Skip to main content
SpringerLink
Log in

Effective Constructive Heuristic and Metaheuristic for the Distributed Assembly Blocking Flow-shop Scheduling Problem

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Scheduling in distributed production system has become an active research field in recent years. This paper investigates the distributed assembly blocking flow-shop scheduling problem (DABFSP), which consists of two stages: production and assembly. The first stage is processing jobs in several identical factories. Each factory has a series of machines no intermediate buffers existing between adjacent ones. The second stage assembles the processed jobs into the final products through a single machine. The objective is to minimize the maximum completion time or makespan of all products. To address this problem, a constructive heuristic is proposed based on a new assignment rule of jobs and a product-based insertion procedure. Afterwards, an iterated local search (ILS) is presented, which integrates an integrated encoding scheme, a multi-type perturbation procedure containing four kinds of perturbed operators based on problem-specific knowledge and a critical-job-based variable neighborhood search. Finally, a comprehensive computational experiment and comparisons with the closely related and well performing methods in the literature are carried out. The experimental and comparison results show that the proposed constructive heuristic and ILS can solve the DABFSP effectively and efficiently.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Cheng CY, Ying KC, Chen HH, Lu HS (2019) Minimising makespan in distributed mixed no-idle flowshops. Int J Prod Res 57(1):48–60

    Google Scholar 

  2. Naderi B, Ruiz R (2010) The distributed permutation flowshop scheduling problem. Comput Oper Res 37(4):754–768

    MathSciNet  MATH  Google Scholar 

  3. Ruiz R, Pan QK, Naderi B (2019) Iterated greedy methods for the distributed permutation flowshop scheduling problem. Omega 83:213–222

    Google Scholar 

  4. Shao W, Shao Z, Pi D (2020) Modeling and multi-neighborhood iterated greedy algorithm for distributed hybrid flow shop scheduling problem. Knowledge-Based Systems, pp 105527

  5. Zheng J, Wang L, Wang JJ (2020) A cooperative coevolution algorithm for multi-objective fuzzy distributed hybrid flow shop. Knowledge-Based Systems, pp 105536

  6. Lei D, Yuan Y, Cai J, Bai D (2020) An imperialist competitive algorithm with memory for distributed unrelated parallel machines scheduling. Int J Prod Res 58(2):597–614

    Google Scholar 

  7. Chang HC, Liu TK (2017) Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms. J Intell Manuf 28(8):1973–1986

    Google Scholar 

  8. Pan QK, Gao L, Xin-Yu L, Jose FM (2019) Effective constructive heuristics and meta-heuristics for the distributed assembly permutation flowshop scheduling problem. Appl Soft Comput 81(92):1054

    Google Scholar 

  9. Pan QK, Gao L, Wang L, Liang J, Li X (2019) Effective heuristics and metaheuristics to minimize total flowtime for the distributed permutation flowshop problem. Expert Syst Appl 124:309–324

    Google Scholar 

  10. Rifai AP, Nguyen HT, Dawal SZM (2016) Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling. Appl Soft Comput 40: 42–57

    Google Scholar 

  11. Shao W, Pi D, Shao Z (2017) Optimization of makespan for the distributed no-wait flow shop scheduling problem with iterated greedy algorithms. Knowl-Based Syst 137:163–181

    Google Scholar 

  12. Ying KC, Lin SW, Cheng CY, He CD (2017) Iterated reference greedy algorithm for solving distributed no-idle permutation flowshop scheduling problems. Comput Ind Eng 110:413– 423

    Google Scholar 

  13. Ying KC, Lin SW (2018) Minimizing makespan for the distributed hybrid flowshop scheduling problem with multiprocessor tasks. Expert Syst Appl 92:132–141

    Google Scholar 

  14. Shao Z, Pi D, Shao W (2020) Hybrid enhanced discrete fruit fly optimization algorithm for scheduling blocking flow-shop in distributed environment. Expert Syst Appl 113(147):145

    Google Scholar 

  15. Hatami S, Ruiz R, AndrésRomano C (2013) The distributed assembly permutation flowshop scheduling problem. Int J Prod Res 51(17):5292–5308

