Skip to main content
SpringerLink
Log in

Scheduling Heuristics

  • Reference work entry
  • First Online:
Handbook of Heuristics

  • 4227 Accesses

Abstract

The scheduling of operations over resources is a relevant theoretical and practical problem with applications in many fields and disciplines, including the manufacturing industry. Scheduling problems are as varied as the reality they model. Additionally, some scheduling settings are among the hardest combinatorial problems there are. This is a perfect scenario for heuristic methods where high-quality robust solutions can be obtained in a short amount of time. This chapter concentrates on heuristics for production scheduling problems and summarizes the main results that range from simple rules to advanced metaheuristics. The importance of proper scheduling in practice is first highlighted, along with its difficulty and relevance. A summary of the scheduling notation is also given. Basic scheduling techniques, dispatching rules, combined rules, advanced heuristics, and an introduction to metaheuristics are also summarized in the chapter. While necessarily brief and incomplete, this chapter serves as an introductory point to those interested readers seeking to delve in the vast and rich world of scheduling heuristics. Some pointers to fruitful future research avenues are also provided. A large list of journal articles and monographs are provided as a reference for additional details and study.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 999.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 1,199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Adams J, Balas E, Zawack D (1988) The shifting bottleneck procedure for job shop scheduling. Manag Sci 34(3):391–401

    Google Scholar 

  2. Błażewicz J, Ecker KH, Pesch E, Schmidt G, Wȩglarz J (2001) Scheduling computer and manufacturing processes, 2nd edn. Springer, Berlin

    Google Scholar 

  3. Baldacci R, Mingozzi A, Roberti R (2011) New route relaxation and pricing strategies for the vehicle routing problem. Oper Res 59(5):1269–1283

    Google Scholar 

  4. Blackstone JH Jr, Phillips DT, Hogg GL (1982) A state-of-the-art survey of dispatching rules for manufactuing job shop operations. Int J Prod Res 20(1):27–45

    Google Scholar 

  5. Briskorn D (2008) Sports leagues scheduling. Models, combinatorial properties, and optimization algorithms. Lecture notes in economics and mathematical systems, vol 603. Springer, Berlin/Heidelberg

    Google Scholar 

  6. Brucker P (2007) Scheduling algorithms, 5th edn. Springer, New York

    Google Scholar 

  7. Brucker P, Jurisch B, Sievers B (1994) A branch and bound algorithm for the job-shop scheduling problem. Discret Appl Math 49(1–3):107–127

    Google Scholar 

  8. Cai X, Wu X, Zhou X (2014) Optimal stochastic scheduling. International series in operations research & management science, vol 207. Springer, New York

    Google Scholar 

  9. Carlier J (1982) The one-machine sequencing problem. Eur J Oper Res 11(1):42–47

    Google Scholar 

  10. Chakraborty UK, Laha D (2007) An improved heuristic for permutation flowshop scheduling. Int J Inf Commun Technol 1(1):89–97

    Google Scholar 

  11. Ciavotta M, Minella G, Ruiz R (2013) Multi-objective sequence dependent setup times flowshop scheduling: a new algorithm and a comprehensive study. Eur J Oper Res 227(2):301–313

    Google Scholar 

  12. Dong XY, Huang HK, Chen P (2006) A more effective constructive algorithm for permutation flowshop problem. In: Intelligent data engineering and automated learning (IDEAL 2006). lecture notes in computer science, vol 4224. Springer, Berlin/New York (Beijing Jiaotong Univ, Sch Comp & IT, Beijing 100044, Peoples R China), pp 25–32

    Google Scholar 

  13. Dong XY, Huang HK, Chen P (2008) An improved NEH-based heuristic for the permutation flowshop problem. Comput Oper Res 35(12):3962–3968

    Google Scholar 

  14. Dudek RA, Panwalkar SS, Smith ML (1992) The lessons of flowshop scheduling research. Oper Res 40(1):7–13

    Google Scholar 

  15. El-Ghazali T (2009) Metaheuristics: from design to implementation. Wiley, New York

    Google Scholar 

  16. Fanjul-Peyro L, Ruiz R (2010) Iterated greedy local search methods for unrelated parallel machine scheduling. Eur J Oper Res 207(1):55–69

    Google Scholar 

  17. Fernandez-Viagas V, Framinan JM (2014) On insertion tie-breaking rules in heuristics for the permutation flowshop scheduling problem. Comput Oper Res 45:60–67

