IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v58y2007i1d10.1057_palgrave.jors.2602132.html
   My bibliography  Save this article

AI and OR in management of operations: history and trends

Author

Listed:
  • K A H Kobbacy

    (University of Salford)

  • S Vadera

    (University of Salford)

  • M H Rasmy

    (Cairo University)

Abstract

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested.

Suggested Citation

  • K A H Kobbacy & S Vadera & M H Rasmy, 2007. "AI and OR in management of operations: history and trends," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(1), pages 10-28, January.
    • Handle: RePEc:pal:jorsoc:v:58:y:2007:i:1:d:10.1057_palgrave.jors.2602132
    DOI: 10.1057/palgrave.jors.2602132
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/palgrave.jors.2602132
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/palgrave.jors.2602132?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cai, X. & Li, K. N., 2000. "A genetic algorithm for scheduling staff of mixed skills under multi-criteria," European Journal of Operational Research, Elsevier, vol. 125(2), pages 359-369, September.
    2. Easton, Fred F. & Mansour, Nashat, 1999. "A distributed genetic algorithm for deterministic and stochastic labor scheduling problems," European Journal of Operational Research, Elsevier, vol. 118(3), pages 505-523, November.
    3. Bayraktar, Demet, 1998. "A knowledge-based expert system approach for the auditing process of some elements in the quality assurance system," International Journal of Production Economics, Elsevier, vol. 56(1), pages 37-46, September.
    4. Chan, Chi Kin & Cheung, Bernard K. -S. & Langevin, André, 2003. "Solving the multi-buyer joint replenishment problem with a modified genetic algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 37(3), pages 291-299, March.
    5. Herbert A. Simon, 1987. "Two Heads Are Better than One: The Collaboration between AI and OR," Interfaces, INFORMS, vol. 17(4), pages 8-15, August.
    6. Al-Najjar, Basim & Alsyouf, Imad, 2003. "Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making," International Journal of Production Economics, Elsevier, vol. 84(1), pages 85-100, April.
    7. Bogataj, Marija & Usenik, Janez, 2005. "Fuzzy approach to the spatial games in the total market area," International Journal of Production Economics, Elsevier, vol. 93(1), pages 493-503, January.
    8. Mattfeld, Dirk C. & Bierwirth, Christian, 2004. "An efficient genetic algorithm for job shop scheduling with tardiness objectives," European Journal of Operational Research, Elsevier, vol. 155(3), pages 616-630, June.
    9. Lee, H. C. & Dagli, Cihan H., 1997. "A parallel genetic-neuro scheduler for job-shop scheduling problems," International Journal of Production Economics, Elsevier, vol. 51(1-2), pages 115-122, August.
    10. Kenneth Fordyce & Peter Norden & Gerald Sullivan, 1987. "Artificial Intelligence and the Management Science Practitioner: Links between Operations Research and Expert Systems," Interfaces, INFORMS, vol. 17(4), pages 34-40, August.
    11. Baker, Barrie M., 1999. "A spreadsheet modelling approach to the assortment problem," European Journal of Operational Research, Elsevier, vol. 114(1), pages 83-92, April.
    12. Malmborg, Charles J., 1996. "A genetic algorithm for service level based vehicle scheduling," European Journal of Operational Research, Elsevier, vol. 93(1), pages 121-134, August.
    13. Chanas, Stefan & Kasperski, Adam, 2003. "On two single machine scheduling problems with fuzzy processing times and fuzzy due dates," European Journal of Operational Research, Elsevier, vol. 147(2), pages 281-296, June.
    14. Sakawa, Masatoshi & Kubota, Ryo, 2000. "Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms," European Journal of Operational Research, Elsevier, vol. 120(2), pages 393-407, January.
    15. Adamopoulos, George I. & Pappis, Costas P., 1996. "A fuzzy-linguistic approach to a multi-criteria sequencing problem," European Journal of Operational Research, Elsevier, vol. 92(3), pages 628-636, August.
