 41.

    Santosa B, Safitri AL (2015) Biogeography-based optimization (BBO) algorithm for single machine total weighted tardiness problem (SMTWTP). Procedia Manuf 4:552–557

    Article  Google Scholar 

  42. 42.

    Marichelvam M, Geetha M (2016) A hybrid cuckoo search metaheuristic algorithm for solving single machine total weighted tardiness scheduling problems with sequence dependent setup times. Int J Comput Complex Intell Algorithms 1(1):23–34

    Google Scholar 

  43. 43.

    Ding J, Lü Z, Cheng T, Xu L (2017) A hybrid evolutionary approach for the single-machine total weighted tardiness problem. Comput Ind Eng 108:70–80

    Article  Google Scholar 

  44. 44.

    Petrynski K, Szost R, Pozniak-Koszalka I, Koszalka L, Kasprzak A (2018) Single machine weighted tardiness problem: an algorithm and experimentation system. In: International conference on computational collective intelligence. Springer, Berlin, pp 36–44

  45. 45.

    Chaabane L (2019) A novel hybrid algorithm for minimizing total weighted tardiness cost. Comput Sci J Moldova 80(2):230–241

    MathSciNet  MATH  Google Scholar 

  46. 46.

    Lamiche C, Bouchra D (2019) An efficient system for minimizing total weighted tardiness cost on single machine. In: 2019 International conference on digitization (ICD). IEEE, pp 57–59

  47. 47.

    Poongothai V, Godhandaraman P, Jenifer AA (2019) Single machine scheduling problem for minimizing total tardiness of a weighted jobs in a batch delivery system, stochastic rework and reprocessing times. In: AIP conference proceedings, vol 2112. AIP Publishing LLC, pp 020132

  48. 48.

    Harrath Y, Mahjoub A, Kaabi J (2019) A multi-objective genetic algorithm to solve a single machine scheduling problem with setup-times. Int J Serv Oper Manag 33(4):494–511

    Google Scholar 

  49. 49.

    Romanuke VV (2019) Accurate total weighted tardiness minimization in tight-tardy progressive single machine scheduling with preemptions by no idle periods. KPI Sci News 5–6:26–42

    Article  Google Scholar 

  50. 50.

    Abitz D, Hartmann T, Middendorf M (2020) A weighted population update rule for PACO applied to the single machine total weighted tardiness problem. arXiv:200408433

  51. 51.

    Jun S, Lee S (2020) Learning dispatching rules for single machine scheduling with dynamic arrivals based on decision trees and feature construction. Int J Product Res 1–19. https://doi.org/10.1080/00207543.2020.1741716

  52. 52.

    Queiroga E, Pinheiro RG, Christ Q, Subramanian A, Pessoa AA (2020) Iterated local search for single machine total weighted tardiness batch scheduling. J Heuris 1–86. https://doi.org/10.1007/s10732-020-09461-x

  53. 53.

    Joshi SK, Bansal JC (2020) Parameter tuning for meta-heuristics. Knowl Based Syst 189:105094

    Article  Google Scholar 

  54. 54.

    Gopal A, Sultani MM, Bansal JC (2020) On stability analysis of particle swarm optimization algorithm. Arab J Sci Eng 45:2385–2394. https://doi.org/10.1007/s13369-019-03991-8

    Article  Google Scholar 

  55. 55.

    Lalwani S, Sharma H, Satapathy SC, Deep K, Bansal JC (2019) A survey on parallel particle swarm optimization algorithms. Arab J Sci Eng 44(4):2899–2923

    Article  Google Scholar 

  56. 56.

    Bhambu P, Kumar S, Sharma K (2018) Self balanced particle swarm optimization. Int J Syst Assur Eng Manag 9(4):774–783

    Article  Google Scholar 

  57. 57.

    Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: The 1998 IEEE international conference on evolutionary computation proceedings, 1998. IEEE world congress on computational intelligence. IEEE, pp 69–73

  58. 58.

    Çomak E (2019) A particle swarm optimizer with modified velocity update and adaptive diversity regulation. Expert Syst 36(1):e12330

    Article  Google Scholar 

  59. 59.

    Wang C, Song W (2019) A modified particle swarm optimization algorithm based on velocity updating mechanism. Ain Shams Eng J 10(4):847–866

    MathSciNet  Article  Google Scholar 

  60. 60.

    Rathore A, Sharma H (2017) Review on inertia weight strategies for particle swarm optimization. In: Proceedings of sixth international conference on soft computing for problem solving. Springer, Berlin, pp 76–86

  61. 61.

    Grubelnik V, Marhl M, Repnik R (2018) Determination of the size and depth of craters on the moon. Center Educ Policy Stud J 8(1):35–53

    Article  Google Scholar 

  62. 62.

    Smith R (2015) Moon phases explained. https://www.absolutesoulsecrets.com/supermoon/moon-phases-explained/. Retrieved 10 May 2020

  63. 63.

    Gwern (2013) Lunar circadian rhythms. https://www.gwern.net/Lunar-sleep. Retrieved 10 May 2020

  64. 64.

    Sharma N, Sharma H, Sharma A (2019) An effective solution for large scale single machine total weighted tardiness problem using lunar cycle inspired artificial bee colony algorithm. IEEE/ACM Trans Comput Biol Bioinf 17(5):1573–1581. https://doi.org/10.1109/TCBB.2019.2897302

    Article  Google Scholar 

Download references

Author information

Affiliations

  1. Career Point University, Kota, India

    Shruti Gupta

  2. JECRC University, Jaipur, India

    Rajani Kumari

  3. Guru Jambheshwar University of Science and Technology, Hisar, 125001, India

    Rishi Pal Singh

Authors
  1. Shruti Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajani Kumari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rishi Pal Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rajani Kumari.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gupta, S., Kumari, R. & Singh, R.P. Lunar cycle inspired PSO for single machine total weighted tardiness scheduling problem. Evol. Intel. (2021). https://doi.org/10.1007/s12065-020-00556-9

Download citation

Keywords

  • Scheduling problem
  • Lunar cycle
  • Combinatorial optimization
  • Particle swarm optimization