Skip to main content
SpringerLink
Log in

Solving manpower scheduling problem in manufacturing using mixed-integer programming with a two-stage heuristic algorithm

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

Abstract

Manpower scheduling problem is one of the key scheduling problems with extensive applications in manufacturing. This paper presents a mixed-integer programming model with a two-stage heuristic algorithm for solving the manpower scheduling problem in the precision engineering industry. Firstly, a mixed-integer programming formulation is developed to model the manpower scheduling problem in this high-mix low-volume manufacturing environment. Secondly, a two-stage heuristic algorithm is proposed where the first stage is deployed to calculate the skill requirements for each shift by considering the jobs, machines, and their production schedule and the second stage is designed to assign operators to the machines by considering the skill set requirements and the operator's expressed preferences. Lastly, the computational results based on problem instances emulating real-world scenarios demonstrated the feasibility and effectiveness of the proposed heuristic.

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. Stadtler H (2005) Supply chain management and advanced planning—basics, overview and challenges. Eur J Oper Res 163(3):575–588

    Article  MATH  Google Scholar 

  2. Wang L, Zheng DZ (2003) An effective hybrid heuristic for flow shop scheduling. Int J Adv Manuf Technol 21(1):38–44

    Article  Google Scholar 

  3. Dimopoulos C, Zalazala AMS (2000) Recent development in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons. IEEE Trans Evol Comput 4(2):93–113

    Article  Google Scholar 

  4. Liu B, Wang L, Jin YH (2007) An effective hybrid particle swarm optimization for no-wait flow shop scheduling. Int J Adv Manuf Technol 31:1001–1011

    Article  Google Scholar 

  5. Wang L (2003) Shop scheduling with genetic algorithms. Tsinghua University Press, Beijing

    Google Scholar 

  6. Pan QK, Wang L (2008) No-idle permutation flow shop scheduling based on a hybrid discrete particle swarm optimization algorithm. Int J Adv Manuf Technol 39:796–807

    Article  Google Scholar 

  7. Pan QK, Wang L, Zhao BH (2008) An improved iterated greedy algorithm for the no-wait flow shop scheduling problem with makespan criterion. Int J Adv Manuf Technol 38(7–8):778–786

    Article  Google Scholar 

  8. Pan QK, Wang L, Tasgetirend MF, Zhao BH (2008) A hybrid discrete particle swarm optimization algorithm for the no-wait flow shop scheduling problem with makespan criterion. Int J Adv Manuf Technol 38(3–4):337–347

    Article  Google Scholar 

  9. Chua TJ, Cai TX, Low JMW (2008) Dynamic operations and manpower scheduling for high-mix, low-volume manufacturing. Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 15–18 September, pp 54–57

  10. Aykin T (2000) A comparative evaluation of modeling approaches to the labor shift scheduling problem. Eur J Oper Res 125:381–397

    Article  MATH  Google Scholar 

  11. Grabot B, Letouzey A (2000) Short-term manpower management in manufacturing systems: new requirements and DSS prototyping. Comput Ind 43:11–29

    Article  Google Scholar 

  12. Burns RN, Carter MW (1985) Workforce scheduling with cyclic demands and days-off constraints. Manag Sci 24:161–167

    Google Scholar 

  13. Emmons H (1987) Workforce scheduling with cyclic requirements and constraints on days off, weekends off and workstrech. IIE Trans 17:8–16

    Article  Google Scholar 

  14. Hung R (1994) Multiple-shift workforce scheduling under the 3–4 workweek with different weekday and weekend labor requirements. Manag Sci 40:280–284

    Article  MATH  MathSciNet  Google Scholar 

  15. Yano C, Rachamadugu R (1991) Sequencing to minimize work overload in assembly lines with product options. Manag Sci 37(5):1–15

    Article  Google Scholar 

  16. Russell RS, Huang PY, Leu Y (1991) A study of labor allocation strategies in cellular manufacturing. Decis Sci 22(3):594–611

    Article  Google Scholar 

  17. Lagodimos AG, Leopoulos V (2000) Greedy heuristic algorithms for manpower shift planning. Int J Prod Econ 68(1):95–106

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science, Liaocheng University, Liaocheng, 252059, People’s Republic of China

    Quan-Ke Pan

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore

    Ponnuthurai N. Suganthan

  3. Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore, 638075, Singapore

    Tay J. Chua & T. X. Cai

Authors
  1. Quan-Ke Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ponnuthurai N. Suganthan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tay J. Chua
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. T. X. Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ponnuthurai N. Suganthan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pan, QK., Suganthan, P.N., Chua, T.J. et al. Solving manpower scheduling problem in manufacturing using mixed-integer programming with a two-stage heuristic algorithm. Int J Adv Manuf Technol 46, 1229–1237 (2010). https://doi.org/10.1007/s00170-009-2175-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-009-2175-8

Keywords

Search

Navigation