Skip to main content
SpringerLink
Log in

Further Research and Extensions

  • Chapter
  • First Online:
Grouping Genetic Algorithms

Part of the book series: Studies in Computational Intelligence ((SCI,volume 666))

  • 1232 Accesses

Abstract

Advances and applications of grouping genetic algorithms (GGAs) have been proposed, tested, and applied to a wide range of grouping problems from various disciplines in industry. Computational results have shown that the performance of the algorithms is promising, in terms of efficiency and solution quality. However, further extensions to the algorithms and their applications are still quite possible; it will be interesting to look into possible future developments of the algorithms and the further widening of the scope of their applications. In this vein, this chapter first highlights some of the possible further advances and extensions to various genetic techniques in grouping genetic algorithms and its variants, and the possible investigative experiments that may be carried out to test the specific techniques. Future potential applications of these GGA techniques are then presented. It is hoped that the suggested extensions will significantly go a long way to further advance the research in grouping genetic algorithms.

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 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
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

  • Agustın-Blas LE, Salcedo-Sanz S, Vidales P, Urueta G, Portilla-Figueras JA (2011) Near optimal citywide WiFi network deployment using a hybrid grouping genetic algorithm. Expert Syst Appl 38(8):9543–9556

    Article  Google Scholar 

  • Avci M, Topaloglu S (2016) A hybrid metaheuristic algorithm for heterogeneous vehicle routing problem with simultaneous pickup and delivery. Expert Syst Appl 53:160–171

    Article  Google Scholar 

  • Burke EK, Bykov Y, Petrovic S (2001) A multi-criteria approach to examination timetabling. In: Burke EK, Erben W (eds) Practice and theory of automated timetabling: selected papers from the 3rd international conference. Lecture Notes in Computer Science 2079, pp 118–131

    Google Scholar 

  • Chen AL, Martinez DH (2012) A heuristic method based on genetic algorithm for the baseline-product design. Expert Syst Appl 39(5):5829–5837

    Article  Google Scholar 

  • Chen Y, Fan Z-P, Ma J, Zeng S (2011) A hybrid grouping genetic algorithm for reviewer group construction problem. Expert Syst Appl 38:2401–2411

    Article  Google Scholar 

  • de Jonge B, Klingenberg W, Teunter R, Tinga T (2016) Reducing costs by clustering maintenance activities for multiple critical units. Reliab Eng Syst Saf 145:93–103

    Article  Google Scholar 

  • Dereli T, Baykasoglu A, Das GS (2007) Fuzzy quality-team formation for value added auditing: a case study. J Eng Tech Manage 24(4):366–394

    Article  Google Scholar 

  • Gunn EA, Diallo C (2015) Optimal opportunistic indirect grouping of preventive replacements in multicomponent systems. Comput Ind Eng 90:281–291

    Article  Google Scholar 

  • Ho GTS, Ip WH, Lee CKM, Mou WL (2012) Customer grouping for better resources allocation using GA based clustering technique. Expert Syst Appl 39:1979–1987

    Article  Google Scholar 

  • Höglund H (2013) Estimating discretionary accruals using a grouping genetic algorithm. Expert Syst Appl 40:2366–2372

    Article  Google Scholar 

  • Kalaycılar EG, AzizoÄŸlu M, Yeralan S (2016) A disassembly line balancing problem with fixed number of workstations. Eur J Oper Res 249(2):592–604

    Article  MathSciNet  Google Scholar 

  • Kashan AH, Akbari AA, Ostadi B (2015) Grouping evolution strategies: an effective approach for grouping problems. Appl Math Model 39(9):2703–2720

    Article  MathSciNet  Google Scholar 

  • Landa-Torres I, Gil-Lopez S, Del Ser J, Salcedo-Sanz S, Manjarres D, Portilla-Figueras JA (2013) Efficient citywide planning of open WiFi access networks using novel grouping har-mony search heuristics. Eng Appl Artif Intell 26:1124–1130

    Article  Google Scholar 

  • Li F, Ma L, Sun Yong, Mathew J (2013) Group maintenance scheduling: a case study for a pipeline network. In: Engineering Asset Management 2011. Proceedings of the sixth annual world congress on engineering asset management (Lecture Notes in Mechanical Engineering), Springer, Duke Energy Center, Ohio, pp 163–177

    Google Scholar 

  • Liu H, Xu Z, Abraham A (2005) Hybrid fuzzy-genetic algorithm approach for crew grouping. In: Proceedings of the 5th international conference on intelligent systems design and applications (ISDA’05), pp 332–337

    Google Scholar 

  • Mutingi M, Mbohwa C (2014) A Fuzzy-based particle swarm optimization approach for task assignment in home healthcare. S Afr J Ind Eng 25(3):84–95

    Google Scholar 

  • Mutingi M, Mbohwa C (2016) Fuzzy grouping genetic algorithm for homecare staff scheduling. In: Mutingi M and Mbohwa C (ed) Healthcare staff scheduling: emerging fuzzy optimization approaches, 1st edn. CRC Press, Taylor & Francis, New York, pp 119–136

    Google Scholar 

  • Onwubolu GC, Mutingi M (2001) A genetic algorithm approach to cellular manufacturing systems. Comput Ind Eng 39:125–144

    Article  MATH  Google Scholar 

  • Strnad D, Guid N (2010) A Fuzzy-Genetic decision support system for project team formation. Appl Soft Comput 10(4):1178–1187

    Google Scholar 

  • Torabi SA, Fatemi Ghomi SMT, Karimi B (2006) A hybrid genetic algorithm for the finite horizon economic lot and delivery scheduling in supply chains. Eur J Oper Res 173:173–189

    Article  MathSciNet  MATH  Google Scholar 

  • Toth P, Vigo D (2002) The vehicle routing problem. SIAM Monograph on Discrete Mathematics and Applications, Philadelphia

    Book  MATH  Google Scholar 

  • Van Do P, Barros A, Bérenguer C, Bouvard K, Brissaud F (2013) Dynamic grouping maintenance with time limited opportunities. Reliab Eng Syst Saf 120:51–59

    Article  Google Scholar 

  • Vidalis MI, Papadopoulosb CT, Heavey C (2005) On the workload and phaseload allocation problems of short reliable production lines with finite buffers. Comput Ind Eng 48(4):825–837

    Article  Google Scholar 

  • Vroblefski M, Brown EC (2006) A grouping genetic algorithm for registration area planning. Omega 34:220–230

    Article  Google Scholar 

  • Weitz RR, Jelassi MT (1992) Assigning students to groups: a multi-criteria decision support system approach. Decis Sci 23:746–757

    Article  Google Scholar 

  • Weitz RR, Lakshminarayanan S (1996) On a heuristic for the final exam scheduling problem. J Oper Res Soc 47:599–600

    Article  Google Scholar 

  • Yu S, Yang Q, Tao J, Tian X, Yin F (2011) Product modular design incorporating life cycle issues—group genetic algorithm (GGA) based method. J Clean Prod 19(9–10):1016–1032

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, Namibia University of Science and Technology, Windhoek, Namibia

    Michael Mutingi

  2. Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa

    Charles Mbohwa

Authors
  1. Michael Mutingi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Charles Mbohwa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael Mutingi .

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Mutingi, M., Mbohwa, C. (2017). Further Research and Extensions. In: Grouping Genetic Algorithms. Studies in Computational Intelligence, vol 666. Springer, Cham. https://doi.org/10.1007/978-3-319-44394-2_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-44394-2_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44393-5

  • Online ISBN: 978-3-319-44394-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation