This study develops an interactive possibilistic linear programming (PLP) method for solving the integrated production/transportation planning decision (PTPD) problem with imprecise objective and constraints in an uncertain environment. The proposed PLP method attempts to minimize the total net costs with reference to available supply, machine capacity, labor level and budget constraints at eac...

This paper presents an intellectual property (IP)-based system-on-chip (SoC) synthesis framework focusing on how to select IPs from different sources and how to integrate the selected IPs using on-chip buses. In order to synthesize an on-chip bus-based SoC architecture using IPs with imprecise design costs, we propose a possibilistic mixed integer linear programming (PMILP) model, which is conv...

Ranking functions are qualitative degrees of uncertainty ascribed to events charged by uncertainty and taking as their values non-negative integers in the sense of ordinal numbers. Introduced are ranking functions induced by real-valued possibilistic measures and it is shown that different possibilistic measures with identical ranking functions yield the same results when applied in decision pr...

In standard possibilistic logic, prioritized in­ formation are encoded by means of weighted knowledge bases. This paper proposes an exten­ sion of possibilistic logic to deal with partially ordered information which can be viewed as a family of possibilistic knowledge bases. We show that all basic notions of possibilis­ tic logic have natural counterparts when dealing with partially ordered inf...

In this paper, application of possibilistic clustering techniques to identification of local linear models will be discussed. In particular, a generalisation of some possibilistic algorithms in the bibliography is obtained. With the presented procedures, a trade-off between an “expected shape” of the membership functions and model fit can be stated. Possibilistic clustering may allow for better...

Stability and sensitivity analysis becomes more and more attractive also in the area of multiple objective mathematical programming (for excellent surveys see e.g. [Gal86] and [Rios90]). Publications on this topic usually investigate the impact of parameter changes (in the righthand side or/and the objective function or/and the ’A-matrix’ or/and the domination structure) on the solution in vari...

Uncertainty reasoning and inconsistency handling are two important problems that often occur in the applications of the Semantic Web. Possibilistic description logics provide a flexible framework for representing and reasoning with ontologies where uncertain and/or inconsistent information exists. Based on our previous work, we develop a possibilistic description logic reasoner. Our demo will i...

The intended purpose of this research 1 is i) to show the specificity of possibilistic logic with respect to other logics with weighted statements, in particular its ability to deal both with uncertainty and vagueness, ii) to develop proof methods for possibilistic logic, and iii) to relate its ability to cope with partial inconsistency to belief revision and non-monotonic reasoning issues. See...

In this paper, an analysis of the convergence performance is conducted for a class of possibilistic clustering algorithms utilizing the Zangwill convergence theorem. It is shown that under certain conditions the iterative sequence generated by a possibilistic clustering algorithm converges, at least along a subsequence, to either a local minimizer or a saddle point of the objective function of ...

In recent decades, the forest industry has been growingly expanded due to economic conditions, climate changes, environmental and energy policies, intense demand changes. Thus, appropriate planning is required improve this industry. To achieve economic, social goals, a supply chain network designed based on multi-period multi-product Mixed-Integer Non-Linear Programming (MINLP) model in which o...