**2. Decision support systems**

The decisional support signifies a set of procedures based upon mathematical models on processing data, in the view of assisting the manager on taking decisions in conditions of simplicity, robustness, controllability, adaptability and completeness (Little, 1970). The Deci‐ sion Making Process (DMP) consists in the outcome of decisional activities carried out by the representative taking decisions, assisted by a decisional team on supporting and/or a De‐ cisional Support System (DSS). According to Nobel Prize laureate H. Simon, the DMP in‐ cludes the following tasks: informing and up-taking of information, in order to formulate accurately the decisional issue; designing (includes activities in the view of understanding the decisional issue, adopting a new type of running, generating some new potential action ways and building the models); selecting the alternatives and adopting the solution; imple‐ menting the decision and evaluating the involvements. The activities and the phases in the H. Simon model emphasize a generic character and cover a large series of situations. The content and ampleness of them depends upon the decisional issue's features and upon the approaching way that was adopted in the view of solving the issue.

#### **2.1. Basic elements of the DSS concept**

DSS should cover a significant number of activities and phases of the decisional process. DSS is running in coordination with other components of the global informatics or informa‐ tional system of the organization, where data or information is transferred. The available In‐ formation and computer technologies, ICT, and the designing and implementation methodologies play an important part within the quality of the chosen solution. The accu‐ rate and equilibrated admixture between those three technologies (computerized models, databases and friendly interfaces) signify the technological centre of the DSS.

The typical constraints of a DSS are: a facile user's control adaptability to certain situations / user's characteristics, as well as the level of use during the process of decisions issuing. The controllability refers to the possibility of using the system at any moment and respectively, the possibility of changing the course of running in accordance to the own wish. DSS should be flexible enough to adapt to the deciding representative; by providing trust, the DSS leads towards a synergic evolution of use, in conditions of customization. Agility represents the capability to change and adapt quickly to changing circumstances. Using DSS is intended to support all phases of the decisional process.

Building a DSS starts with the participants (issuers of instruments, building analysts and the end-users), as well as the context elements (the current situation and the changes estimated in the context of the ITC progress) that interact with the organization. In this process, the specialists and end-users participate (analysts, designers or issuers of DSS instruments), working together within a team assigned by DSS. The DSS team will know details about the own products, as well as the competition alternative products; as regards the success of con‐ ception, building and implementation of DSS, the following conditions should be met: a bet‐ ter knowledge of the application, access to the knowledge sources, the identification of challenges of decision making process in connection with the end-users particularities, re‐ straining the information instruments and accurate methods for designing an efficient DSS. The DSS team should provide as faster as possible an employable version and easy to adapt on the technological changes.

#### **2.2. The DSS components**

It is necessary to adopt new methods, new technics and new instruments for decision sup‐

**•** facilitate communication between decision-making factors and structuring decision-mak‐

**•** online analysis of the data and extracting information and knowledge using Data Ware‐

**•** assess the effects of application of alternative decision-making (what-if) using simulation

**•** construct and recommend the optimal decision-making alternative using multi-criteria

**•** suggest decision-making alternatives based on artificial intelligence (expert system ES, ar‐ tificial neural networks ANN, Case based reasoning CBR, Genetic Algorithms GA,

The decisional support signifies a set of procedures based upon mathematical models on processing data, in the view of assisting the manager on taking decisions in conditions of simplicity, robustness, controllability, adaptability and completeness (Little, 1970). The Deci‐ sion Making Process (DMP) consists in the outcome of decisional activities carried out by the representative taking decisions, assisted by a decisional team on supporting and/or a De‐ cisional Support System (DSS). According to Nobel Prize laureate H. Simon, the DMP in‐ cludes the following tasks: informing and up-taking of information, in order to formulate accurately the decisional issue; designing (includes activities in the view of understanding the decisional issue, adopting a new type of running, generating some new potential action ways and building the models); selecting the alternatives and adopting the solution; imple‐ menting the decision and evaluating the involvements. The activities and the phases in the H. Simon model emphasize a generic character and cover a large series of situations. The content and ampleness of them depends upon the decisional issue's features and upon the

DSS should cover a significant number of activities and phases of the decisional process. DSS is running in coordination with other components of the global informatics or informa‐ tional system of the organization, where data or information is transferred. The available In‐ formation and computer technologies, ICT, and the designing and implementation

approaching way that was adopted in the view of solving the issue.

**•** recommend a candidate solution by multi-attribute decision making (MADM);

**•** structure and enhance decision-making meetings (intelligent decisional planning);

housing (DW), Online Analytic Processing (OLAP), and Data Mining;

ing problems (decision charts, decision trees);

Swarm Intelligence SI or hybrid models).

**2. Decision support systems**

**2.1. Basic elements of the DSS concept**

port capable to:

92 Decision Support Systems

techniques;

optimization techniques;

DSS is formed of four essential subsystems: the language subsystem, LS, which emphasizes the set of expression forms, by which the user can transmit; the subsystem of presentation, PS, that signifies a set of forms or means by which messages are transferred (from out to DSS) towards the user or third parties (executants of decisions, data sources within organi‐ zation); the subsystem of the knowledge elements, KES, which includes elements of knowl‐ edge purchased or created internally; the subsystem of problem solving – PSS, signifying the set of software modules by which the KES knowledge elements are processed, as result of rendering the input messages. The amplitude and characteristics of these four subsystems and the adopted solutions of information transposing can make a difference between the ap‐ plication systems.

