Skip to main content
SpringerLink
Log in

Heuristics for flow shop rescheduling with mixed blocking constraints

  • Original Paper
  • Published:
TOP Aims and scope Submit manuscript

Abstract

In the flow shop rescheduling literature, many papers consider unlimited buffer capacities between successive machines. In real fact, these capacities may be limited, or no store may exist. Thus, a blocking situation is inducted. Diverse types of blocking constraints are studied in the flow shop scheduling problems. However, in dynamic environments, only few papers deal with these kinds of constraints. The aim of this paper is to investigate a problem of rescheduling the jobs in a flowshop environment and mixed blocking as a constraint, considering simultaneously schedule efficiency and stability as a performance measure, and job arrival as a disruption. An iterative methodology based on the predictive–reactive strategy is implemented for dealing with this rescheduling problem. The problem has first been modeled as a Mixed Integer Linear Programing (MILP) model. Experimental results show that the MILP resolution is only possible for small-sized instances. Hence, inspired by NEH algorithm, we proposed four heuristics for solving large-sized instances of this problem. Eventually, we discussed the performance of the proposed heuristics for different blocking situations, both in terms of solution efficiency and resolution 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

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author, AT, upon reasonable request.

References

  • Akkan C (2015) Improving schedule stability in single-machine rescheduling for new operation insertion. Comput Oper Res 64:198–209

    Article  Google Scholar 

  • Akyol Ozer E, Sarac T (2019) MIP models and a matheuristic algorithm for an identical parallel machine scheduling problem under multiple copies of shared resources constraints. TOP 27(1):94–124

    Article  Google Scholar 

  • Auer P, Dósa G, Dulai T, Fügenschuh A, Näser P, Ortner R, Werner-Stark Á (2021) A new heuristic and an exact approach for a production planning problem. CEJOR 29(3):1079–1113

    Article  Google Scholar 

  • Bautista-Valhondo J, Alfaro-Pozo R (2020) Mixed integer linear programming models for Flow Shop Scheduling with a demand plan of job types. CEJOR 28(1):5–23

    Article  Google Scholar 

  • Braune R, Gutjahr WJ, Vogl P (2022) Stochastic radiotherapy appointment scheduling. CEJOR 30(4):1239–1277

    Article  Google Scholar 

  • De La Vega J, Moreno A, Morabito R, Munari P (2023) A robust optimization approach for the unrelated parallel machine scheduling problem. TOP 31(1):31–66

    Article  Google Scholar 

  • Druetto A, Pastore E, Rener E (2022) Parallel batching with multi-size jobs and incompatible job families. Top 31(2):440–458

    Article  Google Scholar 

  • Guo Y, Xie X (2017) Two mixed integer programming formulations on single machine to reschedule repaired jobs for minimizing the total waiting-time. Chinese Automation Congress (CAC), IEEE, pp 2129–2133

  • Gürel S, Körpeoğlu E, Aktürk MS (2010) An anticipative scheduling approach with controllable processing times. Comput Oper Res 37(6):1002–1013

    Article  Google Scholar 

  • Hall NG, Potts CN (2004) Rescheduling for new orders. Oper Res 52(3):440–453

    Article  Google Scholar 

  • Haroune M, Dhib C, Neron E, Soukhal A, Mohamed Babou H, Nanne MF (2022) Multi-project scheduling problem under shared multi-skill resource constraints. Top 31(1):194–235

    Article  Google Scholar 

  • He X, Dong S, Zhao N (2020) Research on rush order insertion rescheduling problem under hybrid flow shop based on NSGA-III. International journal of production research 58(4):1161–1177

    Article  Google Scholar 

  • Herrmann JW (2006) Rescheduling strategies, policies, and methods. Handbook of production scheduling. Springer, Boston, pp 135–148

  • Kacem A, Dammak A (2021) Multi-objective scheduling on two dedicated processors. TOP 29(3):694–721

    Article  Google Scholar 

  • Kan AR (1976) Problem formulation. Machine scheduling problems. Springer, Boston, pp 5–29

    Chapter  Google Scholar 

  • Katragjini K, Vallada E, Ruiz R (2013) Flow shop rescheduling under different types of disruption. Int J Prod Res 51(3):780–797

    Article  Google Scholar 

  • Kecman P, Corman F, D’Ariano A, Goverde RM (2013) Rescheduling models for railway traffic management in large-scale networks. Public Transp 5(1–2):95–123