    Google Scholar 

  18. Fernandez-Viagas V, Framinan JM (2015) A bounded-search iterated greedy algorithm for the distributed permutation flowshop scheduling problem. Int J Prod Res 53(4):1111–1123

    Google Scholar 

  19. Ford FN, Bradbard DA, Ledbetter WN, Cox JF (1987) Use of operations research in production management. Prod Invent Manag 28(3):59–62

    Google Scholar 

  20. Framinan JM, Leisten R (2008) Total tardiness minimization in permutation flow shops: a simple approach based on a variable greedy algorithm. Int J Prod Res 46(22):6479–6498

    Google Scholar 

  21. Framinan JM, Ruiz R (2010) Architecture of manufacturing scheduling systems: literature review and an integrated proposal. Eur J Oper Res 205(2):237–246

    Google Scholar 

  22. Framinan JM, Ruiz R (2012) Guidelines for the deployment and implementation of manufacturing scheduling systems. Int J Prod Res 50(7):1799–1812

    Google Scholar 

  23. Framinan JM, Leisten R, Rajendran C (2003) Different initial sequences for the heuristic of Nawaz, Enscore and Ham to minimize makespan, idletime or flowtime in the static permutation flowshop sequencing problem. Int J Prod Res 41(1):121–148

    Google Scholar 

  24. Framinan JM, Leisten R, Ruiz R (2014) Manufacturing scheduling systems. An integrated view on models, methods and tools. Springer, New York

    Google Scholar 

  25. Graham RL (1969) Bounds on multiprocessing timing anomalies. SIAM J Appl Math 17(2):416–429

    Google Scholar 

  26. Graves SC (1981) A review of production scheduling. Oper Res 29(4):646–675

    Google Scholar 

  27. Grosche T (2009) Computational intelligence in integrated airline scheduling. Studies in computational intelligence, vol 173. Springer, New York

    Google Scholar 

  28. Haupt R (1989) A survey or priority rule-based scheduling. OR Spectr 11(1):3–16

    Google Scholar 

  29. Hoos HH, Stützle T (2005) Stochastic local search: foundations and applications. Morgan Kaufmann, San Francisco

    Google Scholar 

  30. Hopp WJ, Spearman ML (2011) Factory physics, 3rd edn. Waveland Press Inc., Long Grove

    Google Scholar 

  31. Jayamohan MS, Rajendran C (2000) New dispatching rules for shop scheduling: a step forward. Int J Prod Res 38(3):563–586

    Google Scholar 

  32. Jin F, Song SJ, Wu C (2007) An improved version of the NEH algorithm and its application to large-scale flow-shop scheduling problems. IIE Trans 39(2):229–234

    Google Scholar 

  33. Kalczynski PJ, Kamburowski J (2007) On the NEH heuristic for minimizing the makespan in permutation flow shops. OMEGA Int J Manag Sci 35(1):53–60

    Google Scholar 

  34. Kalczynski PJ, Kamburowski J (2008) An improved NEH heuristic to minimize makespan in permutation flow shops. Comput Oper Res 35(9):3001–3008

    Google Scholar 

  35. Kalczynski PJ, Kamburowski J (2009) An empirical analysis of the optimality rate of flow shop heuristics. Eur J Oper Res 198(1):93–101

    Google Scholar 

  36. Laporte G (2009) Fifty years of vehicle routing. Transp Sci 43(4):408–416

    Google Scholar 

  37. Ledbetter WN, Cox JF (1977) Operations research in production management: an investigation of past and present utilisation. Prod Invent Manag 18(3):84–91

    Google Scholar 

  38. Lee YH, Bhaskaran K, Pinedo ML (1997) A heuristic to minimize the total weighted tardiness with sequence-dependent setups. IIE Trans 29(1):45–52

    Google Scholar 

  39. Lenstra JK, Rinnooy Kan AHG, Brucker P (1977) Complexity of machine scheduling problems. Ann Discret Math 1:343–362

    Google Scholar 

  40. Lin SW, Ying KC, Huang CY (2013) Minimising makespan in distributed permutation flowshops using a modified iterated greedy algorithm. Int J Prod Res 51(16):5029–5038

