Skip to main content

Advertisement

SpringerLink
Log in

Scheduling two-stage assembly flow shop with random machines breakdowns: integrated new self-adapted differential evolutionary and simulation approach

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

This paper takes random machines breakdowns and the two-stage assembly flow shop problem into consideration as a realistic assumption in industrial environments. In practical manufacturing environment, disruptions and unforeseen incidents occur, so a schedule being built based on deterministic information is not practical and may lead to poor performance. In this paper, machines in manufacturing and assembly stages are not always available due to random machines breakdowns which occur during processing of each operation. The goal is to minimize the expected the weighted sum of makespan and mean of completion time. Owning to its problem complexity and since the problem belongs to NP-hard class, use of meta-heuristic algorithms is justified to tackle the potential complexity of the problem considered, and hence, we proposed four meta-heuristics algorithms entitled: genetic algorithm, imperialist competitive algorithm, cloud theory-based simulated annealing and new self-adapted differential evolutionary (NSDE) to solve it. Machine breakdown and dynamic nature of the problem, the structural complexity increases markedly. In this regard, to overcome this form of complexity, simulation techniques are typically employed. Eventually, since the proposed problem has both types of complexities (algorithm complexity and structural complexity), simulation is integrated into the proposed meta-heuristic approaches to handle the complexities. We apply artificial neural network as a tuning tool for predicting the input parameters of each proposed meta-heuristics algorithms in uncertain condition. Also, we suggest Taguchi method as one the most important adjusting approaches for analyzing the effect of input parameters in each algorithm. The computational results show which proposed NSDE statistically is better than other proposed meta-heuristics algorithms according two important indicators: quality of solution and computational time.

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29

Similar content being viewed by others

Abbreviations

AFSP:

Assembly flow shop problem

TAFSP:

Two-stage assembly flow shop problem

HFSP:

Hybrid flow shop scheduling problem

MODPFSP:

Multi-objective distributed permutation flow shop scheduling problem

GA:

Genetic algorithm

ICA:

Imperialist competitive algorithm

CSA:

Cloud theory-based simulated annealing

DE:

Differential evolutionary

SDE:

Self-adapted differential evolutionary

NSDE:

New self-adapted differential evolutionary

SA:

Simulated annealing

TS:

Tabu search

ACO:

Ant colony optimization

DCOA:

Cuckoo optimization algorithm

BS-HH:

Backtracking search hyper-heuristic

HICA:

Hybrid imperialist competitive algorithm

NSG:

Neighborhood search with global exchange

LLHs:

Low-level heuristics

LB:

Lower bound

ANN:

Artificial Neural network

Ag:

Breakdown level of the shop floor

MTBF:

Mean time between failure

MTTR:

Mean time to repair

MOPT:

Mean operation processing time

OPTV:

Operation processing time variance

FEL:

Future events list

Exp:

Exponential distribution

RPD:

The relative percent deviation

ANOVA:

Analysis of variance

S/N :

Signal-to-noise

LSD:

Least significance difference

References

  • Abedi M, Seidgar H, Fazlollahtabar H, Bijani R (2015) Bi-objective optimization for scheduling the identical parallel batch-processing machines with arbitrary job sizes, unequal job release times and capacity limits. Int J Prod Res 53(6):1680–1711

    Google Scholar 

  • Al-Anzi FS, Allahverdi A (2004) Hybrid simulated annealing heuristic for multimedia object requests scheduling problem. Int J Comput Appl 26(4):207–211

    Google Scholar 

  • Al-Anzi FS, Allahverdi A (2007) A self-adaptive differential evolution heuristic for two- stage assembly scheduling problem to minimize maximum lateness with setup times. Eur J Oper Res 182:80–94

    MathSciNet  MATH  Google Scholar 

  • AL-Hinai N, Elmekkawy TY (2011) Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm. Int J Prod Econ 132:279–291

    Google Scholar 

  • Allahverdi A (1995) Two-stage production scheduling with separated set-up times and stochastic breakdown. J Oper Res Soc 46:896–904

    MATH  Google Scholar 

  • Allahverdi A (1996) Two-machine proportionate flow shop scheduling with breakdowns to minimize maximum lateness. Comput Oper Res 23:909–916

    MATH  Google Scholar 

  • Allahverdi A, Al-Anzi FS (2006) Evolutionary heuristics and an algorithm for the two-stage assembly scheduling problem to minimise makespan with setup times. Int J Prod Res 44(22):4713–4735

    MATH  Google Scholar 

  • Allahverdi A, Al-Anzi FS (2007) The two-stage assembly flowshop scheduling problem with bicriteria of makespan and mean completion time. Int J Adv Manuf Technol 37(1):166–177

