Hierarchical Decision Model Research Articles

A hierarchical decision model for optimum design alternative selection

Head injury criterion test at Rockwell Collins Flight Dynamics is a requirement for many new government regulatory agency certifications of head-up display systems. A number of design alternatives are available which would meet the regulatory requirement, and a number of seemingly disparate considerations are taken into account when a decision is made regarding the optimum regulatory compliance strategy. No formal method has been used to quantify the decision parameters and integrate them into a decision method. This paper presents a methodology based on hierarchical decision models.

Hierarchical modelling applied to agriculture: wheat planting decisions in the Pacific Northwest

This paper presents a hierarchical decision model that predicted Alturas soft white spring wheat would be the best spring wheat variety to plant in the Pacific Northwest in 2006. Profitability depends on changing market demands and competition with other crops. The challenge of wheat farming is sustaining profitability. A four level hierarchical decision model approach was selected for this research. The top level or mission was to maximise profitability. Quality, yield and operating costs goals were placed at the next level of the hierarchy, and grain, milling, end-product quality, pest resistance and seed costs were placed at a third level. Wheat varieties were placed at the lowest level. The model predicted which wheat to plant from the lowest level would have the highest likelihood of achieving the mission, at the highest level, of maximising the profit. Pairwise comparisons were completed for each level of the model. Quantitative data was collected for three soft white spring wheat cultivars, two hard red spring wheat cultivars, and two hard white spring wheat cultivars.

AN ANALYTICAL APPROACH TO BUILDING A TECHNOLOGY DEVELOPMENT ENVELOPE (TDE) FOR ROADMAPPING OF EMERGING TECHNOLOGIES

This paper presents the research on the development of a new concept and methodology called Technology Development Envelope (TDE). The TDE approach is applied to identify the optimum path in developing a technology roadmap in which the company's technology strategies and business strategies are combined. TDE allows the executive level decision makers in corporations, as well as the policy level decision makers in governments to incorporate emerging technologies into the development of technology strategies. The combination of Delphi method and Hierarchical Decision Modeling (HDM) is used as a foundation for building the TDE concept. The judgments from technology developers and technology implementers are utilized in the process to ensure that the technology strategies are in full support of corporate goals and objectives.

A fuzzy analytic network process for multi-criteria evaluation of contaminated site remedial countermeasures

The Analytic Network Process (ANP) has been proposed to incorporate interdependence and feedback effect in the prioritization of remedial countermeasures using a hierarchical network decision model, but this approach seems to be incapable of capturing the vagueness and fuzziness during value judgment elicitation. The aim of this paper is to present an evaluation method using a fuzzy ANP (FANP) approach to address this shortcoming. Triangular fuzzy numbers (TFN) and their degree of fuzziness are used in the semantic scale as human judgment expressed in natural language is most often vague and fuzzy. The method employs the alpha-cuts, interval arithmetic and optimism index to transform the fuzzy comparative judgment matrix into set of crisp matrices, and then calculates the desired priorities using the eigenvector method. A numerical example, which was drawn from a real-life case study of an uncontrolled landfill in Japan, is presented to demonstrate the process. Results from the sensitivity analysis describe how the fuzziness in judgment could affect the solution robustness of the prioritization method. The proposed FANP approach therefore could effectively deal with the uncertain judgment inherent in the decision making process and derive the meaningful priorities explicitly from a complex decision structure in the evaluation of contaminated site remedial countermeasures.

Fuzzy Multiple Attribute Decision Making for Evaluating Aggregate Risk in Green Manufacturing

Industrial risk and the diversification of risk types both increase with industrial development. Many uncertain factors and high risk are inherent in the implementation of new green manufacturing methods. Because of the shortage of successful examples and complete and certain knowledge, decision-making methods using probabilities to represent risk, which need many examples, cannot be used to evaluate risk in the implementation of green manufacturing projects. Therefore, a fuzzy multiple attribute decision-making (FMADM) method was developed with a three-level hierarchical decision-making model to evaluate the aggregate risk for green manufacturing projects. A case study shows that the hierarchical decision-making model of the aggregate risk and the FMADM method effectively reflect the characteristics of the risk in green manufacturing projects.

