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dc.contributor.authorChiang, CIen_US
dc.contributor.authorYu, PLen_US
dc.date.accessioned2014-12-08T15:44:25Z-
dc.date.available2014-12-08T15:44:25Z-
dc.date.issued2001en_US
dc.identifier.isbn3-540-42377-Xen_US
dc.identifier.issn0075-8442en_US
dc.identifier.urihttp://hdl.handle.net/11536/30006-
dc.description.abstractBecause of rapid change of our technology and environments many problems we are facing are new, complex, and nontrivial. The solutions to these problems are usually outside our day-to-day experience, competence, or our habitual domains. Thus, they are fuzzy and challenging. In order to effectively solve this kind of fuzzy or challenging problem, we need to continually expand our competence or habitual domains so that we can make a good decision with confidence. This paper is to describe a holistic picture of using competence set analysis to solve this kind of decision problem. First, two types of competence set analysis, problem-oriented and skill-oriented, are introduced. Next, the concept of selecting optimal competence set that maximizes the net return is discussed. We then briefly describe a number of algorithms to find the optimal path for expanding our competence set. Finally, we gave some applications of expanding competence set with minimum cost.en_US
dc.language.isoen_USen_US
dc.subjectcompetence set analysisen_US
dc.subjectoptimal expansionen_US
dc.subjecthabitual domainsen_US
dc.subjectdecision-makingen_US
dc.titleCompetence set analysis - An effective means to solve non-trivial decision problemsen_US
dc.typeArticle; Proceedings Paperen_US
dc.identifier.journalMULTIPLE CRITERIA DECISION MAKING IN THE NEW MILLENNIUMen_US
dc.citation.volume507en_US
dc.citation.spage142en_US
dc.citation.epage151en_US
dc.contributor.department管理學院zh_TW
dc.contributor.department運輸與物流管理系 註:原交通所+運管所zh_TW
dc.contributor.departmentCollege of Managementen_US
dc.contributor.departmentDepartment of Transportation and Logistics Managementen_US
dc.identifier.wosnumberWOS:000174948900013-
Appears in Collections:Conferences Paper