Knowledge Requirements of DERESIS Model
Ergonomics uses different disciplinary knowledge as an aim to plan, design, manage, and continuously improve the work systems to be productive, efficient, and to be human-oriented (Schlick et al. 2010 ). In the following chapter, the required knowledge, w
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Knowledge Requirements of DERESIS Model
Ergonomics uses different disciplinary knowledge as an aim to plan, design, manage, and continuously improve the work systems to be productive, efficient, and to be human-oriented (Schlick et al. 2010). In the following chapter, the required knowledge, which is multidisciplinary, is introduced to develop the model. The required methods cover three aspects: technical, organizational, and human science perspectives (see Table 5.1).
5.1
Fuzzy Linguistics Theory
Zadeh (1975a) introduced the fuzzy set theory to enable uncertain and imprecise real world systems to be captured by linguistic variables. Fuzzy logic is therefore a useful tool for dealing with decisions involving complex, ambiguous, and vague phenomena based on the meanings of the linguistic variables. Traditional quantitative methods are problematic when analysing complicated and ill-defined situations. The study by Zadeh (1975b) describes the solution as the fuzzy linguistic method. The fuzzy linguistic approach is an approximate technique which represents qualitative aspects as linguistic values by means of linguistic variables (Zadeh 1975c). Linguistic expression provides a useful approach for interpreting the semantics of vagueness based on the subjective judgments of evaluators. Linguistic variables are variables which do not bear numerical values, but are words or sentences in a natural or artificial language. The concept of linguistic variables has been developed as a counterpart to the concept of a numerical variables (Lin et al. 2013). Fuzzy theory constructs a conceptual framework for a systematic treatment of fuzziness in linguistic variables that are represented in words or sentences. These linguistic variables are interpreted as fuzzy sets, characterised by membership functions. A fuzzy set is a mapping of a set of real numbers onto membership values that lie in the range [0,1]. Membership function can capture the human © Springer Nature Singapore Pte Ltd. 2017 Q. Lin, Analysis of Resource Management in Complex Work Systems, DOI 10.1007/978-981-10-2170-1_5
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5 Knowledge Requirements of DERESIS Model
Table 5.1 The required methods derived from three aspects
Three aspects
Method
Technical perspectives
Fuzzy linguistics theory Balanced Scorecard Networked thinking Change management Group research ∙ Group decision making ∙ Communication in group ∙ Collaboration
Organizational perspectives Human science perspectives
quantitative meaning of such variables so they can be processed as data. To capture the true human meaning of words or sentences, constructing their membership functions is important for the success of fuzzy applications (Lin et al. 2013). The objective of a fuzzy linguistic approach is to solve complicated, subjective and undefined situations. The fuzzy linguistic variables are adapted to triangular fuzzy numbers which are classified to symmetry. Linguistic variables are triangular fuzzy numbers, no matter whether they are symmetric, and have similar estimated results. There is no dif
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