
The Framework of Stochastic Programming Model with Scenario Generation Approach for Sustainable Knowledge Management under Uncertainty Disruption
Author(s) -
Sajadin Sembiring,
Herman Mawengkang,
. Tulus,
Muhammad Zarlis
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1255/1/012063
Subject(s) - computer science , stochastic programming , knowledge management , order (exchange) , process (computing) , knowledge transfer , competitive advantage , business process , business , work in process , mathematical optimization , marketing , mathematics , finance , operating system
Recently, knowledge, as a strategic resource, has become an essential driving forces for business success, in particular in competitive situation. Accordingly, the existence of knowledge management (KM) is to support the business firm to locate, select, organize, distribute, and transfer vital information within the firm. One of the method to increasing effectiveness of knowledge management is to optimize its processes. However, during the process in order to increase the firm performance some disruption may occur. This paper describes a stochastic programming model to optimize the knowledge management processes problem within a firm, considering uncertainty disruption. We adopt scenario generation approach for tackling the stochastic problem.