MGA: a decision support system for complex, incompletely defined problems (original) (raw)
Related papers
Modeling to generate alternatives: A fuzzy approach
Fuzzy Sets and Systems, 1983
Modeling to generate alternatives (MGA) has been proposed as a framework for dealing with complex problems for which there are important unmodeied issues. MGA techniques are designed to provide the analyst (or decision maker) with a set of alternatives that are good with respect to modeled objectives and different from each other. Some of these alternatives may be better than others with respect to the unmodeled issues. Furthermore, by examining a set of different alternatives the analyst may gain insight and understanding. The concept of fuzziness is demonstrated here to be applicable to the MGA framework. The fuzzy approach can increase the flexibility of targets on modeled objectives as well as the flexibility of the original constraints of the model. Illustrations are provided using a linear programming model of a land use planning problem and a mixed integer programming model of a regional wastewater treatment system planning problem.
A Support System for Real-Life Decisions in Numerically Imprecise Domains
Operations Research Proceedings, 1995
This paper presents the foundations of the computer program µ, a program designed to support real-life decision problems. It enables a decision maker to work with a vague and imprecise basis for decisions and, in spite of this, reach a conclusive result. Decision situations that involve different alternatives and consequences are modelled by information bases. Every alternative in an information base has a number of consequences, which are evaluated with respect to their utility and the probability of them occurring. The alternatives are then estimated in accordance with these evaluations. A central feature of the program is that estimations can be expressed by vague and numerically imprecise statements. The evaluation method used is primarily based on the principle of maximising the expected utility, but other decision criteria such as minimax and maximin can also be used. The evaluation results in a set of admissible alternatives, which are further investigated with respect to their relative strength and also to the number of values consistent with the given domain that make them admissible. The program also includes tools for sensitivity analyses.
What Is a Decision Problem? Designing Alternatives
Multiple Criteria Decision Making, 2018
This paper presents a general framework for the design of alternatives in decision problems. The paper addresses both the issue of how to design alternatives within "known decision spaces" and on how to perform the same action within "partially known or unknown decision spaces". The paper aims at providing archetypes for the design of algorithms supporting the generation of alternatives.
Modeling by construction: a new methodology for constructing models for decision support
Proceedings of HICSS-29: 29th Hawaii International Conference on System Sciences, 1996
When to compare the fields of model building and m d e l solving / analyzing, it is the lmer that is more widely researched and understood. ThB is particularly true in respect of the construction of solving-paradigm-free representations (i.e. created not for a special solving method like e.g. linear programming) such as are needed in the case of general models for decision support, to function as problem descriptions. The process of formulating such modeh is supported badly in a conceptual and in a technical way. In this paper we focus on the conceptual way. Model building ist viewed as a creative act, which needs constructive achievement, and which is not only a mapping of reality. We provide a framework for a step-by-step conception and formulation of general models for decision support in an easyto-use and problemdriven manner. Modeling by construction is based on a theoretical 'concept of independence' which definitwn B directly derivedfrom element types of a problem. It b shown that a distinction between semantic and conceptual modeling is arguable on this basis. This concept of independence is the foundation of the definition of eight modeling levels which can function as components of the modeling process. We shall discuss these levekr in detail. We supply an example which uses a graphical modeling language carefully suited to the levek;. Some implications for the transformation operations of the graphical model are considered. We view our methodology not as a replacement of GEOFFNON'S structured modeling, but as a complementary approach to the difficult task of general decision model construction.
1985
This paper describes a problem solver called PLANET that has been developed in collaboration with a large computer manufacturing company to assist planning managers with the formulation and maintenance of planning models for resource allocation. PLANET is equipped with the primitives that enable it to preserve much of the richness of the process of the planning activity, namely, the generation of symbolic alternatives, and for the expression of domain specific knowledge which enables it to synthesize these alternatives into an overall planning model. This knowledge is maintained in a llmeta-model.w In contrast to modeling systems which allow for parametric perturbations of an algebraic model, PLANET1s meta-model provides it with the capability for systematic variations in the symbolic model assumptions, with concomitant structural variations induced in the algebraic model that reflect the interdependencies of those assumptions. Whenever previously held assumptions change, PLANET uses the existing model as a point of departure in formulating the revised plan. In this way, the program is able to take cognizance of the ongoing nature of organizational problem solving, and can serve an important decision support function in maintaining and reasoning about evolving plans. Center for Digital Economy Research Stem School of Business IVorking Paper IS-85-24 ''A good human decision support staff has two jobs to do. First it must reduce the set of all possible actions to the few that look potentially realistic, feasible, and good. It is this small handful that the top level decision maker actually considers when he reaches his final decision. Second, both in winnowing through the alternatives, and in projecting their consequences, the staff somehow must deal directly with the interrelations among the various parties involved. This is the only way it can hope to apply its knowledge about the parties, their goals, their resources, and the constraints under which they must operate. In general, however, we simply do not yet know how to incorporate such knowledge in numerical projection models. As a result, there is a real ceiling to what we can expect of decision support systems cast in current molds." (Reitman, 1981). Perhaps a more serious limitation of existing computer-based systems is their inability to take cognizance of the ongoing, evolutiona_lly: nature of organizational problem solving, that is, to preserve and reason about previous decisions and changes to them-something that is an integral part of a manager's job. If we pose Reitman's question again, we realize that many good alternatives a > in fact generated or synthesized in the course of formulating a plan. However, only a small subset of these become part of the "finalff plan and reflected in the algebraic model that is derived from it. Unfortunately, much of the knowledge about issues and choices that were available, and the rationales for choosing or rejecting alternatives end up in filing cabinets or voluminous reports, often permanently. This is not altogether surprising. Given the effort involved in formulating the plan in the first place, and the difficulty of coordinating the diverse inputs from the various parties involved, a systematic assessment of the ramifications of changes can become overwhelming. Yet, in the absence of this knowledge, the existing algebraic model provided by a modeling system can have the effect of unnecessarily confining users to its limited view of an inherently flexible situation. For such problems, the real decision support needed is not in helping fine tune an existing model, but one of exposing a decision maker to the multiple perspectives brought about by changes in assumptions, and of interactively assisting in the reformulation model of the situation.
