Hybrid Decision Support System Research Articles

A Hybrid Sensory Evaluation and Decision Support System for Vegetable Breeding Selections

A Hybrid Sensory Evaluation and Decision Support System for Vegetable Breeding Selections

FMMSIC: a hybrid fuzzy based decision support system for MMS (in order to estimate interrelationships between criteria)

One of the main tasks in exploitation of ore-body is to select a suitable mining method. In mining method selection (MMS) problems, a decision procedure has to choose the best exploitation method that satisfies the evaluation criteria. It is generally hard to find a mining method that meets all the criteria simultaneously, therefore a good compromise solution is preferred as the final selection. Furthermore, the MMS problem is an inherently uncertain activity. To deal with the uncertainty, this paper presents an hybrid decision support system based on the fuzzy multi attribute decision making, named the fuzzy mining method selection with interrelation criteria (FMMSIC). FMMSIC models the relative weights of criteria by combining the fuzzy analytic network process and fuzzy entropy, and discusses using these hybrid techniques to determine the overall weights. Subsequently, the technique for order preference by similarity to an ideal solution method was modified by various normalization norms according to the MMS problem condition. Finally, to illustrate how the FMMSIC is used for the MMS problems, an empirical study of a real case is conducted. It shows by means of an application that the FMMSIC is well suited as a decision support system for the MMS.

Module to Generate Rules for the Knowledge Base in a CAPCAST Expert System

The study presented here is related with one of the components of a hybrid decision support system called CAPCAST (Computer Aided Process CAST), developed under a research project at the Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology. This is a module for rule generation to serve the knowledge base operating in an expert system. The scope of the system operation involves the selection of technological parameters for the manufacture of machine parts from ductile iron. However, it can be extended to include other materials and technologies.

Activity-Based Costing and Management applied in a hybrid Decision Support System for order management

This article introduces a new Cost Management and Decision Support System (DSS) applicable to Order Management. This model is better fit and compatible with today's competitive, and constantly changing, business environment. The presented Profitable-To-Promise (PTP) approach is a novel modeling approach which integrates System Dynamics (SD) simulation with Mixed-Integer Programming (MIP). This Order Management model incorporates Activity-Based Costing and Management (ABC/M) as a link to merge the two models, MIP and SD. This combination is introduced as a hybrid Decision Support System. Such a system can evaluate the profitability of each Order Fulfillment policy and generate valuable cost information. Unlike existing optimization-based DSS models, the presented hybrid modeling approach can perform on-time cost analysis. This will lead to better business decisions based on the updated information.

A hybrid decision support system for slow moving spare parts joint replenishment: a case study in a nuclear power plant

This paper presents a hybrid Decision Support System (DSS) for slow moving spare parts joint replenishment in a nuclear power plant. In this study, we integrate the fuzzy and grey theory-based spare parts criticality class evaluation model to confirm the target service level, and the web-based joint replenishment DSS to obtain reasonable purchase parameters that can be helpful for reducing total inventory holding costs. The proposed DSS was successful in decreasing inventories holding costs significantly by modifying the unreasonable purchase applications while maintaining the predefined target service level.

A hybrid decision support system for sustainable office building renovation and energy performance improvement

Energy consumption of buildings accounts for around 20–40% of all energy consumed in advanced countries. Over the last decade, more and more global organizations are investing significant resources to create sustainably built environments, emphasizing sustainable building renovation processes to reduce energy consumption and carbon dioxide emissions. This study develops an integrated decision support system to assess existing office building conditions and to recommend an optimal set of sustainable renovation actions, considering trade-offs between renovation cost, improved building quality, and environmental impacts. A hybrid approach that combines A* graph search algorithm with genetic algorithms (GA) is used to analyze all possible renovation actions and their trade-offs to develop the optimal solution. A two-stage system validation is performed to demonstrate the practical application of the hybrid approach: zero-one goal programming (ZOGP) and genetic algorithms are adopted to validate the effectiveness of the algorithm. A real-world renovation project is introduced to validate differences in energy performance projected for the renovation solution suggested by the system. The results reveal that the proposed hybrid system is more computationally effective than either ZOGP or GA alone. The system's suggested renovation actions would provide substantial energy performance improvements to the real project if implemented.