    Google Scholar 

  • Allahverdi A, Al-Anzi FS (2009) The two-stage assembly flowshop scheduling problem to minimise total completion time with setup times. Comput Oper Res 36(10):2740–2747

    MathSciNet  MATH  Google Scholar 

  • Allahverdi A, Mittenthal J (1998) Dual criteria scheduling on a two-machine flow shop subject to random breakdowns. Int Transl Oper Res 5:317–324

    Google Scholar 

  • Allaoui H, Artiba A (2004) Integrating simulation and optimization to schedule a hybrid flow shop with maintenance constraints. Comput Ind Eng 47(4):431–450

    Google Scholar 

  • Atashpaz-Gargari E, Lucas C (2008) Imperialist competitive algorithm: an algorithm for optimisation inspired by imperialistic competition. IEEE Congr Evol Comput (CEC 2007) 4425083:4661–4667

    Google Scholar 

  • Behnamian J, Fatemi Ghomi SMT, Zandieh M (2010) Development of a hybrid metaheuristic to minimise earliness and tardiness in a hybrid flowshop with sequence-dependent setup times. Int J Prod Res 48(5):1415–1438

    MATH  Google Scholar 

  • Berrichi A, Amodeo L, Yalaoui F, Chatelet E, Mezghiche M (2008) Bi-objective optimization algorithms for joint production and maintenance scheduling: application to the parallel machine problem’. J Intell Manuf 20:389–400

    Google Scholar 

  • Bose NK, Liang P (1996) Neural network fundamentals with graph, algorithms, and application. Tata McGraw Hill, New Delhi

    Google Scholar 

  • Cowling PI, Johansson M (2002) Using real-time information for effective dynamic scheduling. Eur J Oper Res 139(2):230–244

    MathSciNet  MATH  Google Scholar 

  • Damak N, Jarboui B, Siarry P, Loukil T (2009) Differential evolution for solving multi-mode resource-constrained project scheduling problems. Comput Oper Res 36:2653–2659

    MathSciNet  MATH  Google Scholar 

  • Deng J, Wang L (2017) A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem. Swarm Evol Comput 32:121–131

    Google Scholar 

  • Deyi L, Yi D (2005) Artificial intelligence with uncertainty. Chapman and Hall, London

    MATH  Google Scholar 

  • Di K, Deyi L, Deren L (1999) Cloud theory and its applications in spatial data mining knowledge discovery. J Image Gr 4(11):930–935

    Google Scholar 

  • Dong Y-H, Jang J (2012) Production rescheduling for machine breakdown at a job shop. Int J Prod Res 50(10):2681–2691

    Google Scholar 

  • Framinan JM, Gonzalez PP (2017) The 2-stage assembly flowshop scheduling problem with total completion time: Efficient constructive heuristic and metaheuristic. Comput Oper Res 88:237–246

    MathSciNet  MATH  Google Scholar 

  • Gholami M, Zandieh M, Alem-Tabriz A (2009a) Scheduling hybrid flow shop with sequence-dependent setup times and machines with random breakdowns. Int J Manuf Technol 42:189–201

    MATH  Google Scholar 

  • Gholami M, Zandieh M, Alem-Tabriz A (2009b) Scheduling hybrid flow shop with sequence-dependent setup times and machines with random breakdowns. Int J Manuf Technol 42:189–201

    MATH  Google Scholar 

  • Goldberg DE (1989) Genetic algorithms: Search, optimization & machine learning, vol 541. Addison-Wesley Inc, Boston

    MATH  Google Scholar 

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

    Google Scholar 

  • Gu J, Gu M, Cao C, Gu X (2010) A novel competitive co-evolutionary quantum genetic algorithm for stochastic job shop scheduling problem. Comput Oper Res 37:927–937

    MathSciNet  MATH  Google Scholar 

  • Haouari M, Daouas T (1999) Optimal scheduling of the 3-machine assembly-type flow shop. RAIRO Oper Res 33(4):439–445

    MathSciNet  MATH  Google Scholar 

  • Hariri AMA, Potts CN (1997) A branch and bound algorithm for the two-stage assembly scheduling problem. Eur J Oper Res 103:547–556

    MATH  Google Scholar 

  • Holthaus O (1999) Scheduling in job shops with machine breakdowns: an experimental study. Comput Ind Eng 36:137–162

    Google Scholar 

  • Kasap N, Aytug H, Paul A (2006) Minimizing makespan on a single machine subject to random breakdowns. Oper Res Lett 34:29–36

    MathSciNet  MATH  Google Scholar 

  • Kazemi H, Mazdeh Mahdavi M, Rostami M (2017) The two stage assembly flow-shop scheduling problem with batching and delivery. Eng Appl Artif Intell 63:98–107