    Google Scholar 

  41. MacCarthy BL, Liu J (1993) Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling. Int J Prod Res 31(1):59–79

    Google Scholar 

  42. McKay KN, Wiers VCS (1999) Unifying the theory and practice of production scheduling. J Manuf Syst 18(4):241–255

    Google Scholar 

  43. McKay KN, Wiers VCS (2004) Practical production control. A survival guide for planners and schedulers. J. Ross Publishing Inc., Boca Raton

    Google Scholar 

  44. McKay KN, Wiers VCS (2006) The human factor in planning and scheduling, chap 2. In: Herrmann JW (ed) Handbook of production scheduling. International series in operations research & management science, vol 89. Springer, New York, pp 23–57

    Google Scholar 

  45. McKay KN, Safayeni FR, Buzacott JA (1988) Job-shop scheduling theory: what is relevant? Interfaces 4(18):84–90

    Google Scholar 

  46. McKay KN, Pinedo ML, Webster S (2002) Practice-focused research issues for scheduling systems. Prod Oper Manag 11(2):249–258

    Google Scholar 

  47. Minella G, Ruiz R, Ciavotta M (2011) Restarted iterated pareto greedy algorithm for multi-objective flowshop scheduling problems. Comput Oper Res 38(11):1521–1533

    Google Scholar 

  48. Morton TE, Pentico DW (1993) Heuristic scheduling sysmtems with applications to production systems and project management. Wiley series in engineering & technology management. Wiley, Hoboken

    Google Scholar 

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

    Article  Google Scholar 

  50. Olhager J, Rapp B (1995) Operations research techniques in manufacturing planning and control systems. Int Trans Oper Res 2(1):29–43

    Article  Google Scholar 

  51. Ovacik IM, Uzsoy R (1997) Decomposition methods for complex factory scheduling problems. Springer, New York

    Book  Google Scholar 

  52. Pan QK, Ruiz R (2012) Local search methods for the flowshop scheduling problem with flowtime minimization. Eur J Oper Res 222(1):31–43

    Article  MathSciNet  Google Scholar 

  53. Pan QK, Ruiz R (2014) An effective iterated Greedy algorithm for the mixed no-idle flowshop scheduling problem. OMEGA Int J Manag Sci 44(1):41–50

    Article  Google Scholar 

  54. 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 

  55. Panwalkar SS, Iskander W (1977) A survey of scheduling rules. Oper Res 25(1):45–61

    Article  MathSciNet  Google Scholar 

  56. Pinedo ML (2009) Planning and scheduling in manufacturing and services, 2nd edn. Springer, New York

    Book  Google Scholar 

  57. Pinedo ML (2012) Scheduling: theory, algorithms and systems, 4th edn. Springer, New York

    Book  Google Scholar 

  58. Rad SF, Ruiz R, Boroojerdian N (2009) New high performing heuristics for minimizing makespan in permutation flowshops. OMEGA Int J Manag Sci 37(2):331–345

    Article  Google Scholar 

  59. Randolph H (ed) (2012) Handbook of healthcare system scheduling. International series in operations research & management science, vol 168. Springer, New York

    Google Scholar 

  60. Reisman A, Kumar A, Motwani J (1997) Flowshop scheduling/sequencing research: a statistical review of the literature, 1952–1994. IEEE Trans Eng Manag 44(3):316–329

    Article  Google Scholar 

  61. Ribas I, Leisten R, Framinan JM (2010) Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective. Comput Oper Res 37(8):1439–1454

    Article  MathSciNet  Google Scholar 

  62. Ribas I, Companys R, Tort-Martorell X (2011) An iterated greedy algorithm for the flowshop scheduling problem with blocking. OMEGA Int J Manag Sci 39(3):293–301

    Article  Google Scholar 

  63. Rinnooy Kan AHG (1976) Machine scheduling problems: classification, complexity and computations. Martinus Nijhoff, The Hague

    Google Scholar 

  64. Ruiz R, Maroto C (2005) A comprehensive review and evaluation of permutation flowshop heuristics. Eur J Oper Res 165(2):479–494

    Article  Google Scholar 

  65. 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

    Article  Google Scholar 

  66. Ruiz R, Stützle T (2008) An iterated greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives. Eur J Oper Res 187(3):1143–1159

    Article  Google Scholar 

  67. Ruiz R, Vázquez-Rodríguez JA (2010) The hybrid flowshop scheduling problem. Eur J Oper Res 205(1):1–18

    Article  Google Scholar 

  68. Ruiz R, Vallada E, Fernández-Martínez C (2009) Scheduling in flowshops with no-idle machines. In: Chakraborty UK (ed) Computational intelligence in flow shop and job shop scheduling. Studies in computational intelligence, vol 230. Springer, Berlin, pp 21–51