    Google Scholar 

  16. Wang SY, Wang L (2016) An estimation of distribution algorithm-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem. IEEE Trans Syst Man Cybern Syst 46(1):139–149

    Google Scholar 

  17. Lin J, Zhang S (2016) An effective hybrid biogeography-based optimization algorithm for the distributed assembly permutation flow-shop scheduling problem. Comput Indu Eng 97:128– 136

    Google Scholar 

  18. Lin J, Wang ZJ, Li X (2017) A backtracking search hyper-heuristic for the distributed assembly flow-shop scheduling problem. Swarm Evol Comput 36:124–135

    Google Scholar 

  19. Li X, Zhang X, Yin M, Wang J (2015) A genetic algorithm for the distributed assembly permutation flowshop scheduling problem. In: 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 3096–3101

  20. Sang HY, Pan QK, Li JQ, Wang P, Han YY, Gao KZ, Duan P (2019) Effective invasive weed optimization algorithms for distributed assembly permutation flowshop problem with total flowtime criterion. Swarm Evol Comput 44:64–73

    Google Scholar 

  21. Yang Y, Li P, Wang S, Liu B, Luo Y (2017) Scatter search for distributed assembly flowshop scheduling to minimize total tardiness. In: 2017 IEEE Congress on evolutionary computation (CEC). IEEE. ISBN 1509046011, pp 861–868

  22. Hatami S, Ruiz R, Andrés-Romano C (2015) Heuristics and metaheuristics for the distributed assembly permutation flowshop scheduling problem with sequence dependent setup times. Int J Prod Econ 169:76–88

    Google Scholar 

  23. Gonzalez-Neira EM, Ferone D, Hatami S, Juan AA (2017) A biased-randomized simheuristic for the distributed assembly permutation flowshop problem with stochastic processing times. Simul Model Pract Theory 79:23–36

    Google Scholar 

  24. Shao W, Pi D, Shao Z (2019) Local search methods for a distributed assembly no-idle flow shop scheduling problem. IEEE Syst J 2(13):1945–1956

    Google Scholar 

  25. Yang Z, Liu C (2018) A hybrid multi-objective gray wolf optimization algorithm for a fuzzy blocking flow shop scheduling problem. Advances in Mechanical Engineering 10(3)

  26. Ribas I, Companys R, Tort-Martorell X (2017) Efficient heuristics for the parallel blocking flow shop scheduling problem. Expert Syst Appl 74(5):41–54

    Google Scholar 

  27. Framinan JM, Leisten R, Ruiz García R (2014) Overview of scheduling methods. Springer, London, pp 153–190

    Google Scholar 

  28. Pinedo M (2015) Scheduling Theory Algorithms, and Systems, 4th Edn. Springer, Berlin

  29. Nawaz M, Enscore EE, Ham I (1983) A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega 11(1):91–95

    Google Scholar 

  30. Framinan JM, Leisten R (2003) An efficient constructive heuristic for flowtime minimisation in permutation flow shops. Omega 31(4):311–317

    Google Scholar 

  31. Ronconi DP (2004) A note on constructive heuristics for the flowshop problem with blocking. Int J Prod Econ 87(1):39– 48

    Google Scholar 

  32. Pan QK, Wang L (2012) Effective heuristics for the blocking flowshop scheduling problem with makespan minimization. Omega 40(2):218–229

    Google Scholar 

  33. Wang L, Pan QK, Suganthan PN, Wang WH, Wang YM (2010) A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems. Comput Oper Res 37(3):509–520

    MathSciNet  MATH  Google Scholar 

  34. Lourenço HR, Martin OC, Stützle T (2010) Iterated local search: Framework and applications. Springer, Boston, pp 363–397

    Google Scholar 

  35. Guan J, Lin G, Feng HB (2018) A multi-start iterated local search algorithm for the uncapacitated single allocation hub location problem. Appl Soft Comput 73:230–241

    Google Scholar 

  36. Pan QK, Gao L, Li X, Gao KZ (2017) Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times. Appl Math Comput 303:89–112

    MathSciNet  MATH  Google Scholar 

  37. Schulz S, Neufeld JS, Buscher U (2019) A multi-objective iterated local search algorithm for comprehensive energy-aware hybrid flow shop scheduling. J Clean Prod 224:421–434