Forecasting Fraudulent Financial Statements using Data Mining

This paper explores the effectiveness of machine learning techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. To this end, a number of experiments have been conducted using representative learning algorithms, which were trained using a data set of 164 fraud and non-fraud Greek firms in the recent period 2001-2002. The decision of which particular method to choose is a complicated problem. A good alternative to choosing only one method is to create a hybrid forecasting system incorporating a number of possible solution methods as components (an ensemble of classifiers). For this purpose, we have implemented a hybrid decision support system that combines the representative algorithms using a stacking variant methodology and achieves better performance than any examined simple and ensemble method. To sum up, this study indicates that the investigation of financial information can be used in the identification of FFS and underline the importance of financial ratios. Keywords—machine learning, stacking, classifier

Credit risk analysis using a hybrid data mining model

Credit risk analysis is an important topic in financial risk management. Owing to recent financial crises, credit risk analysis has been the major focus of the financial and banking industry. An accurate estimation of credit risk could be transformed into a more efficient use of economic capital. To this end, a number of experiments have been conducted using representative learning algorithms, which were tested using two publicly credit datasets. The decision of which particular method to choose is a complicated problem. A good alternative to choosing only one method is to create a hybrid forecasting system incorporating a number of possible solution methods as components (an ensemble of classifiers). For this purpose, we have implemented a hybrid decision support system that combines the representative algorithms using a selective voting methodology and achieves better performance than any examined simple and ensemble method.

An Intelligent Hybrid Decision Support System for the Management of Water Resources

During the last two decades the scarcity of water resources is exponentially growing worldwide. Cyprus is one of the countries in the Mediterranean where the water shortage problem is evident and very severe. This paper describes an intelligent and flexible, hybrid decision support system for the management of water resources in the regions along the South Conveyor Project, the largest water development project in Cyprus. In particular, the paper presents the integrated system and its architecture, together with detailed descriptions of the hybrid components synthesizing it. Details on the implementation and preliminary evaluation of the system are also provided.

Staging of cervical cancer with soft computing.

This paper describes a way of designing a hybrid decision support system in soft computing paradigm for detecting the different stages of cervical cancer. Hybridization includes the evolution of knowledge-based subnetwork modules with genetic algorithms (GA's) using rough set theory and the Interactive Dichotomizer 3 (ID3) algorithm. Crude subnetworks obtained via rough set theory and the ID3 algorithm are evolved using GA's. The evolution uses a restricted mutation operator which utilizes the knowledge of the modular structure, already generated, for faster convergence. The GA tunes the network weights and structure simultaneously. The aforesaid integration enhances the performance in terms of classification score, network size and training time, as compared to the conventional multilayer perceptron. This methodology also helps in imposing a structure on the weights, which results in a network more suitable for extraction of logical rules and human interpretation of the inferencing procedure.

An approach to legal reasoning based on a hybrid decision-support system

This paper describes argument structures to generate plausible explanations for the conclusions reached by rule-based reasoning (RBR), and provides a means of integrating with case-based reasoning (CBR). The area of application is a legal domain, for which a hybrid RBR–CBR knowledge-based system was built. An underlying object-oriented knowledge representation scheme provides a means of modelling both the structural relationships among knowledge entities (i.e. rules and cases) and the control structures among them. Legal reports of previously-decided cases are used as a knowledge source for the CBR part of the system. An argumentation facility is presented, for each predicted rule-based outcome, based on Toulmin's argument structures to provide support via justifications. The framework of similarity for the case base side is based on a model which exploits the fuzzy proximity relations. Retrieved cases are used to help the decision-maker in formulating the final outcome of a new case (whose similarity with the retrieved cases is determined from fuzzy proximity relations). The system is also capable of providing justification of the case selection process.

The development and evaluation of a hybrid decision support system for clinical decision making: The case of discharge from the military

This article reports on the development, implementation, and evaluation of a decision support system (DSS), a hybrid of a statistical model (regression equation) and expert rules, to support this decision. The study focused on the decisions made by social workers, psychologists, and psychiatrists serving as military mental health officers to recommend discharge from compulsory duty in the Israeli army because of mental or emotional difficulties. The process involved three consecutive steps: modeling decisions by statistical analysis of past and current decisions and by eliciting expert rules, developing o hybrid decision support system, and evaluating the DSS through empirical validation and a user survey. The validity of this hybrid DSS was established. The DSS was able to predict 86.5 percent of new decisions made by mental health officers and 70 percent of post decisions. User acceptance, on the other hand, was low. This article discusses these findings and their implications for practice. Key words; clinical judgment; decision support systems; expert rules; mental health officers; military Social workers and other helping professionals make clinical judgments on a routine basis. Many of these decisions have important consequences for clients. Relying on clinical judgment and expertise, practitioners make daily decisions whether to accept clients for treatment, render services, hospitalize, discharge, or commit to involuntary care; such decisions can be of immeasurable significance to people's lives. Our empirical study of these important judgments and decisions made in the helping professions revealed how these professions are fallible and prone to error (Gambrill, 1990; Gibbs & Gambrill, 1996; Nisbett & Ross, 1980). Almost all clinical decisions are made under some degree of uncertainty (Hogarth, 1980). Many factors in the decision-making process, such as incomplete or unreliable information relevant to the decision, and errors or inconsistencies in the application of decision-making rules introduce uncertainty. Much of the uncertainty results from the probabilistic nature of the outcomes of decisions. Decisions made under what seem to be identical situations lead to different outcomes (Cooksey, 1996). Fallibility, therefore, is an inseparable feature of clinical judgment under uncertainty. Our study focused on the decisions made by social workers, psychologists, and psychiatrists serving as military mental health officers (MHOs) to recommend discharge from compulsory duty in the Israeli military services because of mental or emotional difficulties. This decision has significant consequences for the individual soldier, as well as for the soldier's social environment. Compulsory service in the military is a major duty and right of all Jewish citizens in Israel (most non-Jewish citizens are exempt from compulsory service). A discharge on the basis of psychiatric dysfunction stigmatizes the individual in civilian life. Consequences range from difficulties in obtaining a driver's license or in securing employment to less blatant discriminatory attitudes in Israeli society. On the other hand, a misguided decision not to discharge a dysfunctional soldier may lead to dire outcomes such as psychotic breakdown, suicide, and violent behavior within the unit, which in turn could lead to failure in battle (Benbenishty, Zirlin-Shemesh, & Kaplan, 1993). Military MHOs, the majority of whom are social workers, assess the soldier and decide whether or not to lower the soldier's medical rating on psychiatric grounds, thereby causing an immediate dismissal from the military. This decision is based on clinical judgment and not on any preset requirements or guidelines. Thus, it is open to the same problems in judgment identified in so many other contexts. Furthermore, given the consequences of the decision, the need to maintain consistency among MHOs making the decision is of special importance. …

