Multi-attribute Decision-making Model Research Articles

MAPS: A Planning System for International Market Entry

This paper describes an international marketing expert system that helps managers make strategic decisions regarding market entry. The system is called MAPS, for MArket-entry Planning System. MAPS is being developed using a combination of AI knowledge representations (i.e. rules, semantic networks, text-analysis techniques) and multi-attribute decision models based on weighted linear algebraic formulas for judgment and evaluation. Previous research by the author and colleagues resulted in several components of MAPS, developed largely as stand-alone products. These components were developed using a shell called CEVAL (Candidate Evaluator). CEVAL and other AI tools are currently being used to integrate the components into a comprehensive system that addresses most of the major issues facing international market planners. This system serves both an educational and a decision-support purpose. © 1996 Wiley Periodicals, Inc.

Knowledge-based portfolio analysis for project evaluation

A computer-based expert system for the evaluation of research and development projects is presented. The system was developed for The Ministry of Science and Technology of the Republic of Slovenia and in the process of evaluation and selection of projects submitted to the annual competition for funds. The system is based on an adapted portfolio matrix that determines the position of each project with respect to its contents and feasibility. The aggregation of these criteria is carried out by a qualitative multi-attribute decision model that was developed using an expert system shell: DEX. The model consists of a tree of criteria, supplemented by if-then rules. In addition to describing these components, the paper presents and discusses a practical application of the system.

Multicriteria evaluation of automated filling systems: A case study

Abstract Much has been written about the use of multiattribute decision models to evaluate manufacturing technologies. This article describes an application to a real problem using a recently developed multiattribute decision tool. The case study involves an evaluation of three candidate technologies for filling packaged food containers. A manual baseline filling method is compared with the two technology investment options of automated weigh filling and automated volumetric filling. In food industries, the cost of material overfill is important but is not the only factor that should be considered. A multiattribute decision model is formulated that considers difficult-to-quantify criteria, such as material conversion, information conversion, and strategic activities. For the given decision scenario, when only directly measurable annual cost savings are considered, the weigh filler option is preferred; however, when the additional criteria are considered, volumetric filling is preferred primarily due to its product and equipment flexibility. It is proposed that the methodology described here will enhance the acceptability of applying multiattribute decision making to manufacturing investment problems.

Evaluating automated manufacturing technologies: Part II — a methodology for evaluation

In Part I of this paper we provided a review of the techniques used in the evaluation of automated manufacturing technologies. In this paper we provide a framework to integrate the economic, technical, strategic, and qualitative factors in the investment decision. The framework is based on combining a simulation model with a multi-attribute decision model. The simulation model is used to estimate the operational and economic attributes for the investment problem. The multi-attribute decision model is the linear additive scoring model because of its ease of use and its ability to structure multi-dimensional problems.

Multi-attribute decision model using the analytic hierarchy process for the justification of manufacturing systems

There is a general challenge offered to the field of engineering economics by the introduction of advanced technologies. A survey of the existing literature on evaluation of advanced manufacturing systems indicate that the traditional approaches are inadequate for the purpose. Typically new technologies require very high investments. The traditional approaches do not account for the significant but intangible benefits offered by new technology. In this paper, an attempt has been made to overcome the above deficiencies by presenting an approach to account for the justification of the manufacturing system. This is based on the analytic hierarchy process (AHP) and is capable of taking into account many intangible factors as well. The usefulness of the proposed approach is demonstrated through a case situation. Finally, some research directions for future work are identified.

A feedforward neural network for multiple criteria decision making

Many complex real-world problems are characterized as decision making with multiple, conflicting and noncommensurate objectives. Because of the complexity of factors that are involved, it is usually difficult to derive a decision rule for determining the most desirable alternative. This paper is to demonstrate the potential role of artificial neural networks for multiple criteria decision making. This paper presents a feedforward neural network for solving discrete multiple criteria decision problems under certainty. Starting with formulating multiple criteria decision problems under the theme of supervised learning, this paper specifies two types of multiattribute decision models, proposes a particular form of feedforward neural network, analyzes some desirable properties associated with supervised learning, presents an improved learning algorithm and discusses results of illustrative examples and numerical simulation.

Separation and Normalization in Multi-Attribute Decision Models for Investment Evaluation

ABSTRACT The separation of the evaluation attributes into two distinct subsets, objective and subjective measures, is discussed. The use of normalization over the alternatives for each subjective attribute is shown to lead to erroneous choices in certain situations.

Multi-Attribute Decision Models in the Justification of CIM Systems

ABSTRACT A general approach to building an effective justification process around a multi—attribute decision model is suggested which incorporates factory performance measurements to focus on difficult to quantify strategic and tactical benefits from Computer Integrated Manufacturing. This approach differs from previous work in that: attributes are rationally designed performance measures which form the basis for a formal feedback system, and attributes are selected to represent the spectrum of benefits known to be relevant to CIM systems. The paper focuses on selecting an appropriate set of attributes to form the decision model for evaluating the alternatives to be considered in the justification process.

A multi-stage multi-attribute decision model for project selection

In most multi-attribute decision models, much effort is directed at obtaining utility functions of the decision makers for the individual attributes that encode behaviour towards risk. A three-stage methodology is provided to simplify the process and to reduce cognitive stress for the decisionmaker. At the first stage, alternatives which are dominated and/or have significant technological uncertainties are screened out. At the second stage, concepts are modified from some psychological theories of decisionmaking to eliminate alternatives in which some or all attributes do not posses certain aspects. At the end of the first two stages, alternatives that remain have acceptable risk. At the final stage, value functions are used to obtain the preferred alternative under imprecise preference information. The methodology is applied to the real world problem of choosing a project for providing fresh water to the city of Newport News, Virginia, USA.

Reclamation of stripmined lands: a multiattribute decision model

A multiattribute utility function is proposed to model preferences among hydrologic and other outcomes of alternative reclamation schemes typical of stripmined lands in the western USA. A preference model generates the preference function of an assumed group decision-maker and ranks the reclamation schemes on the basis of the preference function. Each of the schemes, which are left unspecified, must restore the area to its premining state or better, and is composed of actions such as mining operations, preparations for postmining land use, and mitigating actions. Grazing and runoff augmentation are examples of postmining land-use goals, and mitigating actions may be measures to protect the environment, in particular, control of pollution in runoff or infiltration. Conflicting objectives are involved, such as meeting coal production targets, alleviating detrimental environmental effects, and minimizing costs. The environmental effects are further uncertain, and the problem is therefore analyzed as a multiattribute decision-making problem under uncertainty.

Multiattribute Perceptual Bias as Revealing of Preference Structure

The assumed separation between judgments of preference and perception typical of most formal multiattribute decision-making models is questioned in favor of an information-processing formulation in which systematic perceptual errors are associated with, and revealing of, underlying preferences. A model and methodology is proposed for measuring the preference-perception interaction and hypotheses about such interdependencies are tested in a laboratory experiment.

Sensitivity of multiattribute decision models to alternative specifications of weighting parameters

The issue of attribute weighting in multiattribute decision models is examined. Results are presented which show that the outputs produced by linear multiattribute models are extremely robust with respect to alternative specifications of the weighting parameters unless the number of attributes included in the models is small, the average correlation among the attributes is low, and the dispersion of the weights is large relative to their mean. Implications of these results are discussed for three different types of weighting schemes-regression weighting, equal weighting, and subjective weighting—which are used in multiattribute decision modeling.