Multi-Criteria Decision Analysis and Support Systems Minitrack

Permanent URI for this collection

https://hdl.handle.net/10125/42372

Most decisions involve multiple objectives. A decision alternative is chosen based on a multitude of often-conflicting decision criteria, and a solution is sought that provides the best compromise with respect to these various desired objectives. Multi-criteria decision-making (MCDM), over the last forty years, has become an established field of research, with extensive theory, a wide choice of solution methods, and a number of available computer-based decision support packages. Many general software tools, such as linear programming packages and electronic spreadsheets that do not implement specific MCDM techniques, can also be used to analyze multi-criteria problems. Multi-criteria decision support (MCDSS) may focus on various stages of the decision making process, from problem exploration and structuring, to discovering the decision-maker’s preferences and the most preferred compromise solution.

Though some MCDSS are commercially marketed packages, many more are experimental software developed to demonstrate the salient points of a proposed solution method. In assessing MCDSS, it is important to consider not only the technology (i.e., computer hardware and software, and underlying algorithms) aspects, but also the role of the decision-maker in the solution process, and the usability of the system.

The International Society on Multiple Criteria Decision Making is the primary academic and professional organization focused on MCDM, and the bi-annual International Conference on MCDM sponsored by this organization is the primary conference dealing with this topic area. However, this organization and conference focus primarily on the underlying theory and on methodological aspects of the field, rather than on systems or technology designed to support decision-making. The Hawaii International Conference on Systems Sciences appears to be a most suitable outlet for research focusing on the application of technology and systems to multi-criteria decision-making.

Possible topics for this minitrack may include:

Success factors for MCDSS
The role of the decision-maker in effective multi-criteria decision support
Case studies of multi-criteria decision support
Design of technology for specific aspects or phases of multi-criteria decision support
Decision support for project selection
Decision support for specific applications domains (e.g. health care)
Classifications of decision problems and solution technology
Tools and techniques for multi-criteria portfolio selection
Solution approaches to special types of decision problems involving conflicting objectives
Quality and types of data in multi-criteria decision support

Minitrack Co-Chairs:

Rakesh Sarin (Primary Contact)
University of California Los Angeles
Email: rakesh.sarin@anderson.ucla.edu

H. Roland Weistroffer
Virginia Commonwealth University
Email: hrweistr@vcu.edu

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Now showing 1 - 5 of 5

Emotion and Concentration Integrated System: Applied to the Detection and Analysis of Consumer Preference
(2017-01-04) Hsu, Wei-Yen; Lu, Jun-Yi; Chien, Chih-Chia; Hsieh, Meng-Chiu; Wang, Yu-Hsiang
With the expansion of consumer market, the appearance becomes an important issue when consumers make decisions under the situation of similar qualities and contents. Accordingly, to attract consumers, companies cost and take much attention on product appearance. Compared to using questionnaires individually, obtaining humans’ thoughts directly from their brains can accurately grasp the actual preference of consumers, which can provide effective and precious decisions for companies. \ In this study, consumers’ brainwaves which are related to concentration and emotion are extracted by wearing a portable and wireless Electroencephalography (EEG) device. The extracted EEG data are then trained by using perceptron learning algorithm (PLA) to make the judgments of concentration and emotion work well with each subject. They are then applied to the detection and analysis of consumer preference. Finally, the questionnaires are also performed and used as the reference on training process. They are integrated with brainwaves data to create one prediction model which can improve the accuracy significantly. The Partial Least Squares is used to compare the correlation between different factors in the model, to ensure the test can accurately meet consumers’ thoughts.
Assessing U.S. Travelers’ Trade-offs for Aviation Safety Objectives: A Natural Experiment
(2017-01-04) Nguyen, Kenneth; Rosoff, Heather; John, Richard
Understanding air travelers’ values for aviation safety is essential to design effective and well-accepted security measures. This study investigates changes in U.S. travelers trade-offs for passenger screening objectives using the occurrence of an international aviation incident (loss of Malaysian Airline Flight 370) as a natural experiment. We also examine how alternative screening procedures affect trade-offs between equity and safety concerns. Results show evidence for an enduring effect of the aviation incident on trade-offs between safety and other passenger screening objectives. Additionally, the use of different procedures to select high-risk passengers for enhanced screening altered the relative importance of the equity objective. Implications for the design of future airport security policies are discussed.
A Stochastic Programming Approach to the Design Optimization of Layered Physical Protection Systems
(2017-01-04) Brown, Nathanael; Jones, Katherine; Bandlow, Alisa; Waddell, Lucas; Nozick, Linda
The performance of many of the technologies used in physical protection systems that guard high-value assets are heavily influenced by weather and visibility conditions as well as intruder capabilities. This complicates the already difficult problem of optimizing the design of multi-layered physical protection systems. This paper develops an optimization model for the automatic design of these systems with explicit consideration of the impact of weather and visibility conditions as well as intruder capabilities on system performance. An illustrative case study is provided.
A Group Recommender for Investment in Microgrid Renewable Energy Sources
(2017-01-04) Mengash, Hanan; Brodsky, Alexander
Integration of renewable energy sources such as photovoltaic arrays and wind turbines into electric power microgrids can significantly reduce greenhouse gas (GHG) emissions. However, deciding on investment in microgrid renewable energy sources is a complex problem due to (1) the space of alternatives which is exponential in a number of components; (2) the complex interactions between old and new equipment in every time interval over an investment time horizon; (3) the multiple criteria that should be considered such as net present value, GHG emissions, and system reliability; and (4) dealing with a group of decision makers with diverse priorities. In this paper, we propose and report on the development of a Power Microgrid Operation and Investment Recommender (PMOIR) to guide a group of decision makers toward investment decisions on microgrid renewable energy sources. This is done under the assumption of optimal operational control over the investment time horizon. PMOIR uses a framework of extracting user preferences, estimating the group utility, optimizing and diversifying a small number of recommended alternatives, and voting. To support optimization, we mathematically model different power components and formalize the overall optimization problem, which is implemented using a mixed integer linear programming model. We also conduct an experimental study to demonstrating PMOIR feasibility, in terms of computational time, to be applied on microgrids involving 200 power components, over a five-year time horizon, with around 8 million binary variables.
Introduction to Multi-Criteria Decision Analysis and Support Systems Minitrack
(2017-01-04) Rakesh, Sarin; Weistroffer, Heinz Roland

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