Systematic decision process for intelligent decision making

Authors:
Behnam Malakooti
Affiliations:
Systems Engineering, Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA 44106
Venue:
Journal of Intelligent Manufacturing
Year:
2011

Citing 6
Cited 2

Remarks on the analytic hierarchy process

Management Science
An exposition of the AHP in reply to the paper “remarks on the analytic hierarchy process”

Management Science
A clarification of “remarks on the analytic hierarchy process”

Management Science
Methods for decision making with cardinal numbers and additive aggregation

Fuzzy Sets and Systems
Introduction to Linear Regression Analysis, Solutions Manual (Wiley Series in Probability and Statistics)

Introduction to Linear Regression Analysis, Solutions Manual (Wiley Series in Probability and Statistics)
Ranking and screening multiple criteria alternatives with partialinformation and use of ordinal and cardinal strength of preferences

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Multi-objective energy aware multiprocessor scheduling using bat intelligence

Journal of Intelligent Manufacturing
Model development for quality features of resistance spot welding using multi-objective Taguchi method and response surface methodology

Journal of Intelligent Manufacturing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, Systematic decision process (SDP) for solving Multiple Criteria Decision Making problems with application for manufacturing location selection is introduced. SDP is a comprehensive approach which is based on eliciting strength of preferences for assessing additive utility functions. SDP consists of three steps: I. assessing weights, II. assessing qualitative criteria, and III. ranking alternatives using the assessed additive utility function. Strengths of preferences can be expressed by using either qualitative or numerical ratings. If the decision maker is inconsistent in his/her responses, such inconsistencies are identified by the method. It is shown that the method has advantages in terms of simplicity and accuracy compared to existing methods such as Analytical Hierarchy Process. Furthermore, a quadratic optimization method for assessing weights of additive utility function by use of pair comparison of actual alternatives is developed. Computational experiments are provided.