Skip to main content
Log in

The Seeds of the NEH Algorithm: An Overview Using Bibliometric Analysis

  • Review
  • Published:
Operations Research Forum Aims and scope Submit manuscript

Abstract

This paper aims to provide a wide overview of the documents that cited the NEH algorithm since it was proposed, in 1983. Such a method is one of the most cited and used algorithms in the scheduling field and, to the best of our knowledge, there is not a bibliometric analysis on the citation of the NEH algorithm by scientific documents over the past 39 years. Hence, we brought a thorough and comprehensive bibliometric analysis to provide a wide overview of two blocks of information, metrics, and knowledge structure of the scientific documents that cited the seminal paper that proposed the mentioned approach. We used all the documents returned from the Scopus database to analyze these blocks. The first block provides an understanding of the characteristics of the sources, authors, and documents of the document collection, and with the second it is possible to analyze the conceptual, intellectual, and social knowledge structures from the selected documents. As a result, we obtained a panorama from 1983 to 2022 of all documents that made a citation of the NEH algorithm, which summed 1936 studies. We discussed the main sources, authors, and documents, individually, as well as the interaction of them and of the institutions, keywords, concepts, and countries. Finally, we pointed out some challenges and research opportunities in this thematic.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

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

Similar content being viewed by others

Availability of Data and Materials

The data sets are available upon request.

Code Availability

The source codes are available upon request.

References

  1. Parente M, Figueira G, Amorim P, Marques A (2020) Production scheduling in the context of industry 4.0: review and trends. Int J Prod Res 58(17):5401–5431

    Google Scholar 

  2. Fuchigami HY, Rangel S (2018) A survey of case studies in production scheduling: analysis and perspectives. J Comput Sci 25:425–436

    Google Scholar 

  3. Pinedo M, Zacharias C, Zhu N (2015) Scheduling in the service industries: an overview. J Syst Sci Syst Eng 24(1):1–48

    Google Scholar 

  4. Michael LP (2018) Scheduling: theory, algorithms, and systems. Springer

  5. 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

    Google Scholar 

  6. Taillard E (1990) Some efficient heuristic methods for the flow shop sequencing problem. Eur J Oper Res 47(1):65–74

    MathSciNet  Google Scholar 

  7. Framinan JM, Leisten R, Ruiz-Usano R (2002) Efficient heuristics for flowshop sequencing with the objectives of makespan and flowtime minimisation. Eur J Oper Res 141(3):559–569

    Google Scholar 

  8. Framinan J, 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 

  9. 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

    MathSciNet  Google Scholar 

  10. Fernandez-Viagas V, Ruiz R, Framinan JM (2017) A new vision of approximate methods for the permutation flowshop to minimise makespan: State-of-the-art and computational evaluation. Eur J Oper Res 257(3):707–721

    MathSciNet  Google Scholar 

  11. Fernandez-Viagas V, Framinan JM (2015) Neh-based heuristics for the permutation flowshop scheduling problem to minimise total tardiness. Comput Oper Res 60:27–36

    MathSciNet  Google Scholar 

  12. Fernandez-Viagas V, Molina-Pariente JM, Framinan JM (2020) Generalised accelerations for insertion-based heuristics in permutation flowshop scheduling. Eur J Oper Res 282(3):858–872

    MathSciNet  Google Scholar 

  13. Nowicki E, Smutnicki C (1996) A fast tabu search algorithm for the permutation flow-shop problem. Eur J Oper Res 91(1):160–175

    Google Scholar 

  14. Armentano VA, Ronconi DP (1999) Tabu search for total tardiness minimization in flowshop scheduling problems. Comput Oper Res 26(3):219–235

    MathSciNet  Google Scholar 

  15. 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

    Google Scholar 

  16. Werner F, Winkler A (1995) Insertion techniques for the heuristic solution of the job shop problem. Discret Appl Math 58(2):191–211

    MathSciNet  Google Scholar 

  17. De Paula MR, Ravetti MG, Mateus GR, Pardalos PM (2007) Solving parallel machines scheduling problems with sequence-dependent setup times using variable neighbourhood search. IMA J Manag Math 18(2):101–115

    MathSciNet  Google Scholar 

  18. Zobolas G, Tarantilis CD, Ioannou G (2009) Solving the open shop scheduling problem via a hybrid genetic-variable neighborhood search algorithm. Cybernetics and Systems: An International Journal 40(4):259–285

    Google Scholar 

  19. 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 

  20. Prata BA, Rodrigues CD, Framinan JM (2022) A differential evolution algorithm for the customer order scheduling problem with sequence-dependent setup times. Expert Syst Appl 189:116097

    Google Scholar 

  21. Teitz MB, Bart P (1968) Heuristic methods for estimating the generalized vertex median of a weighted graph. Oper Res 16(5):955–961

    Google Scholar 

  22. Lin S, Kernighan BW (1973) An effective heuristic algorithm for the traveling-salesman problem. Oper Res 21(2):498–516

    MathSciNet  Google Scholar 

  23. Dalavi AM, Gomes A, Husain AJ (2022) Bibliometric analysis of nature inspired optimization techniques. Comput Ind Eng 169:108161