PLANET: An Intelligent Decision Support System for Resource Planning in Manufacturing Organizations
1985
This paper describes a problem solver called PLANET that has been developed in collaboration with a large computer manufacturing company to assist planning managers with the formulation and maintenance of planning models for resource allocation. PLANET is equipped with the primitives that enable it to preserve much of the richness of the process of the planning activity, namely, the generation of symbolic alternatives, and for the expression of domain specific knowledge which enables it to synthesize these alternatives into an overall planning model. This knowledge is maintained in a llmeta-model.w In contrast to modeling systems which allow for parametric perturbations of an algebraic model, PLANET1s meta-model provides it with the capability for systematic variations in the symbolic model assumptions, with concomitant structural variations induced in the algebraic model that reflect the interdependencies of those assumptions. Whenever previously held assumptions change, PLANET uses the existing model as a point of departure in formulating the revised plan. In this way, the program is able to take cognizance of the ongoing nature of organizational problem solving, and can serve an important decision support function in maintaining and reasoning about evolving plans. Center for Digital Economy Research Stem School of Business IVorking Paper IS-85-24 ''A good human decision support staff has two jobs to do. First it must reduce the set of all possible actions to the few that look potentially realistic, feasible, and good. It is this small handful that the top level decision maker actually considers when he reaches his final decision. Second, both in winnowing through the alternatives, and in projecting their consequences, the staff somehow must deal directly with the interrelations among the various parties involved. This is the only way it can hope to apply its knowledge about the parties, their goals, their resources, and the constraints under which they must operate. In general, however, we simply do not yet know how to incorporate such knowledge in numerical projection models. As a result, there is a real ceiling to what we can expect of decision support systems cast in current molds." (Reitman, 1981). Perhaps a more serious limitation of existing computer-based systems is their inability to take cognizance of the ongoing, evolutiona_lly: nature of organizational problem solving, that is, to preserve and reason about previous decisions and changes to them-something that is an integral part of a manager's job. If we pose Reitman's question again, we realize that many good alternatives a > in fact generated or synthesized in the course of formulating a plan. However, only a small subset of these become part of the "finalff plan and reflected in the algebraic model that is derived from it. Unfortunately, much of the knowledge about issues and choices that were available, and the rationales for choosing or rejecting alternatives end up in filing cabinets or voluminous reports, often permanently. This is not altogether surprising. Given the effort involved in formulating the plan in the first place, and the difficulty of coordinating the diverse inputs from the various parties involved, a systematic assessment of the ramifications of changes can become overwhelming. Yet, in the absence of this knowledge, the existing algebraic model provided by a modeling system can have the effect of unnecessarily confining users to its limited view of an inherently flexible situation. For such problems, the real decision support needed is not in helping fine tune an existing model, but one of exposing a decision maker to the multiple perspectives brought about by changes in assumptions, and of interactively assisting in the reformulation model of the situation.
Computer-aided decision making
Computer Applications in Sustainable Forest Management
Several major classes of software technologies have been used in decision making for forest management applications over the past few decades. These computer-based technologies include optimization, expert systems, network models, multi-criteria decision making, and integrated systems. Each technology possesses unique advantages and disadvantages, and has been applied differentially to decision making in forestry. Several example DSS highlight the incorporation of these various technologies for vastly different management problems. Likely future development trends for decision support technologies over the next few decades include: Internet implementations, agent-based applications, increased social science components, and participatory decision making. As with most other computer applications, in general, we expect that decision support will transition to ever smaller devices that will take advantage of ubiquitous computing.
Solution Generation with Qualitative Models of Preferences
Computational Intelligence, 2004
We consider automated decision aids that help users select the best solution from a large set of options. For such tools to successfully accomplish their task, eliciting and representing users' decision preferences is a crucial task. It is usually too complex to get a complete and accurate model of their preferences, especially regarding the trade-offs between different criteria.We consider decision aid tools where users specify their preferences qualitatively: they are only able to state the criteria they consider, but not the precise numerical utility functions. For each criterion, the tool provides a standardized numerical function that is fixed and identical for all users and used to compare solutions. To compensate for the imprecision of this qualitative model, we let the user choose among a displayed set of possibilities rather than a single optimal solution. We consider the probability of finding the most preferred solution as a function of the number of displayed possibilities and the number of preferences. We present a probabilistic analysis, empirical validation on randomly generated configuration problems and a commercial application. We provide mathematical principles for the design of the selection mechanism, guaranteeing that users are able to find the target solution.
Modelling in Decision-Making Support Systems
2017
The paper addresses the application of mathematical modeling in decision making support systems. The new work of the author of such papers as “Common Approaches to Providing Automated Decision Making Support” and “Automating the Decision Making Support” analyses the main trends of decision making support systems, in the first place, in terms of application of prediction tools as part of such systems. The book is intended to developers of applied software of automated management systems. Author ORCID: 0000-0003-4759-2931 Researcher ID: E-7986-2016