Most of the reads in highly expressed intervals (> 1,000 readseq reads) were mapped to known exons.
We address the problems of determining consistency and of finding a solution for sets of three-point relations expressing exclusion of a point from an interval, and for sets of four-point relations expressing interval disjointness.
The model integrated mixed-integer linear programming, interval-parameter programming, semi-infinite programming and fuzzy-chance-constrained programming within a general framework, which can tackle multiple uncertainties expressed as intervals, functional intervals (dual uncertainties), random variables, fuzzy sets, and their combinations (fuzzy-interval admissible probability).
Wang et al. ([2011]) developed interval-valued fuzzy linear programming with infinite alpha-cuts (IVFLP-I) and applied it to municipal solid waste management under uncertainty expressed as intervals and interval-valued fuzzy sets.
The uncertain parameters with limited data can be modeled as interval probabilistic variables whose distribution parameters are expressed as interval variables instead of unique values.
If decision makers prefer some economic index (such as NPV, etc)., they can calculate the NPVs (expressed in interval numbers) for every block separately according to the interval index weights in Table 4.
Thus, based on the interval-number theory, an interval-parameter mathematical programming (IMP) model can be developed where all or part of the parameters are expressed as interval numbers (i.e. a number with an unknown distribution between fixed lower and upper bounds).
The transfer function should, therefore, be expressed by interval polynomials.
Moreover, it can deal with uncertainties and complexities expressed as interval numbers.
The index weights expressed in interval number form are, therefore, introduced in this paper.
