IDEAS home Printed from https://ideas.repec.org/a/wly/navres/v58y2011i2p73-82.html
   My bibliography  Save this article

A genetic algorithm with neighborhood search for the resource‐constrained project scheduling problem

Author

Listed:
  • Sepehr Proon
  • Mingzhou Jin

Abstract

The resource‐constrained project scheduling problem (RCPSP) consists of a set of non‐preemptive activities that follow precedence relationship and consume resources. Under the limited amount of the resources, the objective of RCPSP is to find a schedule of the activities to minimize the project makespan. This article presents a new genetic algorithm (GA) by incorporating a local search strategy in GA operators. The local search strategy improves the efficiency of searching the solution space while keeping the randomness of the GA approach. Extensive numerical experiments show that the proposed GA with neighborhood search works well regarding solution quality and computational time compared with existing algorithms in the RCPSP literature, especially for the instances with a large number of activities. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011

Suggested Citation

  • Sepehr Proon & Mingzhou Jin, 2011. "A genetic algorithm with neighborhood search for the resource‐constrained project scheduling problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 58(2), pages 73-82, March.
  • Handle: RePEc:wly:navres:v:58:y:2011:i:2:p:73-82
    DOI: 10.1002/nav.20439
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/nav.20439
    Download Restriction: no

    File URL: https://libkey.io/10.1002/nav.20439?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
    ---><---

    References listed on IDEAS

    as
    1. Mireille Palpant & Christian Artigues & Philippe Michelon, 2004. "LSSPER: Solving the Resource-Constrained Project Scheduling Problem with Large Neighbourhood Search," Annals of Operations Research, Springer, vol. 131(1), pages 237-257, October.
    2. Klein, Robert, 1999. "Computing lower bounds by destructive improvement - an application to resource-constrained project scheduling," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 10913, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    3. Kolisch, R. & Padman, R., 2001. "An integrated survey of deterministic project scheduling," Omega, Elsevier, vol. 29(3), pages 249-272, June.
    4. Valls, Vicente & Ballestin, Francisco & Quintanilla, Sacramento, 2008. "A hybrid genetic algorithm for the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 185(2), pages 495-508, March.
    5. Valls, Vicente & Ballestin, Francisco & Quintanilla, Sacramento, 2005. "Justification and RCPSP: A technique that pays," European Journal of Operational Research, Elsevier, vol. 165(2), pages 375-386, September.
    6. Bouleimen, K. & Lecocq, H., 2003. "A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version," European Journal of Operational Research, Elsevier, vol. 149(2), pages 268-281, September.
    7. Herroelen, Willy S. & Van Dommelen, Patrick & Demeulemeester, Erik L., 1997. "Project network models with discounted cash flows a guided tour through recent developments," European Journal of Operational Research, Elsevier, vol. 100(1), pages 97-121, July.
    8. Vicente Valls & Francisco Ballestín & Sacramento Quintanilla, 2004. "A Population-Based Approach to the Resource-Constrained Project Scheduling Problem," Annals of Operations Research, Springer, vol. 131(1), pages 305-324, October.
    9. Sönke Hartmann, 1998. "A competitive genetic algorithm for resource‐constrained project scheduling," Naval Research Logistics (NRL), John Wiley & Sons, vol. 45(7), pages 733-750, October.
    10. Rainer Kolisch & Arno Sprecher & Andreas Drexl, 1995. "Characterization and Generation of a General Class of Resource-Constrained Project Scheduling Problems," Management Science, INFORMS, vol. 41(10), pages 1693-1703, October.
    11. Kolisch, Rainer, 1996. "Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation," European Journal of Operational Research, Elsevier, vol. 90(2), pages 320-333, April.
    12. Rainer Kolisch & Andreas Drexl, 1996. "Adaptive search for solving hard project scheduling problems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 43(1), pages 23-40, February.
    13. Klein, Robert & Scholl, Armin, 1999. "Computing lower bounds by destructive improvement: An application to resource-constrained project scheduling," European Journal of Operational Research, Elsevier, vol. 112(2), pages 322-346, January.
    14. Kolisch, Rainer & Sprecher, Arno, 1996. "PSPLIB - a project scheduling problem library," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 396, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    15. Pilar Tormos & Antonio Lova, 2001. "A Competitive Heuristic Solution Technique for Resource-Constrained Project Scheduling," Annals of Operations Research, Springer, vol. 102(1), pages 65-81, February.
    16. Kolisch, Rainer & Hartmann, Sonke, 2006. "Experimental investigation of heuristics for resource-constrained project scheduling: An update," European Journal of Operational Research, Elsevier, vol. 174(1), pages 23-37, October.
    17. A Sprecher, 2002. "Network decomposition techniques for resource-constrained project scheduling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(4), pages 405-414, April.
    Full references (including those not matched with items on IDEAS)

