Abstract: The development, operation and maintenance of
Integrated Waste Management Systems (IWMS) affects essentially
the sustainable concern of every region. The features of such systems
have great influence on all of the components of sustainability. In
order to reach the optimal way of processes, a comprehensive
mapping of the variables affecting the future efficiency of the system
is needed such as analysis of the interconnections among the
components and modeling of their interactions. The planning of a
IWMS is based fundamentally on technical and economical
opportunities and the legal framework. Modeling the sustainability
and operation effectiveness of a certain IWMS is not in the scope of
the present research. The complexity of the systems and the large
number of the variables require the utilization of a complex approach
to model the outcomes and future risks. This complex method should
be able to evaluate the logical framework of the factors composing
the system and the interconnections between them. The authors of
this paper studied the usability of the Fuzzy Cognitive Map (FCM)
approach modeling the future operation of IWMS’s. The approach
requires two input data set. One is the connection matrix containing
all the factors affecting the system in focus with all the
interconnections. The other input data set is the time series, a
retrospective reconstruction of the weights and roles of the factors.
This paper introduces a novel method to develop time series by
content analysis.
Abstract: Recently, an increasing number of researchers have
been focusing on working out realistic solutions to sustainability
problems. As sustainability issues gain higher importance for
organisations, the management of such decisions becomes critical.
Knowledge representation is a fundamental issue of complex
knowledge based systems. Many types of sustainability problems
would benefit from models based on experts’ knowledge. Cognitive
maps have been used for analyzing and aiding decision making. A
cognitive map can be made of almost any system or problem. A
fuzzy cognitive map (FCM) can successfully represent knowledge
and human experience, introducing concepts to represent the essential
elements and the cause and effect relationships among the concepts to
model the behaviour of any system. Integrated waste management
systems (IWMS) are complex systems that can be decomposed to
non-related and related subsystems and elements, where many factors
have to be taken into consideration that may be complementary,
contradictory, and competitive; these factors influence each other and
determine the overall decision process of the system. The goal of the
present paper is to construct an efficient IWMS which considers
various factors. The authors’ intention is to propose an expert based
system design approach for implementing expert decision support in
the area of IWMSs and introduces an appropriate methodology for
the development and analysis of group FCM. A framework for such a
methodology consisting of the development and application phases is
presented.
Abstract: Fuzzy Cognitive Maps (FCMs) is a causal graph, which shows the relations between essential components in complex systems. Experts who are familiar with the system components and their relations can generate a related FCM. There is a big gap when human experts cannot produce FCM or even there is no expert to produce the related FCM. Therefore, a new mechanism must be used to bridge this gap. In this paper, a novel learning method is proposed to construct causal graph based on historical data and by using metaheuristic such Tabu Search (TS). The efficiency of the proposed method is shown via comparison of its results of some numerical examples with those of some other methods.