#### *2.2.1. The Knowledge Elements Subsystem – KES*

KES includes the knowledge, whichever the user hasn't any ability or time necessary to accumulate it. KES has the mission of simulating the general knowledge volume, specif‐ ic to the application and decisional situation that an ideal decisional assistant should have.

The primary elements of knowledge are useful on recognizing a decisional situation and serve as "basic material" in order to issue a solution. These can be particularized into the following classes: descriptive knowledge, procedural knowledge (mathematical knowledge of simulation and optimization, as well as the algorithms of the associates solving the is‐ sues), the knowledge regarding the reasoning (concerning the governmental rules and justi‐ fying the way of using the procedural knowledge simultaneously with those descriptive).

The support subsystems of documents oriented decisions (DSS-DO), based on the descrip‐ tive, procedural or reasoning knowledge use serve both the information decisional activities, as well as the activities of evaluating the action alternatives and choosing a solution. The fo‐ cus is on the abilities of managing the electronic documents and developing the knowledge,

Emerging Applications of the New Paradigm of Intelligent Decision Making Process: Hybrid Decision Support Systems

for Virtual Enterprise (DSS-VE) http://dx.doi.org/10.5772/3371 95

The support subsystems of models oriented decisions (DSS-MO) appertain on offering solu‐ tions on solving the decisional issue model. The set of solution is directly included in its exe‐ cutable form within the logic of the software modules, transposing the subsystem of issue solving, by means of informatics; or, the set of solvers is kept under the form of programs library, that can be modified in the collection of procedural knowledge of the knowledge el‐ ements subsystem. The user can modify parameters/ data and can specify the paths of calcu‐ lus. The flexible solutions allow the selection of the solving algorithm, defining the presentation way, the modification of the algorithms collection. In contradistinction to the data oriented systems and those oriented on documents, the DSS-MO include a relatively

Regarding intelligent DSS (IDSS), the focus is on storing, managing and processing of knowledge on reasoning, within the system of knowledge elements, by using the engine of interfaces carrying out, which can be implemented into the system of treating issue. Initially, the knowledge oriented systems were aimed on replacing the models oriented systems, in situations where no enough truthful model could be issued in order to solve the decisional issue, or in situations where such model were too complicated so as to be solved by the help

The knowledge elements concerning the reasoning and the software modules based on arti‐ ficial intelligence, and which implement the abilities of processing such knowledge, are seen as an ingredient within the combined structures of the DSS. In this case, the reasoning knowledge can play different tasks: the intelligent processing of the messages expressed within a non-procedural language, the management of other types of knowledge included into KES, using the procedural knowledge within the evaluation and selection of the solu‐

There are more styles of mixing the systems, by means of integrating the software modules that transpose by the help of ICT the capacities of knowledge processing, and respectively, those referring to communication inside the same informatics instrument. The styles that characterize the integration within various information instruments are the following: a) in‐ tegration by knowledge conversion (interfacing) or the endowment of the software modules that should communicate with facilities embedded by knowledge export; b) integration, by

The patterns that define the integration inside the same information system are: integration by nesting, where one or more software modules are embedded within a single "host" pre‐ vailing program; the synergic integration, situation when more knowledge elements repre‐

means of the clipboard memory; c) integration, by means of the common formats.

and the interest is to realize the classification and indexation of documents.

reduced volume of descriptive knowledge elements.

of the already existing algorithms.

tion, at the level of human expert competencies.

sented in various forms can be processed.

The secondary knowledge elements (linguistic, which serve on understanding the significa‐ tion of users solicitations, and of potential reports from the decisions executors, or from oth‐ er data providers; of presentation which describe the way information is sent during the decisions issuance, or eventually, decisions are communicated; assimilative, which use on determining the conditions and on establishing the way the new knowledge elements can be inserted into KES), aiming on supporting the activities of issuing decisions by auxiliary ac‐ tivities, such as: interpretation of the input messages (received from users or third parties); illustration of the output messages issued by the system (towards the users or third parties); maintaining and updating KES. The knowledge stored is characterized by the following: the source (outside DSS or inside the system), specificity (general, applicable to a field, or strict‐ ly related to an application), the persistence and completeness (one or more amongst the types of knowledge elements).

#### *2.2.2. The communication subsystem*

The communication subsystem can include many solutions in order to take into considera‐ tion the following: knowledge and the cognitive style of those interacting with the system and the part these have towards the system (user, manager or data provider). A compro‐ mise should be carried out between simplicity in using, and respectively efficiency and performances.

#### *2.2.3. The problem solving system, PSS*

The PSS signifies the dynamic part of DSS, which carries out the knowledge processing, and includes the software programs that transpose the processing knowledge capacities (pur‐ chasing the knowledge; selecting the already existing knowledge elements, necessary as ba‐ sis on issuing the decision and issuing new achieved knowledge; presenting the results; managing and updating the KES content).

#### **2.3. An analysis of the possibilities of selection DSS architecture**

The DSS- data oriented decision structure (DSS-DO) is focused on the apprehension and di‐ agnosis of decisional issues, identifying the action alternatives and checking the new hy‐ pothesis and ideas. The main particularity consists in organizing the knowledge elements subsystem under the form of well structured databases, which sometimes might have signif‐ icant dimensions. The PSS is transposed in information by means of the software modules, which carry out the data management, the interactive finding of information and various processing, specific to particular applications.

