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Rough multiple objective programming

Author

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  • Atteya, T.E.M.

Abstract

In this paper, we focused on characterizing and solving the multiple objective programming problems which have some imprecision of a vague nature in their formulation. The Rough Set Theory is only used in modeling the vague data in such problems, and our contribution in data mining process is confined only in the “post-processing stage”. These new problems are called rough multiple objective programming (RMOP) problems and classified into three classes according to the place of the roughness in the problem. Also, new concepts and theorems are introduced on the lines of their crisp counterparts; e.g. rough complete solution, rough efficient set, rough weak efficient set, rough Pareto front, weighted sum problem, etc. To avoid the prolongation of this paper, only the 1st-class, where the decision set is a rough set and all the objectives are crisp functions, is investigated and discussed in details. Furthermore, a flowchart for solving the 1st-class RMOP problems is presented.

Suggested Citation

  • Atteya, T.E.M., 2016. "Rough multiple objective programming," European Journal of Operational Research, Elsevier, vol. 248(1), pages 204-210.
    • Handle: RePEc:eee:ejores:v:248:y:2016:i:1:p:204-210
    DOI: 10.1016/j.ejor.2015.06.079
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    References listed on IDEAS

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    1. Youness, Ebrahim A., 2006. "Characterizing solutions of rough programming problems," European Journal of Operational Research, Elsevier, vol. 168(3), pages 1019-1029, February.
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    Cited by:

    1. Chi-Yo Huang & Jih-Jeng Huang & You-Ning Chang & Yen-Chu Lin, 2021. "A Fuzzy-MOP-Based Competence Set Expansion Method for Technology Roadmap Definitions," Mathematics, MDPI, vol. 9(2), pages 1-26, January.
    2. Försch, Steffen & de Haan, Evert, 2018. "Targeting online display ads: Choosing their frequency and spacing," International Journal of Research in Marketing, Elsevier, vol. 35(4), pages 661-672.
    3. Yao, Yiyu & Zhou, Bing, 2016. "Two Bayesian approaches to rough sets," European Journal of Operational Research, Elsevier, vol. 251(3), pages 904-917.

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