    Google Scholar 

  • Khorshidian H, Javadian N, Zandieh M, Rezaeian J, Rahmani K (2011) A genetic algorithm for JIT single machine scheduling with preemption and machine idle time. Expert Syst Appl 38:7911–7918

    Google Scholar 

  • Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680

    MathSciNet  MATH  Google Scholar 

  • Komaki GM, Teymourian E, Kayvanfar V, Booyavi Z (2017) Improved discrete cuckoo optimization algorithm for the three-stage assembly flowshop scheduling problem. Comput Ind Eng 105:158–173

    Google Scholar 

  • Kurz ME, Askin RG (2004) Scheduling flexible flow lines with sequence-dependent setup times. Eur J Oper Res 159(1):66–82

    MathSciNet  MATH  Google Scholar 

  • Lee CY, Lin CS (2001) Single-machine scheduling with maintenance and repair rate-modifying activities. Eur J Oper Res 135:493–513

    MathSciNet  MATH  Google Scholar 

  • Lee C-Y, Cheng TCE, Lin BMT (1993) Minimising the makespan in the 3-machineassembly-type flowshop scheduling problem. Manag Sci 39(5):616–625

    Google Scholar 

  • Lei D (2011) Scheduling stochastic job shop subject to random breakdown to minimize makespan. Int J Adv Manuf Technol 55:1183–1192

    Google Scholar 

  • Lei D, Zheng Y (2017) Hybrid flow shop scheduling with assembly operations and key objectives: a novel neighborhood search. Appl Soft Comput 61:122–128

    Google Scholar 

  • Leon VJ, Wu SD, Storer RH (1994) Robustness measures and robust scheduling for job shops. IIE Trans 26(5):32–43

    Google Scholar 

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

    Google Scholar 

  • Liu L, Gu H, Xi Y (2007) Robust and stable scheduling of a single machine with random machine. Int J Adv Manuf Technol 31:645–654

    Google Scholar 

  • Mokhtari H, Mozdgir A, Abadi IN (2012) A reliability/availability approach to joint production and maintenance scheduling with multiple preventive maintenance services. Int J Prod Res 50(20):5906–5925

    Google Scholar 

  • Moradi E, Zandieh M (2010) Minimizing the makespan and the system unavailability in parallel machine scheduling problem: a similarity-based genetic algorithm. Int J Adv Manuf Technol 51(5–8):829–840

    Google Scholar 

  • Mozdgir A, Fatemi Ghomi SMT, Joli F, Navaei J (2013) Two-stage assembly flow-shop scheduling problem with non-identical assembly machines considering setup times. Int J Prod Res 51:3625–3642

    Google Scholar 

  • Omran MGH, Salman A, Engelbrecht AP (2005) Self-adaptive differential evolution. In: Proceedings of the international conference on computational intelligence and security (CIS 2005). Xi’an, China, December, pp 192–199

  • Pan C-H, Chen J-S (1997) Scheduling alternative operations in two-machine flowshops. J Oper Res Soc 48(5):533–540

    MATH  Google Scholar 

  • Park SH (1995) Robust design and analysis for quality engineering. Chapman and Hall, London

    Google Scholar 

  • Pin L, Lin Y, Jinfang Z (2009) Cloud theory-based simulated annealing algorithm and application. Eng Appl Artif Intell 22(4–5):742–749

    Google Scholar 

  • Potts CN, Sevast’janov SV, Van Wassenhove LN, Zwaneveld CM (1995) The two-stage assembly scheduling problem: complexity and approximation. Oper Res 43(2):346–355

    MathSciNet  MATH  Google Scholar 

  • Qi X, Bard JF, Yu G (2006) Disruption management for machine scheduling: the case of SPT schedules. Int J Prod Econ 103:166–184

    Google Scholar 

  • Reeves CR (1997) Genetic algorithms for the operations researcher. INFORMS J Comput 9:231

    MathSciNet  MATH  Google Scholar 

  • Reeves CR (2003) Genetic algorithms. In: Glover F, Kochenberger GA (eds) Handbook of meta-heuristics, vol 574. Kluwer Academic Publishers, Dordrecht, p 55

    Google Scholar 

  • Ruiz R, Carlos Garcia-Diaz J, Maroto C (2007) Considering scheduling and preventive maintenance in the flowshop sequencing problem. Comput Oper Res 34:3314–3330

    MATH  Google Scholar 

  • Seidgar H, Kiani M, Abedi M, Fazlollahtabar H (2014) An efficient imperialist competitive algorithm for scheduling in the two-stage assembly flow shop problem. Int J Prod Res 52(4):1240–1256