The support subsystems of documents oriented decisions (DSS-DO), based on the descrip‐ tive, procedural or reasoning knowledge use serve both the information decisional activities, as well as the activities of evaluating the action alternatives and choosing a solution. The fo‐ cus is on the abilities of managing the electronic documents and developing the knowledge, and the interest is to realize the classification and indexation of documents.

The primary elements of knowledge are useful on recognizing a decisional situation and serve as "basic material" in order to issue a solution. These can be particularized into the following classes: descriptive knowledge, procedural knowledge (mathematical knowledge of simulation and optimization, as well as the algorithms of the associates solving the is‐ sues), the knowledge regarding the reasoning (concerning the governmental rules and justi‐ fying the way of using the procedural knowledge simultaneously with those descriptive).

The secondary knowledge elements (linguistic, which serve on understanding the significa‐ tion of users solicitations, and of potential reports from the decisions executors, or from oth‐ er data providers; of presentation which describe the way information is sent during the decisions issuance, or eventually, decisions are communicated; assimilative, which use on determining the conditions and on establishing the way the new knowledge elements can be inserted into KES), aiming on supporting the activities of issuing decisions by auxiliary ac‐ tivities, such as: interpretation of the input messages (received from users or third parties); illustration of the output messages issued by the system (towards the users or third parties); maintaining and updating KES. The knowledge stored is characterized by the following: the source (outside DSS or inside the system), specificity (general, applicable to a field, or strict‐ ly related to an application), the persistence and completeness (one or more amongst the

The communication subsystem can include many solutions in order to take into considera‐ tion the following: knowledge and the cognitive style of those interacting with the system and the part these have towards the system (user, manager or data provider). A compro‐ mise should be carried out between simplicity in using, and respectively efficiency and

The PSS signifies the dynamic part of DSS, which carries out the knowledge processing, and includes the software programs that transpose the processing knowledge capacities (pur‐ chasing the knowledge; selecting the already existing knowledge elements, necessary as ba‐ sis on issuing the decision and issuing new achieved knowledge; presenting the results;

The DSS- data oriented decision structure (DSS-DO) is focused on the apprehension and di‐ agnosis of decisional issues, identifying the action alternatives and checking the new hy‐ pothesis and ideas. The main particularity consists in organizing the knowledge elements subsystem under the form of well structured databases, which sometimes might have signif‐ icant dimensions. The PSS is transposed in information by means of the software modules, which carry out the data management, the interactive finding of information and various

types of knowledge elements).

performances.

94 Decision Support Systems

*2.2.2. The communication subsystem*

*2.2.3. The problem solving system, PSS*

managing and updating the KES content).

processing, specific to particular applications.

**2.3. An analysis of the possibilities of selection DSS architecture**

The support subsystems of models oriented decisions (DSS-MO) appertain on offering solu‐ tions on solving the decisional issue model. The set of solution is directly included in its exe‐ cutable form within the logic of the software modules, transposing the subsystem of issue solving, by means of informatics; or, the set of solvers is kept under the form of programs library, that can be modified in the collection of procedural knowledge of the knowledge el‐ ements subsystem. The user can modify parameters/ data and can specify the paths of calcu‐ lus. The flexible solutions allow the selection of the solving algorithm, defining the presentation way, the modification of the algorithms collection. In contradistinction to the data oriented systems and those oriented on documents, the DSS-MO include a relatively reduced volume of descriptive knowledge elements.

Regarding intelligent DSS (IDSS), the focus is on storing, managing and processing of knowledge on reasoning, within the system of knowledge elements, by using the engine of interfaces carrying out, which can be implemented into the system of treating issue. Initially, the knowledge oriented systems were aimed on replacing the models oriented systems, in situations where no enough truthful model could be issued in order to solve the decisional issue, or in situations where such model were too complicated so as to be solved by the help of the already existing algorithms.

The knowledge elements concerning the reasoning and the software modules based on arti‐ ficial intelligence, and which implement the abilities of processing such knowledge, are seen as an ingredient within the combined structures of the DSS. In this case, the reasoning knowledge can play different tasks: the intelligent processing of the messages expressed within a non-procedural language, the management of other types of knowledge included into KES, using the procedural knowledge within the evaluation and selection of the solu‐ tion, at the level of human expert competencies.

There are more styles of mixing the systems, by means of integrating the software modules that transpose by the help of ICT the capacities of knowledge processing, and respectively, those referring to communication inside the same informatics instrument. The styles that characterize the integration within various information instruments are the following: a) in‐ tegration by knowledge conversion (interfacing) or the endowment of the software modules that should communicate with facilities embedded by knowledge export; b) integration, by means of the clipboard memory; c) integration, by means of the common formats.

The patterns that define the integration inside the same information system are: integration by nesting, where one or more software modules are embedded within a single "host" pre‐ vailing program; the synergic integration, situation when more knowledge elements repre‐ sented in various forms can be processed.

