Iraj Mahdavi et al./Asia Pacific Management Review (2006) 11(3), 155-162 155 Conceptual Framework for Quantitative Modeling of Semi-structured MADM Iraj Mahdavi a∗ , Babak Shirazi a , Namjae Cho b and Nezam Mahdavi-Amiri c a Department of Industrial Engineering, College of Technology, Mazandaran University of Science & Technology, PO Box734, Babol, Iran b School of Business, Hanyang University, 17 Haegdang-dong, Seongdong-gu, Seoul, Korea c Faculty of Mathematical Sciences, Sharif University of Technology, Tehran, Iran Accepted in June 2006 Available online Abstract In some situations, MADM matrix is not distinguished completely at the first stage of decision making, because of the complexity of environment. These complexities lead to incomplete cognition and non-optimal decision making. In such “semi-structured” environment, due to its high degree of complexity, the whole environment is not identifiable for Decision Maker (DM). We design an autonomous agent for semi-structured MADM that solves problems when alternatives have incomplete structure and DM is not able to recognize the whole alternatives of the environment for optimal decision making. The proposed model is a systematic approach for semi-structured MADM with multi-layer mathematical model. The Agent’s Stepwise Response Generator (ASRG) moves in semi-structured environment over decision surface step by step to generate hidden alternatives. The new alternatives are designed to go through Feasibility Analyzer and Dynamic Filter Module. The procedure is continued with a closed loop feedback which results in the construction of the Meta-Decision phase. Keywords: Autonomous agent; Decision surface; Meta-decision; Semi-structured MADM ∗ Email: irajarash@rediffmail.com 1. Introduction Multi Attribute Decision Making (MADM) is the most well known branch of decision making. It is a branch of a general class of Operations Research (OR) models which deal with decision problems under the presence of a num- ber of decision criteria (Triantaphyllou et al., 1998). This class of models is very often called Multi Criteria Decision Making (MCDM). According to many authors (Zimmermann, 1996), MCDM is divided into Multi Ob- jective Decision Making (MODM) and Multi Attribute Decision Making (MADM). MODM studies problems in which the decision space is continuous. A typical example is mathematical programming problems with multiple ob- jective functions. On the other hand, MADM concentrates on problems with discrete decision spaces. In these prob- lems the set of decision alternatives tends to be predeter- mined. Although MADM methods vary widely, many of them have certain aspects in common (Chen and Hwang, 1992). Each MADM problem is associated with multiple attributes. Attributes are also referred to as “goals” or “de- cision criteria”. Attributes represent different dimensions from which the alternatives can be viewed and measured. In cases where the number of attributes is large, attributes can be arranged in a hierarchical manner. That is, some attributes are defined as major attributes. Each major at- tribute is associated with several sub-attributes. Similarly, each sub-attribute may be further associated with several sub-sub-attributes and so on. Since different attributes represent different dimensions of an alternative, they may conflict with each other. For instance, cost may conflict with profit, etc. Most of the MADM methods require that attributes be associated with weights of importance. Usu- ally, these weights are normalized to add up to one. Sev- eral methods have been proposed for solving multi-attribute decision making problems. A major criti- cism to MADM is that different techniques yield different results when applied to the same problem. A simulation comparison of selected methods was performed by Zanakis et al. (1998). 2. Multi Attribute Decision Making During the recent decades, the classical decision making of optimization with one criterion or one objective function has evolved into Multiple Criteria Decision Making (MCDM) models for complex decision making problems. These models can be linear, nonlinear, or hybrid. Two categories of decision making with multiple criteria are identified. Multiple Objective Decision Making (MODM) model is used to support planning and MADM is designed to select the best alternative (Hwang and Yoon, 1971; Triantaphyllou et al., 1998). Full-structured MADM model is formulated in the form of decision mak- ing matrix as shown in Table 1. Asia Pacific Management Review (2006) 11(3), 155-162