Skip to main content

Advertisement

SpringerLink
Log in

Particle swarm optimization-based algorithm for fuzzy parallel machine scheduling

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

This research extends the hybrid particle swarm optimization-based metaheuristic to solve the fuzzy parallel machine scheduling problems with bell-shaped fuzzy processing times. In this paper, we propose a discrete particle swarm optimization (DPSO) which comprises two components: a particle swarm optimization and genetic algorithm. In this paper, fuzzy arithmetic on bell-shaped fuzzy numbers is used to determine the completion time of jobs. We also use a defuzzification function to rank the fuzzy numbers. Under this ranking concept among fuzzy numbers, we plan to minimize the fuzzy makespan. An extensive numerical study on large-scale scheduling problems up to 100 jobs is conducted to assess the performance of the DPSO algorithm. The results show the proposed algorithm in comparison with lower bound to be very efficient for different structure instances.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Prade H (1979) Using fuzzy set theory in a scheduling problem: a case study. Fuzzy Sets Syst 2:153–165

    Article  MathSciNet  MATH  Google Scholar 

  2. Dumitru V, Luban F (1982) Membership functions, some mathematical programming models and production scheduling. Fuzzy Sets Syst 8:19–33

    Article  MathSciNet  MATH  Google Scholar 

  3. García-Villoria RP (2009) Introducing dynamic diversity into a discrete particle swarm optimization. Comput Oper Res 36(3):951–966

    Article  MATH  Google Scholar 

  4. Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceeding of the 1995 I.E. international conference on neural network, Perth, Australia, pp. 1942–1948

  5. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In Proceedings of the IEEE 1997 International Conference on Systems, Man and Cybernetics, pp. 4104–4109

  6. Mohan CK, Al-kazemi B (2001) Discrete particle swarm optimization. Proceedings of the Workshop on Particle Swarm Optimization. Indianapolis, IN: Purdue School of Engineering and Technology, IUPUI. pp. 22–29

  7. Laskari E.C., Parsopoulos, K.E., Vrahatis, M.N. (2002) Particle swarm optimization for integer programming. In: Proceedings of the IEEE 2002 Congress on Evolutionary Computation, Honolulu (HI), pp. 1582–1587

  8. Hu et al (2003) Swarm intelligence for permutation optimization: a case study of N-queens problem. Proceedings of the IEEE Swarm Intelligence Symposium, 243–246

  9. Yin P-Y (2004) A discrete particle swarm algorithm for optimal polygonal approximation of digital curves. J Vis Commun Image Represent 15(2):241–260

    Article  Google Scholar 

  10. Chen et al (2006) Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem. J Zhejiang Univ Sci A 7:607–614

    Article  MATH  Google Scholar 

  11. Sha DY, Hs C-Y (2006) A hybrid particle swarm optimization for job shop scheduling problem. Comput Ind Eng 51(4):791–808

    Article  Google Scholar 

  12. Xiong Y, Cheng H-Z, Yan J-y, Zhang L (2007) New discrete method for particle swarm optimization and its application in transmission network expansion planning. Electr Power Syst Res 77(3–4):227–233

    Google Scholar 

  13. Karthi R, Arumugam S, Ramesh Kumar K (2009) Discrete particle swarm optimization algorithm for data clustering nature inspired cooperative strategies for optimization, NICSO 2008, Volume 236

  14. Wang X, Tang L (2009) A tabu search heuristic for the hybrid flowshop scheduling with finite intermediate buffers. Comput Oper Res 36(3):907–918

    Article  MATH  Google Scholar 

  15. Król D, Drożdżowski M (2010) Use of MaSE methodology and swarm-based metaheuristics to solve the traveling salesman problem, J Intell Fuzzy Syst 21, in press

  16. Zhang Z, Jiang S, Zhang Y, Geng S, Wang H, Sang G (2014) An adaptive particle swarm optimization algorithm for reservoir operation optimization. Appl Soft Comput 18:167–177

    Article  Google Scholar 

  17. Shakibian H, Charkari NM (2014) n-cluster vector evaluated particle swarm optimization for distributed regression in WSNs. J Netw Comput Appl 42:80–91