Within the context of the combined systems, calling up the classical paradigm of DSS based on tripe structure of components can be useful: dialogue, data or models (DDM). This is an example of combined data and models oriented system, covering a part of the support pos‐ sibilities for decisions allowed, thus enabling the DSS decomposing in subsystems, and in completing or endowing with new modules, respectively.

transposed into an execution project. The designing process is composed of activities of es‐ tablishing the DSS structure and of defining each technological component of the system, as well as the way it integrates with the other parts of the global information system of the or‐ ganization. The designing stage is accomplished by the specification of carrying out the sys‐ tem and integrating it within the organization. The level of describing in details and of defining the components depends upon how this is allowed while using information plat‐ form. For most of applications, the component where the designer has the most levels of

Emerging Applications of the New Paradigm of Intelligent Decision Making Process: Hybrid Decision Support Systems

for Virtual Enterprise (DSS-VE) http://dx.doi.org/10.5772/3371 97

In the stage of implementation, the content of technical design is transposed into an infor‐ mation system, by means of the instruments selected, and consists in activities of effective building of DSS into an application and integration within the global information system of the organization, testing and issuance of the documentation, of building the future users and taking organizational measures, necessary to effectively exploit the system. Testing the system by the user is necessary in order to see the way the system carried out will satisfy the direct future beneficiary. This represents the validation of user, named also as acceptance

The exploitation and the progress of DSS are important because the efficiency and the costs on the entire life cycle of the system are essential aspects. The validated system in action is provided with documentation of using and maintenance issued, and can be carried out in current exploitation. The further modifications of the tasks, preferences of users can deter‐

The selection process and the effective use of a DSS generator will influence powerfully both the solution achieved, as well as the way one can reach to it. Within the system analysis, a stock-taking of the DSS generators already existing on the market is aimed. These are select‐ ed by means of a limited number of criteria, in the view of filtering those not serving on reaching the requirements included within the functional specification of details, that do not frame within the restrictions imposed by the already existing information infrastructure or in the strategy of developing the global information system of the organization, or that lead

The next stage is placed into the stage of issuing the technical project, when the alternatives selected in the previous stage should be put in order, in the view of performing a selection. In order to solve it, the following became necessary: defining the set of evaluation criteria and of weights associated to them, appreciating the value or the utility of each instrumentcandidate, viewed through all the evaluation criteria and applying a method of establishing

As regards the ordering, a set of evaluation criteria will be used: the completeness, which means that per assembly, the criteria should cover all the issues that can slope the balance towards an alternative or other; the non-redundancy, which imposes a certain issue to be taken into consideration, only by a single evaluation criterion, so that more calculations

test or operational test, and is able to determine the modification of characteristics.

freedom signifies the data basis, followed by the dialogue subsystem.

mine the need of adapting and modernization of the DSS.

towards the unacceptable overreaching of the project budget.

*2.4.2. The DSS generator*

the classification.

#### **2.4. Aspects regarding the construction of DSS architecture**

#### *2.4.1. Stages of creating a DSS process*

Creating a DSS includes a series of activities that start with generating of the idea on in‐ troducing the system within an organization, and ends by achieving the prototype: pre‐ paring the projects, the system analysis, designing, implementation and exploitation. The DSS architecture is determined by elements, such as: the central element aimed in the process of building the DSS architecture; the information platform used; the DSS builder; the form of the process (linear, based on the stages of life cycle or in cycles using the pro‐ totype); the interaction between the technical processes and those social that took place while creating an DSS.

First of all it is necessary to mention that the idea of introducing a DSS has determined a series of strengthening activities, in order to be transformed into a first specification of the future system (the diagnosis of current situation, establishing the main characteristics of the future system, evaluation of feasibility and design planning). The diagnosis consists in iden‐ tifying the current issues and presenting the opportunities, as well as the means of changing or improving, respectively. The commitment decision and the resources allotment follows, in accordance to the feasibility study, framing the project within the development politics is taken into account, as well as the company's priorities, the harmonization with the ICT in‐ frastructure, the availability of funds within context of justifying the impact over the organi‐ zation and preliminary risk management issues. Planning the project includes the orderly list of phases that follow to be developed forwards: the system analysis, the design and im‐ plementation, as well as the potential activities of maintenance. For each stage are indicated: the moment of start/ end, the expected results, the responsible persons and the other partici‐ pants, as well as the allocated resources.

In the system analysis stage, it is necessary to mention data storing and processing, take into consideration the following: the stock-taking and thorough studying of the decisional situa‐ tions, for which providing the information support is aimed; discovering the particularities of each individual that will become users of the system; identification of some frame ele‐ ments, such as: the restrictions introduces by the organization in issuing, transmitting and executing the decisions, the existing information infrastructure and the possibilities of com‐ bining with other parts of the global information system of the organization; the evaluation of results, of the previous initiatives of introducing the DSS in that organization or from oth‐ ers similar, in order to avoid some potential signalized errors.

The technical design should take into consideration both the ground system, as well as its components as regarded from ITC point of view, the content of defining specifications is transposed into an execution project. The designing process is composed of activities of es‐ tablishing the DSS structure and of defining each technological component of the system, as well as the way it integrates with the other parts of the global information system of the or‐ ganization. The designing stage is accomplished by the specification of carrying out the sys‐ tem and integrating it within the organization. The level of describing in details and of defining the components depends upon how this is allowed while using information plat‐ form. For most of applications, the component where the designer has the most levels of freedom signifies the data basis, followed by the dialogue subsystem.

