Application of Soft Computing Methods in Corporate Reorganization

Abstract

Decision making in corporate reorganization is not only triggered in case of insolvency or overindebtedness but also if a serious corporate crisis is already looming. Decisions in this context have to be based on quantitative as well as qualitative data which are characterized by uncertainty, fuzziness, incompleteness and extreme complexity. Because soft computing methods as fuzzy systems, neural nets and evolutionary algorithms are tolerant towards these phenomenons, they are highly potential for applications in auditing of corporate reorganizability and in controlling reorganizing measures. How to solve typical classification, prognosis as well as optimization problems in the reorganization context will be shown guided by examples.