Skip to main content

Advertisement

SpringerLink
Log in

A hybrid random-key genetic algorithm to minimize weighted number of late deliveries for a single machine

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

Abstract

This paper presents a memetic algorithm (MA) to minimize the total weighted number of late jobs (or deliveries) on a single machine. The proposed MA combines a genetic algorithm (GA) with a neighborhood search. The performance of the proposed algorithm is compared with four heuristics from the literature, namely the early due date (EDD), the weighted shortest processing time (WSPT), the forward algorithm (FA), and the weighted forward algorithm (WFA), against 10 benchmark problems and three real-world problems. The results suggest that the MA outperformed the EDD, WSPT, FA, and WFA on the benchmark problems and performed as good as WFA on two of the three real-world problems and outperformed WFA on one real-world problem. The EDD performed the worst among the five solution approaches.

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. Karp RM (1972) Reducibility among combinatorial problems. In: Miller RE, Thatcher JW (eds) Complexity of computer computations. Plenum, New York, pp 85–103

    Chapter  Google Scholar 

  2. Smith WE (1956) Various optimizers for single stage production. Nav Res Logist Q 3(1):59–66

    Article  Google Scholar 

  3. Moore JM (1968) An n job one machine algorithm for minimizing the number of late jobs. Manag Sci 15:102–109

    Article  MATH  Google Scholar 

  4. Phojanamongkolkij N, Grabenstetter D, Ghrayeb O (2003) Scheduling jobs to improve weighted on-time performance of a single machine. J Manuf Syst 22:148–156

    Article  Google Scholar 

  5. Lenstra JK, Rinnooy Kan AHG, Brucker P (1968) Complexity of machine scheduling problems. Ann Discrete Math 1:343–362

    Article  MathSciNet  Google Scholar 

  6. Baptiste P (1999) Polynomial time algorithms for minimizing the weighted number of late jobs on a single machine when processing times are equal. J Scheduling 2(6):245–252

    Article  MathSciNet  MATH  Google Scholar 

  7. Sahni S (1976) Algorithms for scheduling independent jobs. J ACM 23:116–127

    Article  MathSciNet  MATH  Google Scholar 

  8. Villarreal FJ, Bulfin RL (1983) Scheduling a single machine to minimize the weighted number of tardy jobs. IIE Trans 15:337–343

    Article  Google Scholar 

  9. Tang G (1990) A new branch and bound algorithm for minimizing the weighted number of tardy jobs. Ann Oper Res 24:225–232

    Article  MathSciNet  MATH  Google Scholar 

  10. Potts CN, Van Wassenhove LN (1988) Algorithms for scheduling a single machine to minimize the weighted number of late jobs. Manag Sci 34:843–858

    Article  MATH  Google Scholar 

  11. M’Hallah R, Bulfin RL (2003) Minimizing the weighted number of tardy jobs on a single machine. Eur J Oper Res 145:45–56

    Article  MathSciNet  MATH  Google Scholar 

  12. Lin BMT, Cheng TCE (1999) Minimizing the weighted number of tardy jobs and maximum tardiness in a relocation problem with due date constraints. Eur J Oper Res 116:183–193

    Article  MATH  Google Scholar 

  13. Peridy L, Pinson E, Rivereau D (2003) Using short-term memory to minimize the weighted number of late jobs on a single machine. Eur J Oper Res 148:591–603

    Article  MATH  Google Scholar 

  14. Sadykov R (2008) A branch-and-check algorithm for minimizing the weighted number of late jobs on a single machine with release dates. Eur J Oper Res 189:1284–1304

    Article  MathSciNet  MATH  Google Scholar 

  15. Dauzere-Peres S, Sevaux M (2003) Using Lagrangian relaxation to minimize the weighted number of late jobs. Nav Res Logist 50(3):273–288

    Article  MathSciNet  MATH  Google Scholar 

  16. M’Hallah R, Bulfin RL (2007) Minimizing the weighted number of tardy jobs on a single machine with release dates. Eur J Oper Res 176:727–744

    Article  MathSciNet  MATH  Google Scholar 

  17. Sevaux M, Dauzere-Peres S (2003) Genetic algorithms to minimize the weighted number of late jobs on a single machine. Eur J Oper Res 151:296–306

    Article  MathSciNet  MATH  Google Scholar 

  18. Gordon VS, Werner F, Yanushkevich OA (2001) Single machine preemptive scheduling to minimize the weighted number of late jobs with deadlines and nested release/due date intervals. Oper Res 35:71–83

    Article  MathSciNet  MATH  Google Scholar 

  19. Brucker P, Kovalyov MY (1996) Single machine batch scheduling to minimize the weighted number of late jobs. Math Methods Oper Res 43:1–8

    Article  MathSciNet  MATH  Google Scholar 

  20. Vakhania N (2009) Scheduling jobs with release times preemptively on a single machine to minimize the number of late jobs. Oper Res Lett 37(6):405–410

    Article  MathSciNet  MATH  Google Scholar 

  21. Wan G, Yen B (2009) Single machine scheduling to minimize total weighted earliness subject to minimal number of tardy jobs. Eur J Oper Res 195(1):89–97

    Article  MathSciNet  MATH  Google Scholar 

  22. Valente J, Gonçalves JF (2009) A genetic algorithm approach for the single machine scheduling problem with linear earliness and quadratic tardiness penalties. Comput Oper Res 36(10):2707–2715

    Article  MATH  Google Scholar 

  23. Baptiste P, Pape CL (2005) Scheduling a single machine to minimize a regular objective function under setup constraints. Discret Optim 2(1):83–99

    Article  MATH  Google Scholar 

  24. Kethley RB, Alidaee B (2002) Single machine scheduling to minimize total weighted late work: a comparison of scheduling rules and search algorithms. Comput Ind Eng 43(3):509–528

    Article  Google Scholar 

  25. Bean JC (1994) Genetics and random keys for sequencing and optimization. ORSA J Comput 6:154–160

    Article  MATH  Google Scholar 

  26. Gen M, Cheng R (1997) Genetic algorithms and engineering design. Wiley, New York

    Google Scholar 

  27. Chakraborty UK, Deb K, Chakraborty M (1996) Analysis of selection algorithms: a Markov chain approach. Evol Comput 4(2):133–167

    Article  Google Scholar 

  28. Chakraborty UK (2009) Minimizing flow time in permutation flow shop scheduling with improved simulated annealing. Proc. IEEE Congress on Evolutionary Computation, pp. 158–165.

Download references

Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Northern Illinois University, DeKalb, IL, 60115, USA

    Omar Ghrayeb & Purushothaman Damodaran

Authors
  1. Omar Ghrayeb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Purushothaman Damodaran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Purushothaman Damodaran.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ghrayeb, O., Damodaran, P. A hybrid random-key genetic algorithm to minimize weighted number of late deliveries for a single machine. Int J Adv Manuf Technol 66, 15–25 (2013). https://doi.org/10.1007/s00170-012-4302-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-012-4302-1

Keywords

Search

Navigation