The more-for-less paradox in fuzzy posynomial geometric programming

The more-for-less (MFL) problem in fuzzy posynomial geometric programming (FPGP) is advanced in this paper. The research results presented here focus primarily on the nonconvex FPGP in both objective functions and constraint functions. Convexification, quasiconvex, or pseudoconvex, is extended in the sense of an MFL paradox by consolidating the necessary and sufficient conditions. Since the FPGP is equivalent to fuzzy linear programming correspondingly, there exists a solution to the FPGP. Furthermore, the duality or strong duality theorem, the equivalent condition of the MFL paradox and its condition under expansion are examined in detail. It is well known that the fundamental understanding of problems on MFL paradox is of paramount importance to applications of resource allotments and optimal resource management, and correspondingly that the information science and technology advancement play a rule to resource allotments and resource option in management problems. In fact, they are dependent and interwinded.