A cognitive and behavioral hierarchical decision‐making model of college students' alcohol consumption

AbstractThis study examined college students' drinking behaviors from a consumer behavioral perspective. A consumer‐oriented, cognitive, and behavioral‐hierarchical decision‐making model was developed and tested with the use of structural equation modeling. Questionnaires were collected from undergraduate students ( n = 269) attending a large state university in the southwestern United States. Results indicated that personal values can serve as significant predictors of the attitudes college students have toward alcohol use, which in turn can predict intentions to drink. Results also indicated that intentions to drink are strongly related to actual alcohol consumption, and that gender‐predicted attitudes and alcohol use during the past month influences intentions to drink. However, the structure of the model was similar after controlling for gender differences in alcohol use and for the stability of past behavior. The model delineated the specific roles played by personal values (self‐actualizing values vs. social–hedonic values) and attitudes toward the physical and psychological consequences of drinking. Overall, social–hedonic values and psychological consequences predicted actual alcohol consumption far better than did self‐actualizing values and physical consequences, respectively. Theoretical and practical implications are discussed, and directions for future research are suggested. © 2005 Wiley Periodicals, Inc.

A Hierarchical Decision-Making Model for Progress in Reducing Three Evils: Terrorism, Poverty and Environmental Degradation

A Hierarchical Decision-Making Model for Progress in Reducing Three Evils: Terrorism, Poverty and Environmental Degradation

A Hierarchical Decision Model for Re-manufacturing and Re-use

This paper presents a model to facilitate managerial decision-making for re-manufacturing and re-use. A system designed for product re-use relies on product returns. Reverse logistics models assume that the product returns process is exogenous and outside the control of the firm. The proposed approach is unique because it allows financial incentives to control product returns. That way timing, quantity and product quality as well as associated logistics functions are more predictable. The model consists of three stages and is intended for decision support in product acquisition, operational planning and control, as well as demand management and product pricing.

A function-decomposition method for development of hierarchical multi-attribute decision models

Function decomposition is a recent machine learning method that develops a hierarchical structure from class-labeled data by discovering new aggregate attributes and their descriptions. Each new aggregate attribute is described by an example set whose complexity is lower than the complexity of the initial set. We show that function decomposition can be used to develop a hierarchical multi-attribute decision model from a given unstructured set of decision examples. The method implemented in a system called HINT is experimentally evaluated on a real-world housing loans allocation problem and on the rediscovery of three hierarchical decision models. The experimentation demonstrates that the decomposition can discover meaningful and transparent decision models of high classification accuracy. We specifically study the effects of human interaction through either assistance or provision of background knowledge for function decomposition, and show that this has a positive effect on both the comprehensibility and classification accuracy.

Predicting Patient’s Long-Term Clinical Status after Hip Arthroplasty Using Hierarchical Decision Modelling and Data Mining

Construction of a prognostic model is presented for the long-term outcome after femoral neck fracture treatment with implantation of hip endoprosthesis. While the model is induced from the follow-up data, we show that the use of additional expert knowledge is absolutely crucial to obtain good predictive accuracy. A schema is proposed where domain knowledge is encoded as a hierarchical decision model of which only a part is induced from the data while the rest is specified by the expert. Although applied to hip endoprosthesis domain, the proposed schema is general and can be used for the construction of other prognostic models where both follow-up data and human expertise is available.

Dynamic scheduling of process groups

In this paper we introduce the concept. of temporal locality of communication [or process groups and a hierarchical decision model [or dynamic scheduling of process groups. Empirical evidence suggests that, once a member of a process group starts to communicate with other processes in the group, it will continue to do so, while an independent process will maintain its state of isolation for some time. Other instances of inertial behavior of programs arc known. Temporal and spatial locality of reference are example of inertial behavior of programs, exploited by hierarchical storage systems; once a block of information (program or data) is brought into faster storage, it. is very likely that It will be referenced again within a short time frame. When process groups exhibit temporal locality of communication, this information can be used to hide the latency of paging and I/O operations, to perform dynamic scheduling to reduce processor fragmentation, and to identify optimal instances of time for check-pointing of process groups. In our scheduling model the supervisory process of a process group collects information about the dynamics of the group and shares it with local and global scheduling agents . • Work supported in part by NSF grants BlR-9301210 and MCR-9527131, by the Scalable 1/0 Initiative, by a grant from Intel Corporation and by CNPq Brazil

Hierarchical decision-making model for planning and controlling stochastic projects

We will consider a three-level decision-making model for controlling stochastic network projects. The upper level (the company level) is faced with the problem of optimal budget allocation among several projects. We will consider network projects with random activity durations and alternative outcomes in key nodes. There are two different types of alternative events. The first one reflects stochastic (uncontrolled) branching of the project development. The alternative event of the second type is of a deterministic nature, i.e., the project's manager chooses the outcome direction. At the medium level (project level) the management determines on the basis of the available budget an optimal joint variant together with an optimal outcome direction at every decision node which is reached in the course of the project realization. At the subnetwork level the optimal joint variant is controlled by reallocating the remaining budget among the remaining activities to raise the confidence probability of meeting the project's due date on time. This paper is a further development of our current publications. The main goal of the paper is to develop a unified three-level decision-making model and to indicate planning and control actions and optimization problems for all levels. A numerical example is presented.