    Article  Google Scholar 

  18. Wang C, Liu Y, Zhao Y, Chen Y (2014) A hybrid topology scale-free Gaussian-dynamic particle swarm optimization algorithm applied to real power loss minimization. Eng Appl Artif Intell 32:63–75

    Article  Google Scholar 

  19. Rada-Vilela J, Johnston M, Zhang M (2014) Population statistics for particle swarm optimization: Resampling methods in noisy optimization problems, Swarm and Evolutionary Computation, In Press

  20. Das G, Kumar Pattnaik P, Kumari Padhy S (2014) Artificial neural network trained by particle swarm optimization for non-linear channel equalization. Expert Syst Appl 41(7):3491–3496

    Article  Google Scholar 

  21. Zhang E, Wu Y, Chen Q (2014) A practical approach for solving multi-objective reliability redundancy allocation problems using extended bare-bones particle swarm optimization. Reliab Eng Syst Saf 127:65–76

    Article  Google Scholar 

  22. Wang S-C, Yeh M-F (2014) A modified particle swarm optimization for aggregate production planning. Expert Syst Appl 41(6):3069–3077

    Article  Google Scholar 

  23. Xue B, Zhang M, Browne WN (2014) Particle swarm optimisation for feature selection in classification: novel initialisation and updating mechanisms. Appl Soft Comput 18:261–276

    Article  Google Scholar 

  24. Sadeghi J, Sadeghi S, Taghi Akhavan Niaki S (2014) Optimizing a hybrid vendor-managed inventory and transportation problem with fuzzy demand: an improved particle swarm optimization algorithm. Inf Sci 272:126–144

    Article  Google Scholar 

  25. Liu R, Chen Y, Jiao L, Li Y (2014) A particle swarm optimization based simultaneous learning framework for clustering and classification. Pattern Recogn 47(6):2143–2152

    Article  Google Scholar 

  26. Du C-L, Luo C-X, Han Z-T, Zhu Y-S (2014) Applying particle swarm optimization algorithm to roundness error evaluation based on minimum zone circle. Measurement 52:12–21

    Article  Google Scholar 

  27. Elsayed SM, Sarker RA, Mezura-Montes E (2014) Self-adaptive mix of particle swarm methodologies for constrained optimization, Information Sciences, In Press

  28. Wang X, Ma L, Wang T (2014) An optimized nearest prototype classifier for power plant fault diagnosis using hybrid particle swarm optimization algorithm. Int J Electr Power Energy Syst 58:257–265

    Article  Google Scholar 

  29. Bagheri A, Mohammadi Peyhani H, Akbari M (2014) Financial forecasting using ANFIS networks with quantum-behaved particle swarm optimization, expert systems with applications, In Press

  30. Koulinas G, Kotsikas L, Anagnostopoulos K (2014) A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem, Information Sciences, In Press

  31. Li Y, Luh PB, Guan X (1994) Fuzzy Optimization-based scheduling of identical machines with possible breakdown, robotics and automation, Proceedings, IEEE International Conference, San Diego, CA, May 1994, vol.4, 3447–3452

  32. Hong TP, Yu KM, Huang CM (1998) LPT scheduling on fuzzy tasks with Triangular membership function, Second International Conference on Knowledge-Based Intelligent Elecwonic Systems, April 1998, Adelaide, Australia. Editors, L.C. Jain and R.K. Jab

  33. Peng J, Song K (2001) Expected value goal programming models for fuzzy scheduling problem. Proceedings of the Tenth IEEE International Conference on Fuzzy Systems, December, 2001. pp. 292–295, Melbourne, Australia

  34. Peng J, Liu B (2004) Parallel machine scheduling models with fuzzy processing times. Inf Sci 166(1–4):49–66

    Article  MathSciNet  MATH  Google Scholar 

  35. Anglani A, Grieco A, Guerriero E, Musmanno R (2005) Robust scheduling of parallel machines with sequence-dependent set-up costs. Eur J Oper Res 161:704–720

    Article  MathSciNet  MATH  Google Scholar 

  36. Petrovic D, Duenas A (2006) A fuzzy logic based production scheduling/rescheduling in the presence of uncertain disruptions. Fuzzy Sets Syst 157:2273–2285

