سال انتشار: ۱۳۸۴
محل انتشار: چهارمین کنفرانس ملی مهندسی صنایع
تعداد صفحات: ۱۶
Iraj Mahdavi – Assistant Professor , Industrial Engineering Department ,Mazandaran University of Science & Technology, Babol, Iran.
Mohsen Akbarpour Shirazi – Assistant Professor , Faculty of Industrial Engineering ,K. N. Toosi University of Technology ,Tehran, Iran
Babak Shirazi – Lecturer, Industrial Engineering Department, Mazandaran University of Science & Technology, Babol, Iran
Classical models of decision making and system optimization with multiple criteria and complete Multiple Attribute Decision Making (MADM) matrix, use specific methods that categorized classic MADM patterns .But in some situations, MADM matrix is not distinguished completely at the first stages of decision making phase, because of the complexity of environment and uncontrollable variables. These complexities would lead to incomplete cognition and non-optimal decision making. In fact in this type of decision making, complete MADM matrix is vague from cognition range of Decision Maker (DM) .We called these forms “semi-structured MADM”. In semi-structured environment, due to its high degree of complexity, the whole environment is not identifiable for DM. Therefore, decision analysis would be complicated. We design autonomous agents 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 model which has been proposed is a systematic approach for semi-structured MADM with multi-layer mathematical model. Each layer core constructed form OR rules and helps to recognize environment sequentially. The agent’s Stepwise Response Generator moves in semi-structured environment over decision surface step by step to generate hidden alternatives that DM was unable to recognize them. The new alternatives follow Feasibility Analyzer and Dynamic Filter Module. The procedure is continued with a closed loop feedback which results in construction of the Meta-Decision phase.