    16. Ben-Nakhi, Abdullatif E. & Mahmoud, Mohamed A., 2002. "Energy conservation in buildings through efficient A/C control using neural networks," Applied Energy, Elsevier, vol. 73(1), pages 5-23, September.
    17. Khouja, Moutaz & Michalewicz, Zgibniew & Wilmot, Michael, 1998. "The use of genetic algorithms to solve the economic lot size scheduling problem," European Journal of Operational Research, Elsevier, vol. 110(3), pages 509-524, November.
    18. Nearchou, A.C.Andreas C., 2004. "The effect of various operators on the genetic search for large scheduling problems," International Journal of Production Economics, Elsevier, vol. 88(2), pages 191-203, March.
    19. Kurz, Mary E. & Askin, Ronald G., 2004. "Scheduling flexible flow lines with sequence-dependent setup times," European Journal of Operational Research, Elsevier, vol. 159(1), pages 66-82, November.
    20. Ishii, Hiroaki & Tada, Minoru, 1995. "Single machine scheduling problem with fuzzy precedence relation," European Journal of Operational Research, Elsevier, vol. 87(2), pages 284-288, December.
    21. Caraffa, Vince & Ianes, Stefano & P. Bagchi, Tapan & Sriskandarajah, Chelliah, 2001. "Minimizing makespan in a blocking flowshop using genetic algorithms," International Journal of Production Economics, Elsevier, vol. 70(2), pages 101-115, March.
    22. González-Uriel, Ana & Roanes-Lozano, Eugenio, 2004. "A knowledge-based system for house layout selection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 66(1), pages 43-54.
    23. Samanta, B. & Al-Araimi, S. A., 2001. "An inventory control model using fuzzy logic," International Journal of Production Economics, Elsevier, vol. 73(3), pages 217-226, October.
    24. Cavory, G. & Dupas, R. & Goncalves, G., 2001. "A genetic approach to the scheduling of preventive maintenance tasks on a single product manufacturing production line," International Journal of Production Economics, Elsevier, vol. 74(1-3), pages 135-146, December.
    25. Hwang, Hark & Choi, Bum & Lee, Min-Jin, 2005. "A model for shelf space allocation and inventory control considering location and inventory level effects on demand," International Journal of Production Economics, Elsevier, vol. 97(2), pages 185-195, August.
    26. Dagli, C. H. & Sittisathanchai, S., 1995. "Genetic neuro-scheduler: A new approach for job shop scheduling," International Journal of Production Economics, Elsevier, vol. 41(1-3), pages 135-145, October.
    27. Chambers, M. & Mount-Campbell, C. A., 2002. "Process optimization via neural network metamodeling," International Journal of Production Economics, Elsevier, vol. 79(2), pages 93-100, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shivam Gupta & Sachin Modgil & Samadrita Bhattacharyya & Indranil Bose, 2022. "Artificial intelligence for decision support systems in the field of operations research: review and future scope of research," Annals of Operations Research, Springer, vol. 308(1), pages 215-274, January.
    2. Radu Valentin & Croitoru Ionut Marius & Tabirca Alina Iuliana & Stoica Silviu-Ionel, 2023. "Ai Components For Performance Measurement - A Bibliometric Approach," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 6, pages 286-300, December.
    3. Ławrynowicz Anna, 2011. "Genetic Algorithms for Solving Scheduling Problems in Manufacturing Systems," Foundations of Management, Sciendo, vol. 3(2), pages 7-26, January.
    4. Osório, António (António Miguel) & Pinto, Alberto Adrego, 2019. "Information, uncertainty and the manipulability of artifcial intelligence autonomous vehicles systems," Working Papers 2072/376028, Universitat Rovira i Virgili, Department of Economics.
    5. Wong, Bo K. & Lai, Vincent S., 2011. "A survey of the application of fuzzy set theory in production and operations management: 1998-2009," International Journal of Production Economics, Elsevier, vol. 129(1), pages 157-168, January.
    6. Ariel K. H. Lui & Maggie C. M. Lee & Eric W. T. Ngai, 2022. "Impact of artificial intelligence investment on firm value," Annals of Operations Research, Springer, vol. 308(1), pages 373-388, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. J. Behnamian, 2016. "Survey on fuzzy shop scheduling," Fuzzy Optimization and Decision Making, Springer, vol. 15(3), pages 331-366, September.