    Google Scholar 

  • Seidgar H, Zandieh M, Fazlollahtabar H, Mahdavi I (2015) Simulated imperialist competitive algorithm in two-stage assembly flow shop with machine breakdowns and preventive maintenance. Proc Inst Mech Eng Part B J Eng Manuf 1:1. https://doi.org/10.1177/0954405414563554

    Article  Google Scholar 

  • Seidgar H, Tadayoni Rad S, Shafaei R (2017) Scheduling of assembly flow shop problem and machines with random breakdowns. Int J Oper Res 29(2):273–293

    MathSciNet  Google Scholar 

  • Seyed-Alagheband SA, Fatemi Ghomi SMT, Zandieh M (2011) A simulated annealing algorithm for balancing the assembly line type II problem with sequence-dependent setup times between tasks. Int J Prod Res 49(3):805–825

    Google Scholar 

  • Shokrollahpour E, Zandieh M, Dorri B (2011) A novel imperialist competitive algorithm for bi-criteria scheduling of the assembly flow shop problem. Int J Prod Res 49(11):3087–3103

    Google Scholar 

  • Stoop PPM, Weirs VCS (1996) The complexity of scheduling in practice. Int J Oper Prod Manag 16(10):37–53

    Google Scholar 

  • Storn R, Price K (1999) System design by constraint adaptation and differential evolution. IEEE Trans Evol Comput 3(1):22–34

    Google Scholar 

  • Sun X, Morizawa K, Nagasawa H (2003) Powerful heuristics to minimize makespan in fixed, 3-machine, assembly-type flowshop scheduling. Eur J Oper Res 146(3):498–516

    MathSciNet  MATH  Google Scholar 

  • Sung CS, Juhn J (2009) Makespan minimization for a 2-stage assembly scheduling problem subject to component available time constraint. Int J Prod Econ 119:392–401

    Google Scholar 

  • Sung CS, Kim HA (2008) A two-stage multiple-machine assembly scheduling problem for minimizing sum of completion times. Int J Prod Econ 113:1038–1048

    Google Scholar 

  • Suresh V, Chaudhuri D (1993) Dynamic scheduling: a survey of research. Int J Prod Econ 32(1):53–63

    Google Scholar 

  • Torabzadeh E, Zandieh M (2010) Cloud theory-based simulated annealing approach for scheduling in the two-stage assembly flow shop. Adv Eng Softw 41:1238–1243

    MATH  Google Scholar 

  • Tozkapan A, Kirca O, Chung C-S (2003) A branch and bound algorithm to minimise the total weighted flow time for the two-stage assembly scheduling problem. Comput Oper Res 30(2):309–320

    MathSciNet  MATH  Google Scholar 

  • Vieira GE, Hermann JW, Lin E (2003) Rescheduling manufacturing systems: a framework of strategies, policies and methods. J Sched 6(1):39–62

    MathSciNet  MATH  Google Scholar 

  • Wang MY, Sethi SP, Van de Velde SL (1997) Minimizing makespan in a class of reentrant shops. Oper Res 45(5):702–712

    MATH  Google Scholar 

  • Xiong F, Xing K (2014) Meta-heuristics for the distributed two-stage assembly scheduling problem with bi-criteria of makespan and mean completion time. Int J Prod Res 52(9):2743–2766

    Google Scholar 

  • Yamamoto M, Nof SY (1985) Scheduling/rescheduling in the manufacturing operating system environment. Int J Prod Res 23(4):705–722

    Google Scholar 

  • Zandieh M, Adibi MA (2010) Dynamic job shop scheduling using variable neighborhood search. Int J Prod Res 48:2449–2458

    MATH  Google Scholar 

  • Zandieh M, Gholami M (2009) An immune algorithm for scheduling a hybrid flow shop with sequence-dependent setup times and machines with random breakdowns. Int J Prod Res 47:6999–7027

    MATH  Google Scholar 

Download references

Funding

This study was not funded by any organization.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran

    Hany Seidgar

  2. Department of Industrial Engineering, School of Engineering, Damghan University, Damghan, Iran

    Hamed Fazlollahtabar

  3. Department of Industrial Management, Management and Accounting Faulty, Shahid Beheshti University, G.C., Tehran, Iran

    Mostafa Zandieh

Authors
  1. Hany Seidgar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamed Fazlollahtabar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mostafa Zandieh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamed Fazlollahtabar.

Ethics declarations

Conflict of interest

Authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Seidgar, H., Fazlollahtabar, H. & Zandieh, M. Scheduling two-stage assembly flow shop with random machines breakdowns: integrated new self-adapted differential evolutionary and simulation approach. Soft Comput 24, 8377–8401 (2020). https://doi.org/10.1007/s00500-019-04407-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-019-04407-3

Keywords

Search

Navigation