1 A Brief History of Decision Support Systems p. 2 A Conceptual Perspective p. 5 Decision Support vs. Transaction Processing Systems p. 8 Categorizing DSS. Editorial Reviews. Review. "This volume is an excellent book for courses on information systems, decision support systems, and data mining at the advanced . Decision Support Systems (2nd Edition) (): George M. Marakas: Books.

Decision Support System Book

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Researchers and practitioners interested in the current De- cision Support System (DSS) and the shape of future DSS are the intended audience of this book. We have compiled a list of Best Reference Books on Expert and Decision Support Systems Subject. These books are used by students of top. download Foundations of Decision Support Systems - 1st Edition. Print Book & E- Book. ISBN ,

Fedorowicz , helped define the need for such systems. She estimated in her article that only 5 to 10 percent of stored business documents are available to managers for use in decision making.

The World-wide web technologies significantly increased the availability of documents and facilitated the development of document-driven DSS. These DSS are person-computer systems with specialized problem-solving expertise. The "expertise" consists of knowledge about a particular domain, understanding of problems within that domain, and "skill" at solving some of these problems Power, That company and product made it practical to use PC based tools to develop expert systems.

Artificial Intelligence systems have been developed to detect fraud and expedite financial transactions, many additional medical diagnostic systems have been based on AI, expert systems have been used for scheduling in manufacturing operation and web-based advisory systems. In recent years, connecting expert systems technologies to relational databases with web-based front ends has broadened the deployment and use of knowledge-driven DSS.

Web-based DSS Beginning in approximately , the World-wide Web and global Internet provided a technology platform for further extending the capabilities and deployment of computerized decision support. The release of the HTML 2. In addition to Web-based, model-driven DSS, researchers were reporting Web access to data warehouses. DSS Research Resources was started as a web-based collection of bookmarks. By , the World-Wide Web Berners-Lee, was recognized by a number of software developers and academics as a serious platform for implementing all types of Decision Support Systems cf.

The goal was to provide a useful starting point for accessing Web-based material related to the design, development, evaluation, and implementation of Decision Support Systems. In , corporate intranets were developed to support information exchange and knowledge management. The primary decision support tools included ad hoc query and reporting tools, optimization and simulation models, online analytical processing OLAP , data mining and data visualization cf.

Enterprise-wide DSS using database technologies were especially popular in Fortune companies Power, In , vendors introduced new Web-based analytical applications. Many DBMS vendors shifted their focus to Web-based analytical applications and business intelligence solutions. In , application service providers ASPs began hosting the application software and technical infrastructure for decision support capabilities. More sophisticated "enterprise knowledge portals" were introduced by vendors that combined information portals, knowledge management, business intelligence, and communications-driven DSS in an integrated Web environment cf.

Power defined a Web-based decision support system as a computerized system that delivers decision support information or decision support tools to a manager or business analyst using a "thin-client" Web browser like Netscape Navigator or Internet Explorer. Conclusions DSS practice, research and technology continue to evolve.

This article used an expanded DSS framework Power, , to retrospectively discuss the historical evolution of decision support systems. In recent years, the Web has had a significant impact on the variety, distribution and sophistication of DSS, but handheld PCs, wireless networks, expanding parallel processing coupled with very large data bases and visualization tools are continuing to encourage the development of innovative decision support applications.

Decision Support Systems for Sustainable Development

Forecasters use two approaches to extrapolate the past to the future: reasoning by analogy and projection of trends. In many ways, computerized decision support systems are analogous to airplanes, coming in various shapes, sizes and forms, technologically sophisticated and a very necessary tool in many organizations. Decision support systems research and development will continue to exploit any new technology developments and will benefit from progress in very large data bases, artificial intelligence, human-computer interaction, simulation and optimization, software engineering, telecommunications and from more basic research on behavioral topics like organizational decision making, planning, behavioral decision theory and organizational behavior.

Trends suggest that data-driven DSS will use faster, real-time access to larger, better integrated databases. Model-driven DSS will be more complex, yet understandable, and systems built using simulations and their accompanying visual displays will be increasingly realistic.

Communications-driven DSS will provide more real-time video communications support. Document-driven DSS will access larger repositories of unstructured data and the systems will present appropriate documents in more useable formats.

Finally, knowledge-driven DSS will likely be more sophisticated and more comprehensive. The advice from knowledge-driven DSS will be better and the applications will cover broader domains. Current researchers should remember that Decision Support Systems pioneers came from a wide variety of backgrounds and faced many challenges that they successfully overcame to demonstrate the value of using computers, information technologies and specific decision support software to enhance and in some situations improve decision making.

The legacy of the pioneers must be preserved. The future of decision support systems will certainly be different than the opportunistic and incremental innovations seen in the past.