    Chapter  Google Scholar 

  69. Sarin SC, Nagarajan B, Liao L (2014) Stochastic scheduling. Expectation-variance analysis of a schedule. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  70. Sörensen K (2015) Metaheuristics – the metaphor exposed. Int Trans Oper Res 22(1):3–18

    Article  MathSciNet  Google Scholar 

  71. Taillard E (1990) Some efficient heuristic methods for the flow shop sequencing problem. Eur J Oper Res 47(1):67–74

    Article  MathSciNet  Google Scholar 

  72. Taillard E (1993) Benchmarks for basic scheduling problems. Eur J Oper Res 64(2):278–285

    Article  Google Scholar 

  73. T’Kindt V, Billaut JC (2006) Multicriteria scheduling: theory, models and algorithms, 2nd edn. Springer, New York

    MATH  Google Scholar 

  74. Turner S, Booth D (1987) Comparison of heuristics for flow shop sequencing. OMEGA Int J Manag Sci 15(1):75–78

    Article  Google Scholar 

  75. Urlings T, Ruiz R (2007) Local search in complex scheduling problems. In: Stützle T, Birattari M, Hoos HH (eds) Engineering stochastic local search algorithms. Designing, implementing and analyzing effective heuristics. Lecture notes in computer science, vol 4638. Springer, Brussels, pp 202–206

    Chapter  Google Scholar 

  76. Urlings T, Ruiz R, Sivrikaya-Şerifoğlu F (2010) Genetic algorithms for complex hybrid flexible flow line problems. International Journal of Metaheuristics 1(1):30–54

    Article  Google Scholar 

  77. Urlings T, Ruiz R, Stützle T (2010) Shifting representation search for hybrid flexible flowline problems. European Journal of Operational Research 207(2):1086–1095

    Article  MathSciNet  Google Scholar 

  78. Vallada E, Ruiz R (2009) Cooperative metaheuristics for the permutation flowshop scheduling problem. Eur J Oper Res 193(2):365–376

    Article  Google Scholar 

  79. Vepsalainen APJ, Morton TE (1987) Priority rules and lead time estimation for job shop scheduling with weighted tardiness costs. Manag Sci 33(8):1036–1047

    Article  Google Scholar 

  80. Vignier A, Billaut JC, Proust C (1999) Les problèmes d’ordonnancement de type flow-shop hybride: État de l’art. RAIRO Recherche opérationnelle 33(2):117–183 (in French)

    MATH  Google Scholar 

  81. Weyland D (2010) A rigorous analysis of the harmony search algorithm: how the research community can be misled by a “novel” methodology. Int J Appl Metaheuristic Comput 1(2):50–60

    Article  Google Scholar 

  82. Ying KC (2008) An iterated Greedy heuristic for multistage hybrid flowshop scheduling problems with multiprocessor tasks. IEEE Trans Evol Comput 60(6):810–817

    MATH  Google Scholar 

  83. Ying KC (2008) Solving non-permutation flowshop scheduling problems by an effective iterated Greedy heuristic. Int J Adv Manuf Technol 38(3–4):348–354

    Article  Google Scholar 

Download references

Acknowledgements

The author is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project “SCHEYARD – Optimization of Scheduling Problems in Container Yards” (No. DPI2015-65895-R) financed by FEDER funds.

Author information

Authors and Affiliations

  1. Grupo de Sistemas de Optimización Aplicada, Instituto Tecnológico de Informática, Ciudad Politécnica de la Innovación, Edificio 8G, Acc. B. Universitat Politécnica de València, València, Spain

    Rubén Ruiz

Authors
  1. Rubén Ruiz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rubén Ruiz .

Editor information

Editors and Affiliations

  1. Statistics and Operations Research Department, University of Valencia, Valencia, Spain

    Rafael Martí

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  3. Amazon.com, Inc. and University of Washington, Seattle, Washington, USA

    Mauricio G. C. Resende

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Ruiz, R. (2018). Scheduling Heuristics. In: Martí, R., Pardalos, P., Resende, M. (eds) Handbook of Heuristics. Springer, Cham. https://doi.org/10.1007/978-3-319-07124-4_44

Download citation

Publish with us

Policies and ethics

Search

Navigation