In the stage of implementation, the content of technical design is transposed into an infor‐ mation system, by means of the instruments selected, and consists in activities of effective building of DSS into an application and integration within the global information system of the organization, testing and issuance of the documentation, of building the future users and taking organizational measures, necessary to effectively exploit the system. Testing the system by the user is necessary in order to see the way the system carried out will satisfy the direct future beneficiary. This represents the validation of user, named also as acceptance test or operational test, and is able to determine the modification of characteristics.

The exploitation and the progress of DSS are important because the efficiency and the costs on the entire life cycle of the system are essential aspects. The validated system in action is provided with documentation of using and maintenance issued, and can be carried out in current exploitation. The further modifications of the tasks, preferences of users can deter‐ mine the need of adapting and modernization of the DSS.

#### *2.4.2. The DSS generator*

Within the context of the combined systems, calling up the classical paradigm of DSS based on tripe structure of components can be useful: dialogue, data or models (DDM). This is an example of combined data and models oriented system, covering a part of the support pos‐ sibilities for decisions allowed, thus enabling the DSS decomposing in subsystems, and in

Creating a DSS includes a series of activities that start with generating of the idea on in‐ troducing the system within an organization, and ends by achieving the prototype: pre‐ paring the projects, the system analysis, designing, implementation and exploitation. The DSS architecture is determined by elements, such as: the central element aimed in the process of building the DSS architecture; the information platform used; the DSS builder; the form of the process (linear, based on the stages of life cycle or in cycles using the pro‐ totype); the interaction between the technical processes and those social that took place

First of all it is necessary to mention that the idea of introducing a DSS has determined a series of strengthening activities, in order to be transformed into a first specification of the future system (the diagnosis of current situation, establishing the main characteristics of the future system, evaluation of feasibility and design planning). The diagnosis consists in iden‐ tifying the current issues and presenting the opportunities, as well as the means of changing or improving, respectively. The commitment decision and the resources allotment follows, in accordance to the feasibility study, framing the project within the development politics is taken into account, as well as the company's priorities, the harmonization with the ICT in‐ frastructure, the availability of funds within context of justifying the impact over the organi‐ zation and preliminary risk management issues. Planning the project includes the orderly list of phases that follow to be developed forwards: the system analysis, the design and im‐ plementation, as well as the potential activities of maintenance. For each stage are indicated: the moment of start/ end, the expected results, the responsible persons and the other partici‐

In the system analysis stage, it is necessary to mention data storing and processing, take into consideration the following: the stock-taking and thorough studying of the decisional situa‐ tions, for which providing the information support is aimed; discovering the particularities of each individual that will become users of the system; identification of some frame ele‐ ments, such as: the restrictions introduces by the organization in issuing, transmitting and executing the decisions, the existing information infrastructure and the possibilities of com‐ bining with other parts of the global information system of the organization; the evaluation of results, of the previous initiatives of introducing the DSS in that organization or from oth‐

The technical design should take into consideration both the ground system, as well as its components as regarded from ITC point of view, the content of defining specifications is

completing or endowing with new modules, respectively.

*2.4.1. Stages of creating a DSS process*

pants, as well as the allocated resources.

ers similar, in order to avoid some potential signalized errors.

while creating an DSS.

96 Decision Support Systems

**2.4. Aspects regarding the construction of DSS architecture**

The selection process and the effective use of a DSS generator will influence powerfully both the solution achieved, as well as the way one can reach to it. Within the system analysis, a stock-taking of the DSS generators already existing on the market is aimed. These are select‐ ed by means of a limited number of criteria, in the view of filtering those not serving on reaching the requirements included within the functional specification of details, that do not frame within the restrictions imposed by the already existing information infrastructure or in the strategy of developing the global information system of the organization, or that lead towards the unacceptable overreaching of the project budget.

The next stage is placed into the stage of issuing the technical project, when the alternatives selected in the previous stage should be put in order, in the view of performing a selection. In order to solve it, the following became necessary: defining the set of evaluation criteria and of weights associated to them, appreciating the value or the utility of each instrumentcandidate, viewed through all the evaluation criteria and applying a method of establishing the classification.

As regards the ordering, a set of evaluation criteria will be used: the completeness, which means that per assembly, the criteria should cover all the issues that can slope the balance towards an alternative or other; the non-redundancy, which imposes a certain issue to be taken into consideration, only by a single evaluation criterion, so that more calculations will not be carried out in a favorable or non-favorable way; the discomposing, which re‐ quires that a criterion, seen as general or vague, might be decomposed in more simple in‐ dependent criteria; the operability, which shows that formulating the criteria should be expressed very clear, in order to be understood by all involved on taking decisions and choosing a solution; the minimalistic feature, consisting in the drawing a limit for the cri‐ teria number, so as to solve a dilemma between a fast but superficial analysis, and one that can become non-opportune, because of the attempt on taking in view another perfect solution reaching.

*2.4.4. The portfolio of models used within DSS*

provided with a high number of generating variables.

of results and potential taking over other data sets.

DSS models can be classified in accordance to the following criteria: the aim, the time varia‐ ble existence, the certainty degree, the generality, the decisional level and the issue type, in this way, the aim can signify: the understanding of decisional situations, the consequences of applying the decisional alternatives and the robustness of recommended solutions.

