Abstract
In this paper, necessity measures defined by two linear modifier functions, which we call “Linear Necessity Measure”, are studied. We investigate implication functions corresponding to linear necessity measures and apply linear necessity measures to possibilistic linear programming problems. We describe implication functions generated from two linear modifier generating functions, and classify them. We show that necessity measure constrained models with linear necessity measures of fuzzy linear programming problems are solved easily in many cases.
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Inuiguchi, M., Higuchi, T., Tsurumi, M. (2011). Linear Necessity Measures and Their Applications to Possibilistic Linear Programming. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_38
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DOI: https://doi.org/10.1007/978-3-642-24425-4_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24424-7
Online ISBN: 978-3-642-24425-4
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