A hybrid intelligent system for predicting bank holding structures

A composite model of neural network and rough sets components was constructed to predict a sample of bank holding patterns. The final model was able to correctly classify 96% of a testing set of four types of bank holding structures. Holding structure is defined as the number of banks under common ownership. For this study, forms of bank holding structure include: banks that are not owned by another company, single banks that are held by another firm, pairs of banks that are held by another enterprise, and three or more banks that are held by another company. Initially, input to the neural network model was 28 financial ratios for more than 200 banks in Arkansas for 1992. The 28 ratios are organized by categories such as liquidity, credit risk, leverage, efficiency, and profitability. The ratios were constructed with 70 bank variables such as net worth, deposits, total assets, net loans, total operating income, etc. The first neural network model correctly classified 84% of the testing set at a tolerance level of 0.20. Another artificial intelligence (AI) procedure known as two-dimensional rough sets was then applied to the dataset. Rough sets reduced the number of input variables from 28 to 18, a drop of 36% in the number of input variables. This version of rough sets also eliminated a number of records, thereby reducing the information system (i.e., matrix) on both vertical and horizontal dimensions. A second neural network was trained with the reduced number of input variables and records. This network correctly classified 96% of the testing set at a tolerance level of 0.20, an increase of 11% in the accuracy of the prediction. By applying two-dimensional reducts to the dataset of financial ratios, the predictive accuracy of the neural network model was improved substantially. Banking institutions that are prime candidates for mergers or acquisitions can then be more accurately identified through the use of this hybrid decision support system (DSS) which combines different types of AI techniques for the purposes of data management and modeling.

Developing an exploration decision support system (EDSS): A strategy for combining information and analytics

Petroleum exploration companies enter the twenty first century facing an increasingly competitive and risky environment. Under those circumstances, there is a growing need for better systematic decision-making that explicitly embodies the firm's desired goals and resource constraints. Computer-aided decision making, or decision support systems (DSS), provide an aid for those exploration management problems that are large, complex, unstructured, and involve management mudgment. Almost every present day DSS falls into one of two general classes. Vehicle DSSs such as linear/nonlinear programming models and other optimization routines, propose and impose specific methodologies to the decision-maker. On the other hand, toolbox DSSs, such as simulation programs, statistical functions, and graphical packages, are generally flexible in enabling their users to employ a variety of approaches and tools for their decision tasks but provide little guidance on both problem representation and investigation. This paper describes the development of a hybrid DSS model that combines the advantages of both the vehicle and toolbox systems components to provide a comprehensive approach to exploration planning from geological development through the capital allocation process. The Exploration Decision Support System (EDSS) preserves the flexibility of the toolbox system while enriching the problem-solving strategies available to the firm. The central objectives for developing an EDSS framework are: (1) better decisions about resource allocations; (2) more systematic understanding of the factors affecting exploration decisions; (3) improved communication about EP and (4) an explicit vehicle for continuous improvement of the petroleum exploration firm's decision-making process. The EDSS model can guide geological and exploration managers toward a more formal evaluation of projects, provide insight into the impact of competing choice alternatives, and significantly improve the quality of exploration decisions.

Explaining and justifying recommendations in an agriculture decision support system

Agricultural decision support systems are not being readily accepted by all potential users. This paper discusses the reasons for this lack of user acceptance and proposes a method for rectifying this problem. The method is based on the premise that good explanations increase user understanding and acceptance of a system. The method involved building a hybrid decision support system which first interprets the output of a quantitative simulation model and then constructs explanations, which are in turn adapted to the particular user. An initial prototype of the system, known as EXPLAIN, is presented.