Decision support systems as an academic discipline is likely to follow a path similar to computer architecture and software engineering and become more rigorous and more clearly delineated and possibly renamed.

DSS consulting, teaching and research can be mutually supportive and each task can help establish a niche for those interested in building and studying DSS whether in Colleges of Information, Business or Engineering.

The history of Decision Support Systems covers a relatively brief span of years, and the concepts and technologies are still evolving. Today it is still possible to reconstruct the history of Decision Support Systems DSS from retrospective accounts from key participants as well as from published and unpublished materials.

Many of the early innovators and early developers are retiring but their insights and actions can be captured to guide future innovation in this field. It is hoped this web article leads to email and retrospective accounts that can help us understand the "real" history of DSS. The Internet and Web have speeded-up developments in decision support and have provided a new means of capturing and documenting the development of knowledge in this research area.

References Alavi, M. Alter, S. Reading, MA: Addison-Wesley, Armstrong, M. Arnott, D. Pervan, "A critical analysis of decision support systems research", Journal of Information Technology, 20, 2, , These methods require satisfactory rather than best performance in each attribute, i.

In Conjunctive method, an alternative must meet a minimal threshold for all attributes while in disjunctive method; the alternative should exceed the given threshold for at least one attribute. Any option that does not meet the rules is deleted from the further consideration [ 28 ].

Decision trees provide a useful schematic representation of decision and outcome events, provided the number of courses of action, ai, and the number of possible outcomes, Oij, not large. Decision trees are most useful in simple situations where chance events are dependent on the courses of action considered, making the chance events states of nature synonymous with outcomes [ 25 ].

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Square nodes correspond to decision events. Possible courses of action are represented by action lines which link decision events and outcome chance events. Circular nodes differentiate the outcome events from the decision events in order to underline that the decision-maker does not have control when chance or Nature determines an outcome [ 1 ].

The expected value for each course of action is achieved by summing the expected values of each branch associated with the action [ 25 ]. A decision tree representation of a problem is shown below as an example. Three strategies courses of action are investigated See Figure If the road section is replaced, it is assumed that no further capital costs will be incurred.

In this example, states of nature are the same as possible outcomes. The outcomes and associated negative payoffs costs in millions of dollars can be considered as follows:.

The expected value cost of action a2 is the lowest, based on the probability likelihood of occurrence assigned for each outcome, pij and this course of action can be followed [ 9 ].

In lexicographic analysis of problems, a chronological elimination process is continued until either a single solution is found or all the problems are solved. In this method criteria are first rank-ordered in terms of importance. The alternative with the best performance score on the most important criterion is selected.

If there are ties related to this attribute, the performance of the joined option on the next most important factor will be compared until the unique alternative is chosen [ 31 ]. The concept of cost—benefit analysis CBA originated in the United States in the s where it was used to find a solution to problems of water provision.

This method is used to estimate all the costs and benefits associated with a particular project which is usually defined in money terms, in order to weigh up whether a project will bring a net benefit to the public and to be able to compare the possible options for limited resources. It is one of the most comprehensive and at the same time the most difficult technique for decision-making [ 32 ].

According to Kuik et al. Second, certain costs and benefits which are in the social and environmental domains might be difficult to quantify in monetary terms. MAUT is based upon the use of utility functions. Utility functions are employed to quantify the preference of the decision-maker by allocating a numerical index to different degrees of satisfaction as the attribute under consideration takes values between the most and least defined limits [ 34 ].

They are considered a compliant tool of representing how much an attribute or a measure satisfies the decision-maker objectives to transform the raw performance values of the alternatives against diverse criteria, both factual quantitative and judgmental qualitative , to a general dimensionless scale [ 35 ].

They represent a means to translate attributes units into utility units. Utility functions can be specified in terms of a graph, table or mathematical expression. Mathematical expressions of utility functions include: The utility values are estimated by normalizing the output of the simulation tests. Normalization of performance measures is conducted utilizing the minimum and maximum limits that are obtained from the simulation. Moreover, they are commonly checked against the outputs and replaced if there are values beyond the limits.

This method was initially developed by Edwards [ 50 ] and is based on direct numerical ratings that are aggregated additively. In a basic format of SMART, there is a rank-ordering of action items for each criterion setting the worst to zero and the best to and interpolating between [ 27 ]. By filtering the performance values with associated weights for all criteria a utility value for each option is estimated [ 36 ].

The advantage of this approach is that the assessments are not relative; hence shifting the number of options will not change the final outcomes.

If new alternatives are likely to be added, and the action items are compliant to a rating model, then SMART can be a better option [ 37 ]. One of the limitations of this technique is that it disregards the interrelationships between parameters. However, SMART is a valuable technique since it is uncomplicated, easy and quick which is quite important for decision makers.