    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. Dieter Debels & Mario Vanhoucke, 2007. "A Decomposition-Based Genetic Algorithm for the Resource-Constrained Project-Scheduling Problem," Operations Research, INFORMS, vol. 55(3), pages 457-469, June.
    2. Kolisch, Rainer & Hartmann, Sonke, 2006. "Experimental investigation of heuristics for resource-constrained project scheduling: An update," European Journal of Operational Research, Elsevier, vol. 174(1), pages 23-37, October.
    3. Valls, Vicente & Ballestin, Francisco & Quintanilla, Sacramento, 2008. "A hybrid genetic algorithm for the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 185(2), pages 495-508, March.
    4. Peteghem, Vincent Van & Vanhoucke, Mario, 2010. "A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 201(2), pages 409-418, March.
    5. Weglarz, Jan & Józefowska, Joanna & Mika, Marek & Waligóra, Grzegorz, 2011. "Project scheduling with finite or infinite number of activity processing modes - A survey," European Journal of Operational Research, Elsevier, vol. 208(3), pages 177-205, February.
    6. Coelho, José & Vanhoucke, Mario, 2011. "Multi-mode resource-constrained project scheduling using RCPSP and SAT solvers," European Journal of Operational Research, Elsevier, vol. 213(1), pages 73-82, August.
    7. André Schnabel & Carolin Kellenbrink & Stefan Helber, 2018. "Profit-oriented scheduling of resource-constrained projects with flexible capacity constraints," Business Research, Springer;German Academic Association for Business Research, vol. 11(2), pages 329-356, September.
    8. Sönke Hartmann, 2002. "A self‐adapting genetic algorithm for project scheduling under resource constraints," Naval Research Logistics (NRL), John Wiley & Sons, vol. 49(5), pages 433-448, August.
    9. Moumene, Khaled & Ferland, Jacques A., 2009. "Activity list representation for a generalization of the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 199(1), pages 46-54, November.
    10. Zamani, Reza, 2013. "A competitive magnet-based genetic algorithm for solving the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 229(2), pages 552-559.
    11. Hartmann, Sönke & Briskorn, Dirk, 2010. "A survey of variants and extensions of the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 207(1), pages 1-14, November.
    12. Van Peteghem, Vincent & Vanhoucke, Mario, 2014. "An experimental investigation of metaheuristics for the multi-mode resource-constrained project scheduling problem on new dataset instances," European Journal of Operational Research, Elsevier, vol. 235(1), pages 62-72.
    13. Zhenyuan Liu & Lei Xiao & Jing Tian, 2016. "An activity-list-based nested partitions algorithm for resource-constrained project scheduling," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4744-4758, August.
    14. Hartmann, Sönke & Briskorn, Dirk, 2008. "A survey of variants and extensions of the resource-constrained project scheduling problem," Working Paper Series 02/2008, Hamburg School of Business Administration (HSBA).
    15. Chen, Jiaqiong & Askin, Ronald G., 2009. "Project selection, scheduling and resource allocation with time dependent returns," European Journal of Operational Research, Elsevier, vol. 193(1), pages 23-34, February.
    16. Debels, Dieter & De Reyck, Bert & Leus, Roel & Vanhoucke, Mario, 2006. "A hybrid scatter search/electromagnetism meta-heuristic for project scheduling," European Journal of Operational Research, Elsevier, vol. 169(2), pages 638-653, March.
    17. Jürgen Kuster & Dietmar Jannach & Gerhard Friedrich, 2010. "Applying Local Rescheduling in response to schedule disruptions," Annals of Operations Research, Springer, vol. 180(1), pages 265-282, November.
    18. Anıl Can & Gündüz Ulusoy, 2014. "Multi-project scheduling with two-stage decomposition," Annals of Operations Research, Springer, vol. 217(1), pages 95-116, June.
    19. Guo, Weikang & Vanhoucke, Mario & Coelho, José, 2023. "A prediction model for ranking branch-and-bound procedures for the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 306(2), pages 579-595.
    20. Zhengwen He & Nengmin Wang & Pengxiang Li, 2014. "Simulated annealing for financing cost distribution based project payment scheduling from a joint perspective," Annals of Operations Research, Springer, vol. 213(1), pages 203-220, February.

    More about this item

    Statistics

    Access and download statistics

    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:wly:navres:v:58:y:2011:i:2:p:73-82. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1520-6750 .

    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.