Emerging Applications of the New Paradigm of Intelligent Decision Making Process: Hybrid Decision Support Systems

for Virtual Enterprise (DSS-VE) http://dx.doi.org/10.5772/3371 99

Depending upon the presence or absence of time variable within the models form, these might be dynamic or static, respectively. Depending upon the certainty degree, determinis‐ tic and probabilistic models can be distinguished. Regarding from the generality point of view, a model can be used for a class of decisional issued or only into a single application. In accordance to the decisional level the strategic, tactical and operative models can be empha‐ sized, on assisting and establishing the objectives and necessary resources on log terms. These are, in generally, descriptive, dynamic, deterministic and created accurately, being

Within a DSS, more models and associated solvers can exist. As in the situation of data, models signify one of the sources with organization knowledge elements. In order to man‐ age and exploit them, without being known by the user or the application programs and without the need of explaining in details the physical aspects relative to storing the models, the existence of a management system for the portfolio of models capable to execute tasks analogous with those specific to a databases management system. The characteristics of this Models Management System (MMS) emphasize the existence of some accurate control means, for both the expert user and the new one; the flexibility or possibility of choosing or changing the preference towards the model type during the decision al process; the presence of the reaction able to indicate the stage of developing the models execution, the compatibil‐

The main functions of the MMS refers to the abilities of performing the assimilation of pro‐ cedural knowledge on designing and selecting, in the view of exploitation, in order to proc‐ ess descriptive knowledge elements, such as: creating some models in the view of storing them within the DSS models database. Creation and assimilation of new models can be ac‐ complished in more ways, meaning: selection performed in the view of storing new models from the set of the already existing products on the market, formulating new models, by the help of designing languages and using the issue's characteristics, the content of some com‐ plex models from the already existing building blocks and by integrating building modules, similar to the content, but foregoing by the operation of modifying modules; maintenance of the models base by updating and extension actions; selecting and preparing, in the view of executing the existing models and the algorithms of solving, so as to assist the activities on decisional issues solving; the execution of models and data sets, followed by the evaluation

All these assume the existence of more elements, such as: a language of designing, aimed on ensuring the creation and loading of models within the models base and by the help of solv‐ ing algorithms; models database and solving algorithms easy to be accessed; a diagram of

treating the models and algorithms, enabling the selection in the view of execution.

ity with the solutions chosen for the system of databases management system.

The DSS generator can be used in order tot test some requirements or ideas of designing, which were unclear from the start of the project, and in order to simulate the future user's interest, and of convincing the project's sponsor, after which designing an optimized and flexible system will be carried out by using primary constructive elements. In this way, the DSS generators are ideal means on building a progressive and based on prototype structure.

#### *2.4.3. Data management in DSS*

In order to achieve efficient results, quality data, well-structured and organized has be‐ come necessary. The set of attributes that characterize the data quality is different from one type of information system to another. In this way, the systems necessary to con‐ trol in real time the technological processes or to enable fast data access signify the most important feature, while high data precision within computer aided applications is compulsory. The main data models are: the hierarchical model, the network type model and the relational model. Starting from the very beginning, some issues that might oc‐ cur within a DSS could be identified, being caused by weak quality of data: the neces‐ sary data on issuing the decision does not simply exist, since none has thought that such data might be necessary; as consequence, it should be stored; the already existing data within the system are not accurate or are old; data gathered in the system are not consistent with the way of performing activities on issuing decisions, where decisions are being represented accurately. The data storage signifies the name of a specialized database, aimed on satisfying firstly the information necessities of the top managers or of the workers based on knowledge, which develop activities of strategic type within an organization. The data included within the storage come from multiple and various internal and external sources.

The main classes of operations are drawn up by: data storage by its extraction from various sources; the data conversion from the original format into the adopted format, in order to be used within the data storage; ordering the data, by identifying and correcting the conversion errors, and by completing the omissions; the internal derivation of new data from the opera‐ tive data received and processed, by means of aggregation or synthetizing actions, so that the data storage can include elements that haven't been met within the operative databases; effective loading into the chosen data structure, in order to be used in subsequent interroga‐ tions and analysis.

#### *2.4.4. The portfolio of models used within DSS*

will not be carried out in a favorable or non-favorable way; the discomposing, which re‐ quires that a criterion, seen as general or vague, might be decomposed in more simple in‐ dependent criteria; the operability, which shows that formulating the criteria should be expressed very clear, in order to be understood by all involved on taking decisions and choosing a solution; the minimalistic feature, consisting in the drawing a limit for the cri‐ teria number, so as to solve a dilemma between a fast but superficial analysis, and one that can become non-opportune, because of the attempt on taking in view another perfect

The DSS generator can be used in order tot test some requirements or ideas of designing, which were unclear from the start of the project, and in order to simulate the future user's interest, and of convincing the project's sponsor, after which designing an optimized and flexible system will be carried out by using primary constructive elements. In this way, the DSS generators are ideal means on building a progressive and based on prototype

In order to achieve efficient results, quality data, well-structured and organized has be‐ come necessary. The set of attributes that characterize the data quality is different from one type of information system to another. In this way, the systems necessary to con‐ trol in real time the technological processes or to enable fast data access signify the most important feature, while high data precision within computer aided applications is compulsory. The main data models are: the hierarchical model, the network type model and the relational model. Starting from the very beginning, some issues that might oc‐ cur within a DSS could be identified, being caused by weak quality of data: the neces‐ sary data on issuing the decision does not simply exist, since none has thought that such data might be necessary; as consequence, it should be stored; the already existing data within the system are not accurate or are old; data gathered in the system are not consistent with the way of performing activities on issuing decisions, where decisions are being represented accurately. The data storage signifies the name of a specialized database, aimed on satisfying firstly the information necessities of the top managers or of the workers based on knowledge, which develop activities of strategic type within an organization. The data included within the storage come from multiple and various

The main classes of operations are drawn up by: data storage by its extraction from various sources; the data conversion from the original format into the adopted format, in order to be used within the data storage; ordering the data, by identifying and correcting the conversion errors, and by completing the omissions; the internal derivation of new data from the opera‐ tive data received and processed, by means of aggregation or synthetizing actions, so that the data storage can include elements that haven't been met within the operative databases; effective loading into the chosen data structure, in order to be used in subsequent interroga‐

solution reaching.

