IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v143y2006i1p237-25010.1007-s10479-006-7385-y.html
   My bibliography  Save this article

A new approach based on the surrogating method in the project time compression problems

Author

Listed:
  • Hadi Bidhandi

Abstract

This paper develops a mathematical model for project time compression problems in CPM/PERT type networks. It is noted this formulation of the problem will be an adequate approximation for solving the time compression problem with any continuous and non-increasing time-cost curve. The kind of this model is Mixed Integer Linear Program (MILP) with zero-one variables, and the Benders' decomposition procedure for analyzing this model has been developed. Then this paper proposes a new approach based on the surrogating method for solving these problems. In addition, the required computer programs have been prepared by the author to execute the algorithm. An illustrative example solved by the new algorithm, and two methods are compared by several numerical examples. Computational experience with these data shows the superiority of the new approach. Copyright Springer Science + Business Media, Inc. 2006

Suggested Citation

  • Hadi Bidhandi, 2006. "A new approach based on the surrogating method in the project time compression problems," Annals of Operations Research, Springer, vol. 143(1), pages 237-250, March.
    • Handle: RePEc:spr:annopr:v:143:y:2006:i:1:p:237-250:10.1007/s10479-006-7385-y
    DOI: 10.1007/s10479-006-7385-y
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-006-7385-y
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-006-7385-y?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. Holmberg, Kaj, 1999. "Exact solution methods for uncapacitated location problems with convex transportation costs," European Journal of Operational Research, Elsevier, vol. 114(1), pages 127-140, April.
    2. Krishan Rana & R. G. Vickson, 1988. "A Model and Solution Algorithm for Optimal Routing of a Time-Chartered Containership," Transportation Science, INFORMS, vol. 22(2), pages 83-95, May.
    3. Fred Glover, 1975. "Surrogate Constraint Duality in Mathematical Programming," Operations Research, INFORMS, vol. 23(3), pages 434-451, June.
    4. Holmberg, Kaj, 1994. "On using approximations of the Benders master problem," European Journal of Operational Research, Elsevier, vol. 77(1), pages 111-125, August.
    5. Moustapha Diaby, 1993. "Implicit Enumeration for the Pure Integer 0/1 Minimax Programming Problem," Operations Research, INFORMS, vol. 41(6), pages 1172-1176, December.
    6. Cote, Gilles & Laughton, Michael A., 1984. "Large-scale mixed integer programming: Benders-type heuristics," European Journal of Operational Research, Elsevier, vol. 16(3), pages 327-333, June.
    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. Georgios Saharidis & Marianthi Ierapetritou, 2013. "Speed-up Benders decomposition using maximum density cut (MDC) generation," Annals of Operations Research, Springer, vol. 210(1), pages 101-123, November.
    2. Rahmaniani, Ragheb & Crainic, Teodor Gabriel & Gendreau, Michel & Rei, Walter, 2017. "The Benders decomposition algorithm: A literature review," European Journal of Operational Research, Elsevier, vol. 259(3), pages 801-817.
    3. Teodor Gabriel Crainic & Mike Hewitt & Francesca Maggioni & Walter Rei, 2021. "Partial Benders Decomposition: General Methodology and Application to Stochastic Network Design," Transportation Science, INFORMS, vol. 55(2), pages 414-435, March.
    4. Walter Rei & Jean-François Cordeau & Michel Gendreau & Patrick Soriano, 2009. "Accelerating Benders Decomposition by Local Branching," INFORMS Journal on Computing, INFORMS, vol. 21(2), pages 333-345, May.
    5. Munoz, F.D. & Hobbs, B.F. & Watson, J.-P., 2016. "New bounding and decomposition approaches for MILP investment problems: Multi-area transmission and generation planning under policy constraints," European Journal of Operational Research, Elsevier, vol. 248(3), pages 888-898.
    6. M. Jenabi & S. M. T. Fatemi Ghomi & S. A. Torabi & Moeen Sammak Jalali, 2022. "An accelerated Benders decomposition algorithm for stochastic power system expansion planning using sample average approximation," OPSEARCH, Springer;Operational Research Society of India, vol. 59(4), pages 1304-1336, December.
    7. Peiling Wu & Joseph C. Hartman & George R. Wilson, 2005. "An Integrated Model and Solution Approach for Fleet Sizing with Heterogeneous Assets," Transportation Science, INFORMS, vol. 39(1), pages 87-103, February.
    8. John N. Hooker, 2002. "Logic, Optimization, and Constraint Programming," INFORMS Journal on Computing, INFORMS, vol. 14(4), pages 295-321, November.
    9. Renaud Chicoisne, 2023. "Computational aspects of column generation for nonlinear and conic optimization: classical and linearized schemes," Computational Optimization and Applications, Springer, vol. 84(3), pages 789-831, April.
    10. Azad, Nader & Hassini, Elkafi, 2019. "Recovery strategies from major supply disruptions in single and multiple sourcing networks," European Journal of Operational Research, Elsevier, vol. 275(2), pages 481-501.
    11. Pourbabai, B. & Ashayeri, J. & Van Wassenhove, L.N., 1992. "Strategic marketing, production, and distribution planning of an integrated manufacturing system," Other publications TiSEM 16c2bacb-2c2b-427e-b429-c, Tilburg University, School of Economics and Management.
    12. Li, Han-Lin & Fang, Shu-Cherng & Huang, Yao-Huei & Nie, Tiantian, 2016. "An enhanced logarithmic method for signomial programming with discrete variables," European Journal of Operational Research, Elsevier, vol. 255(3), pages 922-934.
    13. Y.M. Ermoliev & A.V. Kryazhimskii & A. Ruszczynski, 1995. "Constraint Aggregation Principle in Convex Optimization," Working Papers wp95015, International Institute for Applied Systems Analysis.
    14. M. A. Venkataramana & John J. Dinkel & John Mote, 1991. "Vector processing approach to constrained network problems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 38(1), pages 71-85, February.
    15. Mina Husseinzadeh Kashan & Ali Husseinzadeh Kashan & Nasim Nahavandi, 2013. "A novel differential evolution algorithm for binary optimization," Computational Optimization and Applications, Springer, vol. 55(2), pages 481-513, June.
    16. Ludwig Kuznia & Bo Zeng & Grisselle Centeno & Zhixin Miao, 2013. "Stochastic optimization for power system configuration with renewable energy in remote areas," Annals of Operations Research, Springer, vol. 210(1), pages 411-432, November.
    17. Selcuk Karabati & Panagiotis Kouvelis & Gang Yu, 2001. "A Min-Max-Sum Resource Allocation Problem and Its Applications," Operations Research, INFORMS, vol. 49(6), pages 913-922, December.
    18. Jain, A. S. & Meeran, S., 1999. "Deterministic job-shop scheduling: Past, present and future," European Journal of Operational Research, Elsevier, vol. 113(2), pages 390-434, March.
    19. Siddhartha Syam & Bala Shetty, 1998. "Coordinated replenishments from multiple suppliers with price discounts," Naval Research Logistics (NRL), John Wiley & Sons, vol. 45(6), pages 579-598, September.
    20. Paola Cappanera & Marco Trubian, 2005. "A Local-Search-Based Heuristic for the Demand-Constrained Multidimensional Knapsack Problem," INFORMS Journal on Computing, INFORMS, vol. 17(1), pages 82-98, February.

    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:spr:annopr:v:143:y:2006:i:1:p:237-250:10.1007/s10479-006-7385-y. 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.springer.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.