دانلود مقاله روش‌های تصمیم‌گیری چند ویژگی با الهام از شناخت در شرایط عدم قطعیت
چکیده
مقدمه
تحلیل آماری مطالعات شناختی
روش های الهام گرفته شده MADM
روند توسعه MADM با الهام از شناخت
مواد و روش ها
توزیع نشریات مرتبط
فرآیند اساسی الهام گرفتن از شناخت
MADM
طبقه بندی MADM با الهام از شناخت
مواد و روش ها
زمینه های کاربردی الهام گرفته از شناخت
روش های MADM
کاربردهای صنعتی
خدمات عمومی
مدیریت بهداشت و درمان
خلاصه ها
چالش ها و روندهای آینده
نتیجه گیری
اعلامیه ها
منابع
Abstract
Introduction
Statistical Analysis of Studies on Cognitively
Inspired MADM Methods
Development Trend of Cognitively Inspired MADM
Methods
Distribution of Related Publications
The Basic Process of Cognitively Inspired
MADM
Classification of Cognitively Inspired MADM
Methods
Application Fields of Cognitively Inspired
MADM Methods
Industrial Applications
Public Services
Healthcare Management
Summaries
Challenges and Trends for the Future
Conclusions
Declarations
References
چکیده
در دهه های اخیر، هنر و علم تصمیم گیری چند ویژگی (MADM) شاهد پیشرفت های چشمگیری بوده و در بسیاری از حوزه های فعال کاربرد پیدا کرده است. تحقیقات زیادی توانایی تکنیک های شناختی را در برخورد با اطلاعات تصمیم گیری پیچیده و نامطمئن نشان داده است. هدف از بازنمایی شناخت انسان در فرآیند تصمیم‌گیری، ادغام روانشناسی شناختی و نظریه تصمیم‌گیری چند ویژگی را تشویق می‌کند. با توجه به ظهور تحقیقات در مورد روش‌های MADM با الهام از شناخت، ما مروری جامع بر مقالات منتشر شده در این زمینه و کاربردهای آنها داریم. این مقاله در پنج بخش گروه بندی شده است: ابتدا برخی از تحلیل های آماری مقالات دانشگاهی را از دو زاویه: روند توسعه و توزیع نشریات مرتبط انجام می دهیم. برای نشان دادن فرآیند اساسی روش‌های MADM با الهام از شناخت، برخی از ایده‌های اساسی و ساختار سیستماتیک این نوع روش را ارائه می‌کنیم. سپس، مروری بر روش‌های MADM الهام‌گرفته از دیدگاه‌های مختلف انجام می‌دهیم. کاربردهای این روش ها بیشتر مورد بررسی قرار می گیرد. در نهایت، برخی از چالش ها و روندهای آینده به طور خلاصه بیان می شوند. این مقاله مزایای رویکرد هم افزایی را که بر اساس تکنیک‌های شناختی و روش‌های MADM توسعه یافته است، برجسته می‌کند و مرزها را در این زمینه شناسایی می‌کند.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
In the last decades, the art and science of multi-attribute decision-making (MADM) have witnessed significant developments and have found applications in many active areas. A lot of research has demonstrated the ability of cognitive techniques in dealing with complex and uncertain decision information. The purpose of representing human cognition in the decision-making process encourages the integration of cognitive psychology and multi-attribute decision-making theory. Due to the emergence of research on cognitively inspired MADM methods, we make a comprehensive overview of published papers in this field and their applications. This paper has been grouped into five parts: we first conduct some statistical analyses of academic papers from two angles: the development trends and the distribution of related publications. To illustrate the basic process of cognitively inspired MADM methods, we present some underlying ideas and the systematic structure of this kind of method. Then, we make a review of cognitively inspired MADM methods from different perspectives. Applications of these methods are further reviewed. Finally, some challenges and future trends are summarized. This paper highlights the benefits of the synergistic approach that is developed based on cognitive techniques and MADM methods and identifies the frontiers in this field.
Introduction
Real-world decision-making problems are often too complex to be considered through a single criterion. In order to solve problems with multiple attributes, multi-attribute decision-making (MADM) methods are developed, which signifcantly enhance the ability of decision-making methods. As a cognitive process, MADM refers to selecting the optimal solution by evaluating alternatives in the presence of multiple attributes. The number of alternatives for MADM problems is predetermined and limited. Basically, MADM methods can be roughly grouped into two categories [1]: (1) Outranking techniques. The outranking methods are based on pairwise comparison of alternatives, like ELECTRE (Elimination et Choix Traduisant la Realité in French, Elimination and Choice Expressing the Reality) [2] and PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) [3]. (2) Multi-attribute utility and value theories. This kind of method usually assigns a utility to each alternative and includes UTA (utility additives) [5], AHP (analytic hierarchy process) [6], ANP (analytic network process) [7], MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) [8], etc. There is a growing body of literature that recognizes the importance of MADM methods in many fields of human life, such as healthcare [9, 10], transportation [11–14], manufacturing [15–18], and business [20, 21].
Conclusions
The emergence of cognitively inspired MADM methods enriches the theoretical framework in MADM and greatly promotes the development of intelligent decision-making. It presents excellent modeling ability in processing complex information, which makes it of signifcant importance to MADM both from theoretical and practical points of view.
In this paper, we have gone through the recent contributions about cognitively inspired MADM methods and attempted to provide a comprehensive review in this feld. Firstly, some statistical analyses of published papers regarding cognitively inspired MADM have been unfolded from two aspects: the development trend and the distribution of related publications. We have seen a steady increase in the number of academic papers in this feld. As to this fact, more investors have their sights on the cognitively inspired MADM methods in recent years. After that, the system architecture of cognitively inspired MADM has been illustrated. Then, a comprehensive overview of diferent types of cognitively inspired MADM approaches has been provided, which concludes the contributions of these methods to modern decisionmaking. After analyzing the theoretical knowledge, practical applications have also been summarized, which concludes that cognitively inspired MADM methods make great efects on practical applications. We have ended by discussing some challenges and future trends in this feld.