A new concept of job shop scheduling system — hierarchical decision model

The function of scheduling is to determine when the product is to be completed, i.e. to make the schedule of the order, and to manufacture the product on schedule. In job shop environment, productive activities are started when the customer order arrives at the shop and are finished when the product is completed. Consequently, job shop scheduling system has to cover the time period between the arrival of the customer order and the completion of the product. Examined the accuracy of informations essential to scheduling, they are rough when the customer order arrived, and it becomes accurate step by step. Due to the range scheduling system must cover and to the accuracy of informations, decisions should be made hierarchically in job shop scheduling. Looking over the researches on job shop scheduling, almost all researches have focused on dispatching problem. Dispatching problem is a decision made in job shop scheduling system, to be sure, but models of those researches have defects in basic structure of the system. It is out of bounds of the possibility that order is put into object of dispatching directly after it arrived at the shop. Scheduling is a set of decisions to be made hierarchically by its very nature. Therefore, in this paper, a new concept of job shop scheduling system which consists of five decision systems such as order entry, master scheduling, job scheduling, operation scheduling and dispatching is proposed.

Qualifier‐2: Knowledge‐Based System for Contractor Prequalification

Among other concerns, contractor prequalification involves the evaluation of a candidate contractor's reputation, past performance, financial stability, experience record, firm capacity, current workload, and technical expertise. Qualifier‐2, a prototype knowledge‐based expert system that aids construction owners in performing contractor prequalification, is presented in this paper. A description of a previously developed algorithmic model for contractor prequalification, Qualifier‐1, is also discussed and its shortcoming are outlined. The hierarchical decision model applied in Qualifier‐2 is described along with sample decision rules implemented within the developed system followed by a description of the prototype system's structure and facilities. The system prompts the user for pertinent contractor data and performs the evaluation. A help, explanation, and recommendation facility have been included to aid the user during a consultation. The system provides an automated contractor evaluation process through which rational and consistent prequalification decisions can be made.

An artificial intelligence modelling approach to simulating animal/habitat interactions

Ecological modellers have begun to recognize the potential of object-oriented programming techniques in structuring models. However, little has been done to take advantage of artificial intelligence's (AI) symbolic representations to model the decision-making processes of animals. Here, a generic model of animal-habitat interaction and a specific model of moose-, Alces alces L., forest interactions in Finland are described that are event-driven and behavior-based. Individual level simulation is accomplished through an object-oriented knowledge representation scheme and AI techniques to implement a hierarchical decision-making model of behavior. The habitat is likewise represented in an object-oriented scheme, allowing the simulation of a heterogeneous environment. Other AI techniques for modelling behavior, memory, and actions are discussed including LISP methods, rule-based reasoning, and several search algorithms. Simulations of the moose-forest system show the power of this approach but are not intended to advance the theory of large-herbivore behavior and foraging. AI techniques are found to be most beneficial in (a) studying population processes based on individual level models of behavior, (b) modelling spatial heterogeneity, (c) building event-driven models, (d) providing a conceptual clarity to model construction, and (e) providing a structure equally well suited to simulating resource management.

Test of a Hierarchical Model of Auto Choice on Data from the National Transportation Survey

In this study a hierarchical decision model of the choice of different size cars is tested and used to make aggregate forecasts of the U.S. automobile market. The model, originally developed in a study of southern California automobile buyers, was tested using two types of national data. First, it was used to predict choices made by the new car buyers who were respondents in the National Transportation Survey done in 1978. Second, an aggregation procedure was developed to enable the model to forecast market shares for different size cars for the period 1980-1990. The predictions of the model were then compared with the actual market shares. Both tests confirm the principal prediction of the model: that there is a constant demand for cars with larger interior capacity. The model answers an important question in transportation policy: what array of fuel- efficient cars will best meet the transportation needs of U.S. families? The aggregation technique developed for the study is a straightforward method for ...

A participative approach to program evaluation

This paper discusses the methodology for the measurement of subjective values via constant-sum comparisons, the development of a Hierarchical Decision Model (HDM), and the formation of expert consensus through the hierarchical decision process. Its focus is on post-program evaluation, but the methodology is equally applicable to pre-program evaluation, ongoing program evaluation, goal formation, capital expenditures, resource allocations, project selection, project performance evaluation, and many other similar decisions in a wide range of management situations.

A hierarchical decision-process model for forecasting automobile type-choice

This article presents a hierarchical decision model based on interviews with 45 buyers of new 1978 automobiles. The methodology is borrowed from anthropological decision making studies and focuses on the transportation requirements of the car buyer rather than on characteristics of the automobile. Interior car size is found to be the most constraining decision criterion for this group of buyers. The model accounts for 42 of the 45 cases in the sample.