Decision Support Systems and Expert Systems

Supporting decision making requires understanding of the various processes involved to enable computer-based system support to be designed and increase their efficiency (Lukasheh et al., 2001). The DSSs are computer-based information systems which have been designed to affect and improve the process of decision making. They underline the ideas that collectively use data and models to solve unstructured problems (Sprague and Carlson, 1982). DSS may consist of three parts: 1) an interactive graphic display capacity for managing the interface between the decision makers and the system; 2) a data management system (DMS); and 3) a model base management system (MBMS), which aggregates different models, such as optimization models, forecasting models, and simulation models. The DSS components described each have their own mode of interaction, with a higher information change. DMS is capable of supplying information to MBMS and after completion this information can be returned to the DMS to be stored. But data can be changed and updated by users through the interactive graphic display. While the DMS may be the same as a MIS, the interactive graphic display capacity may be configured in a GIS environment. Applying and developing DSS for SWM can be justified by the need to solve unstructured, semi-structured and structured

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problems. Such model makes possible the construction and evaluation of arguments both for and against competing courses of action (MacDonald, 1996b).

Other types of DSS models might include a fourth part, related to a knowledge-based system, with the intention of helping to estimate input parameters and helping to interpret modeling results (Lukasheh et al., 2001). Such knowledge-based systems may be called expert systems (ESs). An ES is a computer program which is designed to imitate the advice of a human expert. It aims to draw conclusions from information where there is not a precise, unambiguous answer (AEA Technology, 1998). Thus, an ES consists of three components: 1) a knowledge base; 2) an inference engine which applies built-in rules (often rather rough rules of thumb) to the knowledge base to draw conclusions; and 3) a user interface, which enables the user to ask questions and understand the answers. The way that such components work together has been described by Lukasheh et al. (2001). ES categories can be divided in rule-based, knowledge-rule-based, neural networks, fuzzy, object-oriented, case-based reasoning, system architecture development, intelligent agent, modeling, ontology and database methodology (Liao, 2005). Once an interaction with the ES is initiated, the inference engine searches for matching patterns. This mechanism compares information supplied by the user with the knowledge contained in the knowledge base and deduces whatever conclusion may logically follow (Lukasheh et al., 2001). During this interaction, a working memory holds all information supplied by the user or deduced by the system‘s inference mechanism, while working on the knowledge base (Lukasheh et al., 2001). A case-based ES in SWM can be developed through the acquisition of relevant data and information providing the planner with technical information that may not be readily available. For example, an ES database was used to characterize a waste stream, and estimate implications concerning transport, processing and disposing of materials and waste (MacDonald, 1996b). Table 2.5 summarizes the applications of EDI/EDX and GIS, DSS, and ES for SWM.

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Table 2.5A summary of MIS, DSS, and ES applied for solid waste management

Reference Scope Methodology

Chiueh and Yu, 2006 An integrated framework of solid waste management information system, with applications in public or private sectors and partial contribution of sustainable development indicators

MIS

Hřebíček et al, 2003 Slovak waste information system to support data collection

related to waste management MIS

Chang et al., 2001 Internet-based management information system for scrap

vehicle management MIS

Şener et al., 2006 Landfill site selection GIS

Chang et al., 1997c Scheduling and vehicle routing in a collection waste system GIS Ghose et al., 2006 GIS optimal routing model to determine the minimum

cost/distance efficient collection paths for transporting solid waste to landfills

GIS/DSS

Karadimas and Loumos, 2008

GIS based model developed to establish a waste collection system considering waste generation parameters GIS Chang and Wang,

1996d

Solid waste management system planning DSS Haastrup et al., 1998 Model for evaluating policies for service organization of the

collection and for identifying areas suitable for locating waste treatment and disposal plants

DSS

Bhargava and Tettelbach, 1997

Model to help recycling system on World Wide Web DSS

Simonetto and Borenstein, 2007

SCOLDSS – a DSS applied to the operational planning of solid waste collection systems

DSS MacDonald, 1996b Model to assist in improving solid waste the decision-making

process

DSS and ES

Barlishen and Baetz, 1996

Model for planning a MSW management system DSS and ES

Wey, 2005 Model to support waste incineration siting problems DSS and ES Basri and Stentiford,

1995

Guidelines for ES application to solid waste management ES

McCauley-Bell, and Reinhart, 1997

Methodology for MSW composition studies ES

Rubenstein-Montanto and Zandi,1999;

Rubenstein-Montano, 2000

Solid waste management policy planning ES

2.6 TYPES OF ANALYTICAL TOOLS FOR SYSTEM ASSESSMENT

The classification of analytical tools for system assessment includes: 1) SD, 2) MFA, 3) LCA, 4) RA, 5) EIA, 6) SEA, 7) SoEA, and 8) SA. They are complementary in many real world applications. A summary of all the contemporary assessment tools for various process

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assessments would be very helpful for model synthesis and integration when dealing with a variety of SWM systems in different countries.