98 Decision Support Systems

*2.4.3. Data management in DSS*

internal and external sources.

tions and analysis.

structure.

DSS models can be classified in accordance to the following criteria: the aim, the time varia‐ ble existence, the certainty degree, the generality, the decisional level and the issue type, in this way, the aim can signify: the understanding of decisional situations, the consequences of applying the decisional alternatives and the robustness of recommended solutions.

Depending upon the presence or absence of time variable within the models form, these might be dynamic or static, respectively. Depending upon the certainty degree, determinis‐ tic and probabilistic models can be distinguished. Regarding from the generality point of view, a model can be used for a class of decisional issued or only into a single application. In accordance to the decisional level the strategic, tactical and operative models can be empha‐ sized, on assisting and establishing the objectives and necessary resources on log terms. These are, in generally, descriptive, dynamic, deterministic and created accurately, being provided with a high number of generating variables.

Within a DSS, more models and associated solvers can exist. As in the situation of data, models signify one of the sources with organization knowledge elements. In order to man‐ age and exploit them, without being known by the user or the application programs and without the need of explaining in details the physical aspects relative to storing the models, the existence of a management system for the portfolio of models capable to execute tasks analogous with those specific to a databases management system. The characteristics of this Models Management System (MMS) emphasize the existence of some accurate control means, for both the expert user and the new one; the flexibility or possibility of choosing or changing the preference towards the model type during the decision al process; the presence of the reaction able to indicate the stage of developing the models execution, the compatibil‐ ity with the solutions chosen for the system of databases management system.

The main functions of the MMS refers to the abilities of performing the assimilation of pro‐ cedural knowledge on designing and selecting, in the view of exploitation, in order to proc‐ ess descriptive knowledge elements, such as: creating some models in the view of storing them within the DSS models database. Creation and assimilation of new models can be ac‐ complished in more ways, meaning: selection performed in the view of storing new models from the set of the already existing products on the market, formulating new models, by the help of designing languages and using the issue's characteristics, the content of some com‐ plex models from the already existing building blocks and by integrating building modules, similar to the content, but foregoing by the operation of modifying modules; maintenance of the models base by updating and extension actions; selecting and preparing, in the view of executing the existing models and the algorithms of solving, so as to assist the activities on decisional issues solving; the execution of models and data sets, followed by the evaluation of results and potential taking over other data sets.

All these assume the existence of more elements, such as: a language of designing, aimed on ensuring the creation and loading of models within the models base and by the help of solv‐ ing algorithms; models database and solving algorithms easy to be accessed; a diagram of treating the models and algorithms, enabling the selection in the view of execution.

### **2.5. The integration of artificial intelligence (AI) in DSS**

In order to increase the decisional performances, DSS can be endowed by means of artificial intelligence techniques. The artificial intelligence techniques are used for both information processing and data visualization, as well as for extracting the information from high data volumes, in the view of searching templates that might be helpful within decisions taking processes.

function and improves the solution. GA are used to solve optimization problems (near-opti‐

Emerging Applications of the New Paradigm of Intelligent Decision Making Process: Hybrid Decision Support Systems

for Virtual Enterprise (DSS-VE) http://dx.doi.org/10.5772/3371 101

The AI has studied new architectures of computing, able to: offer support in situations of unclearness and uncertainty; to use knowledge and the experience on adapting to the envi‐ ronment changes; to understand, to deduce and to analyze new situations; to recognize the relative significance of various elements in the context of fast changing of situations or to

In the view of achieving an intelligent machine, such amplitudes concerning the intelligence and related behaviors or mechanisms should integrate within the computing system. The in‐ telligent system should be able to offer a fast, soft and adaptable support, endowed by the ability of acting accurately within an uncertain and chaotic field (Meystel, Albus, 2002).

AI based technologies are able to establish both an alternative to the numeric methods, when these fail or cannot be applied due to the qualitative issues preponderance and uncer‐ tainty presence, as well as a complement of them, when the limits above mentioned can af‐

The limits of the AI based technologies should be also taken into account. For example the solution could be sensitive to decider's preferences and there is a limited ability of treating small variations of the attributes, with impact on the experiments (since the user does not have access to rules within the knowledge base). There are also technical difficulties specific to various means of knowledge representation and the transmission of the parameters be‐ tween the software components that implement the numerical methods, and those including the AI components. One of the most known AI based information technology met within the frame of decisional activities assistance is represented by the expert systems (ES). The differ‐ entiation between the DSS and ES can be expressed by: borders of the application field, which are evasive and many times variable and unpredictable in the situation of DSS, and limitary and well shaped within the ES; the historical progress that took place, since the be‐ ginning of real applications on carry out systems within DSS, respectively, from the study of abstract reasoning, in the attempts of creating some general systems of solving issues within ES; the normative, which is more pronounced within ES; the goal aimed, which consists in increasing the decisions efficiency on DSS and respectively, on growing the efficiency within the process of ES solution achieving; the user's attitude towards the system, which is of ac‐ ceptance or rejection of solutions and explanations, based on the best knowledge within ES. An ES will usually designate an AI based information technology, while and DSS will in‐

The enterprise modeling is a complex process of building integrated systems of models (process models, data models, resource models) dedicated to the managerial support of a

mal solutions), planning or search problems.

detect the ambiguous or contradictory messages.

volve, more often the idea of an application.