    Article  MathSciNet  MATH  Google Scholar 

  37. Franke C, Hoffmann F, Lepping J, Schwiegelshohn U (2008) Development of scheduling strategies with genetic fuzzy systems. Appl Soft Comput 8:706–721

    Article  Google Scholar 

  38. Gharehgozli AH, Tavakkoli-Moghaddam R, Zaerpour N (2009) A fuzzy-mixed-integer goal programming model for a parallel-machine scheduling problem with sequence-dependent setup times and release dates. Robot Comput Integr Manuf 25:853–859

    Article  Google Scholar 

  39. Muralidhar A, Alwarsamy T (2009) Multi-objective optimization of parallel machine scheduling using fuzzy logic and simulated annealing. Int J Appl Eng Res 4:11

    Google Scholar 

  40. Chyu C-C, Chang W-S (2011) Optimizing fuzzy makespan and tardiness for unrelated parallel machine scheduling with archived metaheuristics. Int J Adv Manuf Technol 57(5–8):763–776

    Article  Google Scholar 

  41. Balin S (2011) Parallel machine scheduling with fuzzy processing times using a robust genetic algorithm and simulation. Inf Sci 181(17):3551–3569

    Article  Google Scholar 

  42. Balin S (2012) Non-identical parallel machine scheduling with fuzzy processing times using genetic algorithm and simulation. Int J Adv Manuf Technol 61(9–12):1115–1127

    Article  Google Scholar 

  43. Alcan P, Başlıgil H (2012) A genetic algorithm application using fuzzy processing times in non-identical parallel machine scheduling problem. Adv Eng Softw 45(1):272–280

    Article  Google Scholar 

  44. Torabi SA, Sahebjamnia N, Mansouri SA, Aramon Bajestani M (2013) A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem. Appl Soft Comput 13(12):4750–4762

    Article  Google Scholar 

  45. Bojadziev G, Bojadziev M (1996) Fuzzy sets, fuzzy logic, applications. World Scientific Pub Co Inc, Hackensack

    Book  Google Scholar 

  46. Yuan Q, Qian F, Du W (2010) A hybrid genetic algorithm with the Baldwin effect. Inf Sci 180(5):640–652

    Article  MathSciNet  MATH  Google Scholar 

  47. Low C, Yuling Y (2009) Genetic algorithm-based heuristics for an open shop scheduling problem with setup, processing, and removal times separated. Robot Comput Integr Manuf 2(25):314–322

    Article  Google Scholar 

  48. Kurz ME, Askin RG (2003) Comparing scheduling rules for flexible flow lines. Int J Prod Econ 85:371–388

    Article  Google Scholar 

  49. Tavakkoli-Moghaddam R, Azarkish M, Sadeghnejad-Barkousaraie A (2011) A new hybrid multi-objective Pareto archive PSO algorithm for a bi-objective job shop scheduling problem. Expert Syst Appl 38(9):10812–10821

    Article  Google Scholar 

  50. Sha DY, Lin H-H (2010) A multi-objective PSO for job-shop scheduling problems. Expert Syst Appl 37(2):1065–1070

    Article  Google Scholar 

  51. Lei D (2008) A Pareto archive particle swarm optimization for multi-objective job shop scheduling. Comput Ind Eng 54(4):960–971

    Article  Google Scholar 

  52. Zhang G, Shao X, Li P, Gao L (2009) An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Comput Ind Eng 56(4):1309–1318

    Article  Google Scholar 

  53. Lin T-L et al (2010) An efficient job-shop scheduling algorithm based on particle swarm optimization. Expert Syst Appl 37(3):2629–2636

    Article  Google Scholar 

  54. Liu B, Wang L, Jin Y-H (2008) An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers. Comput Oper Res 35(9):2791–2806

    Article  MATH  Google Scholar 

  55. Liou C-D, Liu C-H (2010) A novel encoding scheme of PSO for two-machine group scheduling. Adv Swarm Intell Lect Notes ComputSci 6145:128–134

    Article  Google Scholar 

  56. Tang J, Zhang G, Lin B, Zhang B (2010) A hybrid PSO/GA algorithm for job shop scheduling problem. Adv Swarm Intell Lect Notes Comput Sci 6145:566–573