    Article  Google Scholar 

  • Kovalyov MY, Kress D, Meiswinkel S, Pesch E (2019) A parallel machine schedule updating game with compensations and clients averse to uncertain loss. Comput Oper Res 103:148–157

    Article  Google Scholar 

  • Li Y, Carabelli S, Fadda E, Manerba D, Tadei R, Terzo O (2020) Machine learning and optimization for production rescheduling in Industry 4.0. Int J Adv Manuf Technol 110(9):2445–2463

    Article  Google Scholar 

  • Liu Z, Ro YK (2014) Rescheduling for machine disruption to minimize makespan and maximum lateness. J Sched 17(4):339–352

    Article  Google Scholar 

  • Liu L, Zhou H (2013) Open shop rescheduling under singular machine disruption. Comput Integr Manuf Syst 10:12

    Google Scholar 

  • Lodree E Jr, Jang W, Klein CM (2004) A new rule for minimizing the number of tardy jobs in dynamic flow shops. Eur J Oper Res 159(1):258–263

    Article  Google Scholar 

  • Machado-Dominguez LF, Paternina-Arboleda CD, Vélez JI, Barrios-Sarmiento A (2022) An adaptative bacterial foraging optimization algorithm for solving the MRCPSP with discounted cash flows. TOP 30(2):221–248

    Article  Google Scholar 

  • Manzini M, Demeulemeester E, Urgo M (2022) A predictive–reactive approach for the sequencing of assembly operations in an automated assembly line. Robot Comput-Integr Manuf 73:102201

    Article  Google Scholar 

  • Martinez S, Dauzère-Pérès S, Gueret C, Mati Y, Sauer N (2006) Complexity of flowshop scheduling problems with a new blocking constraint. Eur J Oper Res 169(3):855–864

    Article  Google Scholar 

  • Mohan J, Lanka K, Rao NA, Manupati VK (2022) Sustainable flexible job shop scheduling: a systematic literature review. In: Global congress on manufacturing and management. Springer, Cham, pp 227–246

  • Mula J, Bogataj M (2021) OR in the industrial engineering of Industry 4.0: experiences from the Iberian Peninsula mirrored in CJOR. Central Eur J Oper Res 29(4):1163–1184

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Ozolins A (2021) Dynamic programming approach for solving the open shop problem. CEJOR 29(1):291–306

    Article  Google Scholar 

  • Pitombeira-Neto AR, Prata BDA (2020) A matheuristic algorithm for the one-dimensional cutting stock and scheduling problem with heterogeneous orders. TOP 28(1):178–192

    Article  Google Scholar 

  • Prata BDA, de Abreu LR, Lima JYF (2021) Heuristic methods for the single-machine scheduling problem with periodical resource constraints. TOP 29(2):524–546

    Article  Google Scholar 

  • Rahmani D, Ramezanian R (2016) A stable reactive approach in dynamic flexible flow shop scheduling with unexpected disruptions: a case study. Comput Ind Eng 98:360–372

    Article  Google Scholar 

  • Sauvey C, Sauer N (2020) Two NEH heuristic improvements for flowshop scheduling problem with makespan criterion. Algorithms 13(5):112

    Article  Google Scholar 

  • Sauvey C, Trabelsi W, Sauer N (2020) Mathematical model and evaluation function for conflict-free warranted makespan minimization of mixed blocking constraint job-shop problems. Mathematics 8(1):121

    Article  Google Scholar 

  • Sayed SI, Contreras I, Diaz JA, Luna DE (2020) Integrated cross-dock door assignment and truck scheduling with handling times. TOP 28(3):705–727

    Article  Google Scholar 

  • Şenyiğit E, Atici U, Şenol MB (2022) Effects of OCRA parameters and learning rate on machine scheduling. Central Eur J Oper Res 30:941–959

    Article  Google Scholar 

  • Serrano-Ruiz JC, Mula J, Poler R (2021) Smart manufacturing scheduling: a literature review. J Manuf Syst 61:265–287

    Article  Google Scholar 

  • Serrano-Ruiz JC, Mula J, Poler R (2022) Development of a multidimensional conceptual model for job shop smart manufacturing scheduling from the Industry 4.0 perspective. J Manuf Syst 63:185–202