**3.1. Definition of concepts**

**3. Virtual enterprise VE and virtual organization VO**

fect the decisions quality.

The Artificial Intelligence (AI) is characterized by a high learning ability, in the view of con‐ tinuous improvement with or without external helping. The main applications of the AI are the following: the expert systems, the neuronal networks, the logic fuzzy, the genetic algo‐ rithms, the intelligent agents and pattern recognition. Programming languages have been especially created, such as LISP and Prolog, in the view of carrying out the research in the field, and even on creating artificial intelligence devices or programs.

One of the candidates to be incorporated in the intelligent decision making paradigm is the intelligent control (Fu, 1970) with the aim to reproduce the most important human intelli‐ gence characteristics (adaptation, learning, planning in uncertainty environments) and the capability to interpret a huge quantity of data. Based on the new approaches (artificial neu‐ ral networks - ANN, fuzzy logic - FL, genetic algorithms - GA, expert systems and hybrid systems), DSS could be reinforced via the biological inspiration (Swarm Intelligence, SI) and could solve an extended category of applications.

FL shapes the rationing of human brain based on the approximate, non-quantitative, nonbinary reasoning. Applying the FL method is performed in the following steps: defining the input-output variables, defining subsets intervals, choosing the functions, setting the if-then rules, performing calculations and adjusting the rules.

NN tries to reproduce the structure and functions of the human nervous system (consisting of a large number of interconnected neurons that determine the way in which the informa‐ tion is stored). In ANN the neurons receive inputs from other neurons through a weighted function (with increasing / decreasing signal). These signals are received and collected by the neuron, and if the amount exceeds a certain threshold, the neuron will send its own sig‐ nal to other neurons. Information is stored in the neuron input weights and the adjustments offer the ability to store different information. The storage capacity of a single neuron is lim‐ ited, but the set of neurons interconnected in several layers provide superior performance. ANN are used to solve problems of estimation, identification and predictive or problems of complex optimization. Due to independence of operations inside the components, related models have a great potential for parallelism.

Based on the Darwin's principles of genetics and natural selection, GAs are adaptive techni‐ ques for heuristic search (Holland, 1975). The biological process of evolution is based on the adaptation to the environment, the capability to survive/ evolve over generations. GA is a complex model that emulate biological evolutionary model to solve/ optimize problems. It includes a set of individual elements represented in the form of binary sequences (popula‐ tion) and a set of biological operators defined on the population. With the support of opera‐ tors, GA manipulates the most promising sequences evaluated according to an objective function and improves the solution. GA are used to solve optimization problems (near-opti‐ mal solutions), planning or search problems.

The AI has studied new architectures of computing, able to: offer support in situations of unclearness and uncertainty; to use knowledge and the experience on adapting to the envi‐ ronment changes; to understand, to deduce and to analyze new situations; to recognize the relative significance of various elements in the context of fast changing of situations or to detect the ambiguous or contradictory messages.

In the view of achieving an intelligent machine, such amplitudes concerning the intelligence and related behaviors or mechanisms should integrate within the computing system. The in‐ telligent system should be able to offer a fast, soft and adaptable support, endowed by the ability of acting accurately within an uncertain and chaotic field (Meystel, Albus, 2002).

AI based technologies are able to establish both an alternative to the numeric methods, when these fail or cannot be applied due to the qualitative issues preponderance and uncer‐ tainty presence, as well as a complement of them, when the limits above mentioned can af‐ fect the decisions quality.

The limits of the AI based technologies should be also taken into account. For example the solution could be sensitive to decider's preferences and there is a limited ability of treating small variations of the attributes, with impact on the experiments (since the user does not have access to rules within the knowledge base). There are also technical difficulties specific to various means of knowledge representation and the transmission of the parameters be‐ tween the software components that implement the numerical methods, and those including the AI components. One of the most known AI based information technology met within the frame of decisional activities assistance is represented by the expert systems (ES). The differ‐ entiation between the DSS and ES can be expressed by: borders of the application field, which are evasive and many times variable and unpredictable in the situation of DSS, and limitary and well shaped within the ES; the historical progress that took place, since the be‐ ginning of real applications on carry out systems within DSS, respectively, from the study of abstract reasoning, in the attempts of creating some general systems of solving issues within ES; the normative, which is more pronounced within ES; the goal aimed, which consists in increasing the decisions efficiency on DSS and respectively, on growing the efficiency within the process of ES solution achieving; the user's attitude towards the system, which is of ac‐ ceptance or rejection of solutions and explanations, based on the best knowledge within ES. An ES will usually designate an AI based information technology, while and DSS will in‐ volve, more often the idea of an application.