    Google Scholar 

  24. Di Mascolo M, Martinez C, Espinouse ML (2021) Routing and scheduling in home health care: a literature survey and bibliometric analysis. Comput Ind Eng 158:107255

    Google Scholar 

  25. Eghtesadifard M, Khalifeh M, Khorram M (2020) A systematic review of research themes and hot topics in assembly line balancing through the web of science within 1990–2017. Comput Ind Eng 139:106182

    Google Scholar 

  26. Riahi Y, Saikouk T, Gunasekaran A, Badraoui I (2021) Artificial intelligence applications in supply chain: a descriptive bibliometric analysis and future research directions. Expert Syst Appl 173:114702

    Google Scholar 

  27. Zhou X, Wei X, Lin J, Tian X, Lev B, Wang S (2021) Supply chain management under carbon taxes: a review and bibliometric analysis. Omega 98:102295

    Google Scholar 

  28. Aria M, Cuccurullo C (2017) bibliometrix: An r-tool for comprehensive science mapping analysis. J Informet 11(4):959–975

    Google Scholar 

  29. Bradford SC (1934) Sources of information on specific subjects. Engineering 137:85–86

    Google Scholar 

  30. Lawler EL, Lenstra JK, Kan AHR, Shmoys DB (1993) Sequencing and scheduling: Algorithms and complexity. Handbooks Oper Res Management Sci 4:445–522

    Google Scholar 

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

    Google Scholar 

  32. Pinedo ML (2012) Scheduling, vol 29. Springer

  33. T’kindt V, Billaut JC (2006) Multicriteria scheduling: theory, models and algorithms. Springer Science & Business Media

  34. Reeves CR (1995) A genetic algorithm for flowshop sequencing. Comput Oper Res 22(1):5–13

    ADS  Google Scholar 

  35. Osman IH, Potts C (1989) Simulated annealing for permutation flow-shop scheduling. Omega 17(6):551–557

    Google Scholar 

  36. Tasgetiren MF, Liang YC, Sevkli M, Gencyilmaz G (2007) A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem. Eur J Oper Res 177(3):1930–1947

    Google Scholar 

  37. He Q (1999) Knowledge discovery through co-word analysis. Graduate School of Library and Information Science. University of Illinois ..

  38. Cobo MJ, López-Herrera AG, Herrera-Viedma E, Herrera F (2011) An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. J Informet 5(1):146–166

    Google Scholar 

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

    MathSciNet  Google Scholar 

  40. Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    ADS  MathSciNet  CAS  PubMed  Google Scholar 

  41. Rolim GA, Nagano MS (2020) Structural properties and algorithms for earliness and tardiness scheduling against common due dates and windows: A review. Comput Ind Eng 149:106803

    Google Scholar 

  42. Rossit DA, Tohmé F, Frutos M (2018) The non-permutation flow-shop scheduling problem: a literature review. Omega 77:143–153

    Google Scholar 

  43. Bampis E, Letsios D, Lucarelli G (2015) Green scheduling, flows and matchings. Theoret Comput Sci 579:126–136

    MathSciNet  Google Scholar 

  44. Gahm C, Denz F, Dirr M, Tuma A (2016) Energy-efficient scheduling in manufacturing companies: A review and research framework. Eur J Oper Res 248(3):744–757

    MathSciNet  Google Scholar 

  45. Shabtay D, Steiner G (2007) A survey of scheduling with controllable processing times. Discret Appl Math 155(13):1643–1666

    MathSciNet  Google Scholar 

  46. Fernandez-Viagas V, Framinan JM (2015) Controllable processing times in project and production management: analysing the trade-off between processing times and the amount of resources. Math Probl Eng 2015

  47. Ding J, Song S, Zhang R, Gupta JN, Wu C (2015) Accelerated methods for total tardiness minimisation in no-wait flowshops. Int J Prod Res 53(4):1002–1018

    Google Scholar 

Download references

Funding

This study was financed in part by the Coordination for the Improvement of Higher Education Personnel (CAPES) and the National Council for Scientific and Technological Development (CNPq), through grants 404232/2016-7, 303594/2018-7, 306075/2017-2, 430137/2018-4, 312585/2021-7, and 4071-51/2021-4.

Author information

Authors and Affiliations

Authors

Contributions

Bruno Prata: Conceptualization, Methodology, Investigation, Software, Formal analysis, Writing - original draft, Visualization, Writing - Review & Editing, Funding. Marcelo Nagano: Conceptualization, Validation, Writing - Review & Editing, Supervision, Project administration. Nádia Fróes: Conceptualization, Methodology, Validation, Writing - Review & Editing, Supervision. Levi Abreu: Conceptualization, Validation.

Corresponding author

Correspondence to Levi Ribeiro de Abreu.

Ethics declarations

Ethics Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Competing Interests

The authors declare no competing interests.

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

de Athayde Prata, B., Nagano, M.S., Martarelli Fróes, N.J. et al. The Seeds of the NEH Algorithm: An Overview Using Bibliometric Analysis. Oper. Res. Forum 4, 98 (2023). https://doi.org/10.1007/s43069-023-00276-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s43069-023-00276-7

Keywords

Navigation