    Article  Google Scholar 

  • Tao Z, Liu X (2019) Dynamic scheduling of dual-resource constrained blocking job shop. In: International conference on intelligent robotics and applications. Springer, Cham, pp 447–456

  • Tighazoui A, Sauvey C, Sauer N (2020) New efficiency-stability criterion in a rescheduling problem with dynamic jobs weights. In: 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT), vol 1, IEEE, pp 475–480

  • Tighazoui A, Sauvey C, Sauer N (2021a) Predictive-reactive strategy for flowshop rescheduling problem: minimizing the total weighted waiting times and instability. J Syst Sci Syst Eng 30:253–275

    Article  Google Scholar 

  • Tighazoui A, Sauvey C, Sauer N (2021b) Predictive-reactive strategy for identical parallel machine rescheduling. Comput Oper Res 134:105372

    Article  Google Scholar 

  • Trabelsi W, Sauvey C, Sauer N (2011) Complexity and mathematical model for flowshop problem subject to different types of blocking constraint. IFAC Proc Vol 44(1):8183–8188

    Article  Google Scholar 

  • Trabelsi W, Sauvey C, Sauer N (2012) Heuristics and metaheuristics for mixed blocking constraints flowshop scheduling problems. Comput Oper Res 39(11):2520–2527

    Article  Google Scholar 

  • Uhlmann IR, Zanella RM, Frazzon EM (2022) Hybrid flow shop rescheduling for contract manufacturing services. Int J Prod Res 60(3):1069–1085

    Article  Google Scholar 

  • Valledor P, Gomez A, Puente J, Fernandez I (2022) Solving rescheduling problems in dynamic permutation flow shop environments with multiple objectives using the hybrid dynamic non-dominated sorting genetic II algorithm. Mathematics 10(14):2395

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Wang L, Zhang L, Zheng DZ (2006) An effective hybrid genetic algorithm for flow shop scheduling with limited buffers. Comput Oper Res 33(10):2960–2971

    Article  Google Scholar 

  • Wu Q, Xie N, Zheng S, Bernard A (2022) Online order scheduling of multi 3D printing tasks based on the additive manufacturing cloud platform. J Manuf Syst 63:23–34

    Article  Google Scholar 

  • Xiao J, Osterweil LJ, Wang Q, Li M (2010) Dynamic resource scheduling in disruption-prone software development environments. International conference on fundamental approaches to software engineering. Springer, Berlin, pp 107–122

    Chapter  Google Scholar 

  • Yan P, Liu SQ, Sun T, Ma K (2018) A dynamic scheduling approach for optimizing the material handling operations in a robotic cell. Comput Oper Res 99:166–177

    Article  Google Scholar 

  • Yuan K, Sauer N, Sauvey C (2009) Application of EM algorithm to hybrid flow shop scheduling problems with a special blocking. In: 2009 IEEE conference on emerging technologies & factory automation, IEEE, pp 1–7. https://doi.org/10.1109/ETFA.2009.5347066

  • Zhang L, Gao L, Li X (2013) A hybrid intelligent algorithm and rescheduling technique for job shop scheduling problems with disruptions. Int J Adv Manuf Technol 65(5):1141–1156

    Article  Google Scholar 

  • Zhang L, Hu Y, Wang C, Tang Q, Li X (2022a) Effective dispatching rules mining based on near-optimal schedules in intelligent job shop environment. J Manuf Syst 63:424–438

    Article  Google Scholar 

  • Zhang X, Lin WC, Wu CC (2022b) Rescheduling problems with allowing for the unexpected new jobs arrival. J Comb Optim 43(3):630–645

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the Urban Community of Sarreguemines-France and the Grand Est Region-France.

Author information

Authors and Affiliations

  1. Université de Strasbourg, ICUBE Laboratory, CNRS 7357, Strasbourg, France

    Ayoub Tighazoui

  2. Université de Lorraine, LGIPM, 57000, Metz, France

    Christophe Sauvey & Nathalie Sauer

Authors
  1. Ayoub Tighazoui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christophe Sauvey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nathalie Sauer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ayoub Tighazoui.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tighazoui, A., Sauvey, C. & Sauer, N. Heuristics for flow shop rescheduling with mixed blocking constraints. TOP (2023). https://doi.org/10.1007/s11750-023-00662-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11750-023-00662-8

Keywords

Mathemetics Subject Classification

Search

Navigation