    2. Sanja Petrovic & Carole Fayad & Dobrila Petrovic & Edmund Burke & Graham Kendall, 2008. "Fuzzy job shop scheduling with lot-sizing," Annals of Operations Research, Springer, vol. 159(1), pages 275-292, March.
    3. Wong, Bo K. & Lai, Vincent S., 2011. "A survey of the application of fuzzy set theory in production and operations management: 1998-2009," International Journal of Production Economics, Elsevier, vol. 129(1), pages 157-168, January.
    4. Kumar, Akhilesh & Prakash & Tiwari, M.K. & Shankar, Ravi & Baveja, Alok, 2006. "Solving machine-loading problem of a flexible manufacturing system with constraint-based genetic algorithm," European Journal of Operational Research, Elsevier, vol. 175(2), pages 1043-1069, December.
    5. Ozelkan, Ertunga C. & Duckstein, Lucien, 1999. "Optimal fuzzy counterparts of scheduling rules," European Journal of Operational Research, Elsevier, vol. 113(3), pages 593-609, March.
    6. Jones, D. F. & Mirrazavi, S. K. & Tamiz, M., 2002. "Multi-objective meta-heuristics: An overview of the current state-of-the-art," European Journal of Operational Research, Elsevier, vol. 137(1), pages 1-9, February.
    7. Zhen Song & Håkan Schunnesson & Mikael Rinne & John Sturgul, 2015. "Intelligent Scheduling for Underground Mobile Mining Equipment," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-21, June.
    8. Bozorgirad, Mir Abbas & Logendran, Rasaratnam, 2013. "Bi-criteria group scheduling in hybrid flowshops," International Journal of Production Economics, Elsevier, vol. 145(2), pages 599-612.
    9. Anurag Agarwal & Varghese S. Jacob & Hasan Pirkul, 2006. "An Improved Augmented Neural-Network Approach for Scheduling Problems," INFORMS Journal on Computing, INFORMS, vol. 18(1), pages 119-128, February.
    10. Castillo, Ignacio & Joro, Tarja & Li, Yong Yue, 2009. "Workforce scheduling with multiple objectives," European Journal of Operational Research, Elsevier, vol. 196(1), pages 162-170, July.
    11. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    12. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
    13. B Dengiz & C Alabas-Uslu & O Dengiz, 2009. "Optimization of manufacturing systems using a neural network metamodel with a new training approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(9), pages 1191-1197, September.
    14. Nouha Nouri & Talel Ladhari, 2018. "Evolutionary multiobjective optimization for the multi-machine flow shop scheduling problem under blocking," Annals of Operations Research, Springer, vol. 267(1), pages 413-430, August.
    15. Bianchi-Aguiar, Teresa & Hübner, Alexander & Carravilla, Maria Antónia & Oliveira, José Fernando, 2021. "Retail shelf space planning problems: A comprehensive review and classification framework," European Journal of Operational Research, Elsevier, vol. 289(1), pages 1-16.
    16. Goopyo Hong & Byungseon Sean Kim, 2018. "Development of a Data-Driven Predictive Model of Supply Air Temperature in an Air-Handling Unit for Conserving Energy," Energies, MDPI, vol. 11(2), pages 1-16, February.
    17. Ostermeier, Manuel & Düsterhöft, Tobias & Hübner, Alexander, 2021. "A model and solution approach for store-wide shelf space allocation," Omega, Elsevier, vol. 102(C).
    18. Pan, Quan-Ke & Gao, Liang & Li, Xin-Yu & Gao, Kai-Zhou, 2017. "Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times," Applied Mathematics and Computation, Elsevier, vol. 303(C), pages 89-112.
    19. Chan, Chi Kin & Lee, Y.C.E. & Campbell, J.F., 2013. "Environmental performance—Impacts of vendor–buyer coordination," International Journal of Production Economics, Elsevier, vol. 145(2), pages 683-695.
    20. Baraldi, Piero & Podofillini, Luca & Mkrtchyan, Lusine & Zio, Enrico & Dang, Vinh N., 2015. "Comparing the treatment of uncertainty in Bayesian networks and fuzzy expert systems used for a human reliability analysis application," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 176-193.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorsoc:v:58:y:2007:i:1:d:10.1057_palgrave.jors.2602132. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.