IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v266y2015icp1038-1049.html
   My bibliography  Save this article

Modified FGP approach for multi-level multi objective linear fractional programming problems

Author

Listed:
  • Lachhwani, Kailash

Abstract

In this paper, we present a new modified method for solving multi-level multi objective linear fractional programming problems (ML-MOLFPPs) based on fuzzy goal programming (FGP) approach with some modifications in the algorithm suggested by Baky (2010) [18] which dealt with multi-level multi objective linear programming problem (ML-MOLPP). In proposed modified approach, numerator and denominator function of each objective at each level are individually transformed into fuzzy goals and their aspiration levels are determined using individual best solutions. Different linear membership functions are defined for numerator and denominator function of each objective function. Then highest degree of each of these membership goals is achieved by minimising the sum of negative deviational variables. The proposed algorithm simplifies the ML-MOLFPP by eliminating solution preferences by the decision makers at each level, thereby avoiding difficulties associate with multi-level programming problems and decision deadlock situations. The aim of this paper is to present simple and efficient technique to obtain compromise optimal solution of ML-MOLFP problems. Numerical examples are illustrated in order to support the proposed modified FGP technique.

Suggested Citation

  • Lachhwani, Kailash, 2015. "Modified FGP approach for multi-level multi objective linear fractional programming problems," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 1038-1049.
  • Handle: RePEc:eee:apmaco:v:266:y:2015:i:c:p:1038-1049
    DOI: 10.1016/j.amc.2015.06.027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300315008085
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2015.06.027?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. Anandalingam, G. & Apprey, Victor, 1991. "Multi-level programming and conflict resolution," European Journal of Operational Research, Elsevier, vol. 51(2), pages 233-247, March.
    2. Arora, S.R. & Gupta, Ritu, 2009. "Interactive fuzzy goal programming approach for bilevel programming problem," European Journal of Operational Research, Elsevier, vol. 194(2), pages 368-376, April.
    3. Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
    4. Pramanik, Surapati & Roy, Tapan Kumar, 2007. "Fuzzy goal programming approach to multilevel programming problems," European Journal of Operational Research, Elsevier, vol. 176(2), pages 1151-1166, January.
    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. Kailash Lachhwani, 2021. "Solving the general fully neutrosophic multi-level multiobjective linear programming problems," OPSEARCH, Springer;Operational Research Society of India, vol. 58(4), pages 1192-1216, December.
    2. M. A. El Sayed & Ibrahim A. Baky & Pitam Singh, 2020. "A modified TOPSIS approach for solving stochastic fuzzy multi-level multi-objective fractional decision making problem," OPSEARCH, Springer;Operational Research Society of India, vol. 57(4), pages 1374-1403, December.
    3. M. S. Osman & O. E. Emam & M. A. El Sayed, 2017. "Stochastic Fuzzy Multi-level Multi-objective Fractional Programming Problem: A FGP Approach," OPSEARCH, Springer;Operational Research Society of India, vol. 54(4), pages 816-840, December.

    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. M. S. Osman & O. E. Emam & M. A. El Sayed, 2017. "Stochastic Fuzzy Multi-level Multi-objective Fractional Programming Problem: A FGP Approach," OPSEARCH, Springer;Operational Research Society of India, vol. 54(4), pages 816-840, December.
    2. Firoz Ahmad, 2022. "Interactive neutrosophic optimization technique for multiobjective programming problems: an application to pharmaceutical supply chain management," Annals of Operations Research, Springer, vol. 311(2), pages 551-585, April.
    3. Rizk M. Rizk-Allah & Mahmoud A. Abo-Sinna, 2021. "A comparative study of two optimization approaches for solving bi-level multi-objective linear fractional programming problem," OPSEARCH, Springer;Operational Research Society of India, vol. 58(2), pages 374-402, June.
    4. Hong Wang & Xiaodong Zhang, 2018. "A Decentralized Bi-Level Fuzzy Two-Stage Decision Model for Flood Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1615-1629, March.
    5. Dempe, S., 2011. "Comment to "interactive fuzzy goal programming approach for bilevel programming problem" by S.R. Arora and R. Gupta," European Journal of Operational Research, Elsevier, vol. 212(2), pages 429-431, July.
    6. Mustapha Kaci & Sonia Radjef, 2023. "An adaptive method to solve multilevel multiobjective linear programming problems," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 33(3), pages 29-44.
    7. M. A. El Sayed & Ibrahim A. Baky & Pitam Singh, 2020. "A modified TOPSIS approach for solving stochastic fuzzy multi-level multi-objective fractional decision making problem," OPSEARCH, Springer;Operational Research Society of India, vol. 57(4), pages 1374-1403, December.
    8. Pierre Bernhard & Marc Deschamps, 2017. "Kalman on dynamics and contro, Linear System Theory, Optimal Control, and Filter," Working Papers 2017-10, CRESE.
    9. Jones, Randall E. & Cacho, Oscar J., 2000. "A Dynamic Optimisation Model of Weed Control," 2000 Conference (44th), January 23-25, 2000, Sydney, Australia 123685, Australian Agricultural and Resource Economics Society.
    10. Voelkel, Michael A. & Sachs, Anna-Lena & Thonemann, Ulrich W., 2020. "An aggregation-based approximate dynamic programming approach for the periodic review model with random yield," European Journal of Operational Research, Elsevier, vol. 281(2), pages 286-298.
    11. Pam Norton & Ravi Phatarfod, 2008. "Optimal Strategies In One-Day Cricket," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 25(04), pages 495-511.
    12. Aghayi, Nazila & Maleki, Bentolhoda, 2016. "Efficiency measurement of DMUs with undesirable outputs under uncertainty based on the directional distance function: Application on bank industry," Energy, Elsevier, vol. 112(C), pages 376-387.
    13. Tan, Madeleine Sui-Lay, 2016. "Policy coordination among the ASEAN-5: A global VAR analysis," Journal of Asian Economics, Elsevier, vol. 44(C), pages 20-40.
    14. D. W. K. Yeung, 2008. "Dynamically Consistent Solution For A Pollution Management Game In Collaborative Abatement With Uncertain Future Payoffs," International Game Theory Review (IGTR), World Scientific Publishing Co. Pte. Ltd., vol. 10(04), pages 517-538.
    15. Crutchfield, Stephen R. & Brazee, Richard J., 1990. "An Integrated Model of Surface and Ground Water Quality," 1990 Annual meeting, August 5-8, Vancouver, Canada 271011, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    16. Hanafi, Said & Freville, Arnaud, 1998. "An efficient tabu search approach for the 0-1 multidimensional knapsack problem," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 659-675, April.
    17. Schön, Cornelia & König, Eva, 2018. "A stochastic dynamic programming approach for delay management of a single train line," European Journal of Operational Research, Elsevier, vol. 271(2), pages 501-518.
    18. Eric D. Gould, 2008. "Marriage and Career: The Dynamic Decisions of Young Men," Journal of Human Capital, University of Chicago Press, vol. 2(4), pages 337-378.
    19. Lange, Rutger-Jan, 2024. "Bellman filtering and smoothing for state–space models," Journal of Econometrics, Elsevier, vol. 238(2).
    20. Renato Cordeiro Amorim, 2016. "A Survey on Feature Weighting Based K-Means Algorithms," Journal of Classification, Springer;The Classification Society, vol. 33(2), pages 210-242, July.

    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:eee:apmaco:v:266:y:2015:i:c:p:1038-1049. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.