    Article  Google Scholar 

  57. Tavakkoli-Moghaddam R, Azarkish M, Sadeghnejad A (2010) A new hybrid multi-objective Pareto archive PSO algorithm for a classic job shop scheduling problem with ready times. Adv Intell Comput Theor Appl Commun Comput Inf Sci 93:61–68

    Google Scholar 

  58. Tu K, Hao Z, Chen M (2006) PSO with improved strategy and topology for job shop scheduling. Adv Nat Comput Lect Notes Comput Sci 4222:146–155

    Article  Google Scholar 

  59. Tavakkoli-Moghaddam R, Azarkish M, Sadeghnejad-Barkousaraie A (2011) Solving a multi-objective job shop scheduling problem with sequence-dependent setup times by a Pareto archive PSO combined with genetic operators and VNS. Int J Adv Manuf Technol 53(5–8):733–750

    Article  Google Scholar 

  60. AitZai A, Benmedjdoub B, Boudhar M (2014) Branch-and-bound and PSO algorithms for no-wait job shop scheduling, Journal of Intelligent Manufacturing, in press

  61. Zhang G, Zuo X (2013) Deadline constrained task scheduling based on standard-PSO in a hybrid cloud. Adv Swarm Intell Lect Notes Comput Sci 7928:200–209

    Article  Google Scholar 

  62. Dousthaghi S, Tavakkoli-Moghaddam R, Makui A (2013) Solving the economic lot and delivery scheduling problem in a flexible job shop with unrelated parallel machines and a shelf life by a proposed hybrid PSO. Int J Adv Manuf Technol 68(5–8):1401–1416

    Article  Google Scholar 

  63. Niu Q, Zhou T, Wang L (2010) A hybrid particle swarm optimization for parallel machine total tardiness scheduling. Int J Adv Manuf Technol 49(5–8):723–739

    Article  Google Scholar 

  64. Sha DY, Lin HH (2009) A particle swarm optimization for multi-objective flowshop scheduling. Int J Adv Manuf Technol 45(7–8):749–758

    Article  Google Scholar 

  65. Chakaravarthy GV, Marimuthu S, Naveen Sait A (2013) Performance evaluation of proposed Differential Evolution and Particle Swarm Optimization algorithms for scheduling m-machine flow shops with lot streaming. J Intell Manuf 24(1):175–191

    Article  Google Scholar 

  66. Jamili A, Shafia MA, Tavakkoli-Moghaddam R (2011) A hybrid algorithm based on particle swarm optimization and simulated annealing for a periodic job shop scheduling problem. Int J Adv Manuf Technol 54(1–4):309–322

    Article  Google Scholar 

  67. Shiau D-F, Huang Y-M (2012) A hybrid two-phase encoding particle swarm optimization for total weighted completion time minimization in proportionate flexible flow shop scheduling. Int J Adv Manuf Technol 58(1–4):339–357

    Article  Google Scholar 

  68. Marinakis Y, Marinaki M (2013) Particle swarm optimization with expanding neighborhood topology for the permutation flowshop scheduling problem. Soft Comput 17(7):1159–1173

    Article  Google Scholar 

  69. Akhshabi M, Tavakkoli-Moghaddam R, Rahnamay-Roodposhti F (2014) A hybrid particle swarm optimization algorithm for a no-wait flow shop scheduling problem with the total flow time. Int J Adv Manuf Technol 70(5–8):1181–1188

    Article  Google Scholar 

  70. Wang X, Tang L (2010) An improved particle swarm optimization for permutation flowshop scheduling problem with total flowtime criterion. Adv Swarm Intell Lect Notes Comput Sci 6145:144–151

    Article  Google Scholar 

  71. Damodaran P, Gangadhara Rao A, Mestry S (2013) Particle swarm optimization for scheduling batch processing machines in a permutation flowshop. Int J Adv Manuf Technol 64(5–8):989–1000

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran

    J. Behnamian

Authors
  1. J. Behnamian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. Behnamian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Behnamian, J. Particle swarm optimization-based algorithm for fuzzy parallel machine scheduling. Int J Adv Manuf Technol 75, 883–895 (2014). https://doi.org/10.1007/s00170-014-6181-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-014-6181-0

Keywords

Search

Navigation