Linear programming with imprecise data

Abstract
Conventional linear programming requires the deterministic specification of all the relevant data but generally this is only known imprecisely. Several ways in which imprecision may be incorporated into the programs are discussed. These include proximate programming, inexact programming and fuzzy programming. A simple illustrative example concerned with water quality is reworked using some of the described techniques. Fuzzy programming is a particularly useful model which can handle imprecision with respect to all the parameters and can also incorporate multiple goals.

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