Multi-Criteria Decision Support System in a Distributed Environment

Topics: Decision theory, Decision making software, Decision support system Pages: 8 (2228 words) Published: September 9, 2013
EIND – 525: Multi-Attribute Analysis of Advanced Manufacturing and Service System

Multi-criteria Decision Support System in a Distributed Environment Md Mahmudur Rahman Mechanical and Industrial Engineering Department Montana State University Bozeman, MT 59717-3800, USA

Abstract

Multi-Criteria Decision Making (MCDM) has experienced a lot of advancement during the last few decades. However, the methods developed and refined in the field of MCDM mostly benefitted the corporate managers. There has been no decision support system for general people. This paper, considering “Decision Supporting in a Distributed Environment” as an area of future research, tried to provide a framework of developing a new decision support tool for general people. The new decision support tool was developed combining Multi Attribute Utility Theory (MAUT) and Hypothetical Equivalents and Inequivalents Method (HEIM). The new tool is designed to be easily understandable, easy to administer, taking less time and efficient. A step by step explanation of the new tool with the help of an example is presented in the paper. At the end, different ways to refine the tool and discussion on how to build the decision support system is presented. This type of decision support tool could be adopted by online retail sellers to provide their users a way of efficiently comparing between different alternatives.

Keywords
Multi-Criteria Decision Making, MCDM, Multi Attribute Utility Theory, MAUT, Hypothetical Equivalents and Inequivalents Method, HEIM, Decision Supporting in a Distributed Environment.

Rahman: Decision Support System in a Distributed Environment

1. Introduction
Multiple Criteria Decision Making (MCDM) can be defined as the study of methods and procedures by which concerns about multiple conflicting criteria can be formally incorporated into the management planning process [1]. MCDM evaluates the advantages and disadvantages of alternatives based on multiple criteria and produces a ranking of alternatives [2]. Usually, there does not exist a unique optimal solution for such problems and it is necessary to use decision maker’s preferences to differentiate between solutions [3].

There have been important advances in the field of MCDM since the start of the modern multiple criteria decision making discipline in the early 1960s [3]. But, MCDM have traditionally sought to support corporate managers. There has not been enough research to provide decision support systems for the general people. Wallenius et al. [4] in their paper has identified this as an area of future research and wrote: “Decision supporting a distributed environment is somewhat different from what we had expected in the early 1990s. For example, what is a user? Our fields have traditionally sought to support corporate managers. However, household consumers need support for purchasing decisions. What kind of decision support do they want in an Internet or mobile environment? The problem may not be one of having insufficient information, but rather one of having "too much" or an unknown quality of information. We may have to filter information. This is a potential application and development area for MCDM/MAUT.” This paper, considering “Decision Supporting in a Distributed Environment” as an area of future research, tried to provide a framework of developing a decision support tool for general people. This type of decision support tool could be adopted by online retail sellers to provide their users a way of efficiently comparing between different alternatives.

However, decision support system for general mass would be different from traditional MCDM. As it involves general people, there are a number of challenges to overcome:     The tool must be easy to administer. It must take less time. The contents/questions must be understandable to the general people. It must be as efficient as other validated tools.

Rahman: Decision Support System in a Distributed...

References: 1. Ruiz, F. (2012, January 12). Introduction. Retrieved from International MCDM Society: http://www.mcdmsociety.org/intro.html Pirdashti, M., Ghadi, A., Mohammadi, M., & Shojatalab, G. (2009). Multi-Criteria Decision-Making Selection Model with Application to Chemical Engineering Management Decisions. World Academy of Science, Engineering and Technology(49), 54-59. Multi-criteria decision analysis. (2012, November 27). Retrieved from Wikipedia - The Free Encyclopedia: http://en.wikipedia.org/wiki/Multi-criteria_decision_analysis Wallenius, J., Dyer, J. S., Fishburn, P. C., Steuer, R. E., Zionts, S., & Deb, K. (2008). Multiple criteria decision making, multiasttribute utility theory: Recent accomplishments and what lies ahead. Management Science, 1336-1349. Kahneman, D., Slovic, P., & Tversky, A., eds (1982). Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press. Thurston, D. L. (2006). Utility function fundamentals. In K. E. Lewis, W. Chen, & L. C. Schmidt (Eds), Decision Making in Engineering Design (pp. 15-20). New York: ASME Press. Claudio, D., & Okudan, G. E. (2010). Utility function-based patient prioritisation in the emergency department. European Journal of Industrial Engineering, 59-77. Thevenot, H. J., Steva, E. D., Okudan, G. E., & Simpson, T. W. (2006). A Multi-Attribute Utility Theory Based Approach to Product Line Consolidation and Selection. Proceedings of IDETC/CIE. Philadelphia: ASME. See, T., Gurnani, A., & Lewis, K. (2006). Muiti-Attribute Decision-Making Using Hypothetical Equivalents and Inequivalents. In K. E. Lewis, W. Chen, & L. C. Schmidt (Eds), Decision Making in Engineering Design (pp. 145-154). New York: ASME Press.
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10. Claudio, D., Okudan, G. E., & Freivalds, A. (2011). A Case Study on How to Implement MAUT in Healthcare. Proceedings of the 2011 Industrial Engineering Research Conference. 11. Tsetlin, I., & Winkler, R. L. (2007). Decision Making with Multiattribute Performance Targets: The Impact of Changes in Performance and Target Distributions. Operations Research, 226-233.
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