In SMART, changing the number of alternatives will not change the decision scores of the original alternatives and this is useful when new alternatives are added [ 37 ]. He also argued that using SMART in performance measures can be a better alternative than other methods. The AHP was suggested by Saaty and uses an objective function to aggregate various features of a decision where the main goal is to select the decision alternative that has the maximum value of the objective function [ 38 ].

The AHP is based on four clearly defined axioms Saaty [ 39 ]. The process of AHP includes three phases: Through the AHP process, problems are decomposed into a hierarchical structure, and both quantitative and qualitative information can be used to develop ratio scales between the decision elements at each level using pair wise comparisons. The top level of hierarchy corresponds to overall objectives and the lower levels criteria, sub-criteria, and alternatives.

Users are asked to set up a comparison matrix with comparative judgments by comparing pairs of criteria or sub-criteria. A scale of quantities -ranging from 1 indifference to 9 extreme preference is used to identify the users priorities.

Eventually, each matrix is then solved by an eigenvector technique for measuring the performance [ 41 ].

Preferences are then calculated from the comparison matrix by normalising the matrix, to develop the priority vector, by A. A threshold value of 0.

Table 2 shows the average consistencies of RI. Random inconsistency index, adapted from Ishizaka [ 44 ]. The advantages of the AHP method are that it demonstrates a systematic approach through a hierarchy and it has an objectivity and reliability for estimating weighting factors for criteria [ 45 ].

It can also provide a well-tested method which allows analysts to embrace multiple, conflicting, non-monetary attributes into their decision-making. Moreover if a new alternative is added after finishing an evaluation calculation, it is very troublesome because all the calculation processes have to be restarted again [ 46 ]. The limitations of AHP are of a more theoretical nature, and have been the subject of some debate in the technical literature.

Many analysts have pointed out that, the attribute weighting questions must be answered with respect to the average performance levels of the alternatives. Others have noted the possibility for ranking reversal among remaining alternatives after one is deleted from consideration.

The most important outranking methods assume data availability roughly similar to what required for the MAUT methods. Fundamental problems with most MAUT and MAUT-related methods are handling uncertain or fuzzy information and dealing with information stated in other than ratio or interval scale.

In some conditions, instead of quantitative measures descriptive expressions are frequently faced [ 48 ]. The outranking method acts as one alternative for approaching complex choice problems with multiple criteria and multiple participants. Outranking shows the degree of domination of one alternative over another and facilitates the employment of incomplete value information and, for example, judgments on ordinal measurement scale.

They provide the partial preference ranking of the alternatives, not a principal measure of the preference relation [ 48 ]. The main aim of the ELECTRE method is to choose alternative that unites two conditions from the preference concordance on many evaluations with the competitor and preference discordance was supervised by many options of the comparison. The starting point is the data of the decision matrix assuming the sum of the weights equals to 1 [ 49 ].

As shown in Eq. The calculation of the discordance index djk is more complex. If Aj performs better than Ak on all criteria, the discordance index will be zero.

Otherwise, as per Eq. The maximum of these ratios must be between 0 and 1 is the discordance index [ 27 ].

This method determines a partial raking on the alternatives. The set of all options that outrank at least one other alternative and are themselves not outranked. This method was introduced by Brans and Vincke [ 47 ], Brans et al.

The scores of the decision table need not necessarily be normalized or transformed into a dimensionless scale. Higher score value indicates a better performance.

Decision Support Systems

It is also assumed that a preference function is associated to each attribute. For this aim, a preference function PFi Aj, Ak is defined showing the degree of the preference of option A j over A k for criterion C i:. The main benefit of these preferences functions is the simplicity since there are no more than two parameters in each case. The value of this index is between 0 and 1, and characterises the global intensity of preference between the couples of choices [ 27 ]. For ranking the alternatives, the following outranking flows Eq.

The positive outranking describes how much each option is outranking the other items. By Monica Adya and Edward J. Lamelas, O. Marinoni, J. By Connor Wright, Christine W. Chan and Paul Laforge. This is made possible by the EU reverse charge method. Edited by Chiang Jao. Edited by Bishnu Pal.

Edited by Alexander Kokorin. Edited by Theophanides Theophile. Edited by Kresimir Delac. Edited by Sergey Mikhailov. October 17th DOI: Groot Open access peer-reviewed 4.English ISBN Introduction to Decision Support Systems. Correa-Perez, "The Optimization of What? The fourth and final step is implementation. Selected type: The text then elaborates on ideas in decision support, formalizations of purposive systems, and conceptual and operational constructs for building a data base knowledge system.

Watson, and S.

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