    Google Scholar 

  38. Zohali H, Naderi B, Mohammadi M, Roshanaei V (2019) Reformulation, linearization, and a hybrid iterated local search algorithm for economic lot-sizing and sequencing in hybrid flow shop problems. Comput Oper Res 104:127–138

    MathSciNet  MATH  Google Scholar 

  39. Sabar NR, Kendall G (2015) An iterated local search with multiple perturbation operators and time varying perturbation strength for the aircraft landing problem. Omega 56:88–98

    Google Scholar 

  40. Ruiz R, Stützle T (2007) A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. Eur J Oper Res 177(3):2033–2049

  41. Pan QK, Ruiz R (2014) An effective iterated greedy algorithm for the mixed no-idle permutation flowshop scheduling problem. Omega 44(2):41–50

    Google Scholar 

  42. Montgomery DC (2008) Design and analysis of experiments. Wiley, New York. ISBN 0470128666

  43. Shao Z, Pi D, Shao W (2019) A novel multi-objective discrete water wave optimization for solving multi-objective blocking flow-shop scheduling problem. Knowl-Based Syst 165:110–131

  44. Zhao F, Liu H, Zhang Y, Ma W, Zhang C (2018) A discrete water wave optimization algorithm for no-wait flow shop scheduling problem. Expert Syst Appl 91:347–363

    Google Scholar 

  45. Zhao F, Zhang L, Zhang Y, Ma W, Zhang C, Song H (2020) A hybrid discrete water wave optimization algorithm for the no-idle flowshop scheduling problem with total tardiness criterion. Expert Syst Appl 146:113166

    Google Scholar 

  46. Naderi B, Ruiz R (2014) A scatter search algorithm for the distributed permutation flowshop scheduling problem. Eur J Oper Res 239(2):323–334

  47. Yepes-Borrero JC, Villa F, Perea F, Caballero-Villalobos JP (2020) Grasp algorithm for the unrelated parallel machine scheduling problem with setup times and additional resources. Expert Syst Appl 141:112959

    Google Scholar 

  48. Li K, Chen J, Fu H, Jia Z, Fu W (2019) Uniform parallel machine scheduling with fuzzy processing times under resource consumption constraint. Appl Soft Comput 82:105585

    Google Scholar 

  49. Allahverdi A, Aydilek H, Aydilek A (2020) No-wait flowshop scheduling problem with separate setup times to minimize total tardiness subject to makespan. Appl Math Comput 365:124688

    MathSciNet  MATH  Google Scholar 

  50. Cai S, Yang K, Liu K (2018) Multi-objective optimization of the distributed permutation flow shop scheduling problem with transportation and eligibility constraints. J Oper Res Soc China 6(3):391–416

    MathSciNet  MATH  Google Scholar 

  51. Fu Y, Tian G, Fathollahi-Fard AM, Ahmadi A, Zhang C (2019) Stochastic multi-objective modelling and optimization of an energy-conscious distributed permutation flow shop scheduling problem with the total tardiness constraint. J Clean Prod 226:515–525

    Google Scholar 

Download references

Acknowledgments

This research is supported by the Natural Science Basic Research Program of Shaanxi (Program No. 2020JQ-425), the Fundamental Research Funds for the Central Universities (No. GK202003073), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 19KJB520042) and the Research Startup Fund of Shaanxi Normal University.

Author information

Authors and Affiliations

  1. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Zhongshi Shao

  2. School of Computer Science and Technology, Nanjing Normal University, Nanjing, 210023, China

    Weishi Shao

  3. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Dechang Pi

  4. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China

    Dechang Pi

Authors
  1. Zhongshi Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weishi Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dechang Pi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weishi Shao.

Additional information

Publisher’s note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

(RAR 39.8 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shao, Z., Shao, W. & Pi, D. Effective Constructive Heuristic and Metaheuristic for the Distributed Assembly Blocking Flow-shop Scheduling Problem. Appl Intell 50, 4647–4669 (2020). https://doi.org/10.1007/s10489-020-01809-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-020-01809-x

Keywords

Search

Navigation