MADM AND FUTURES STUDIES; A NECESSITY

Multiple Attribute Decision Making (MADM) has been developing in different methods, perspectives and frameworks since introducing step. Futures Studies as a specialized framework and methodology has introduced newer and has been always in developing phase too. MADM as a part of Multiple Criteria Decision Making is known as multi-disciplinary approach, framework and methodology. Nowadays, Futures Studies is also known as multi-disciplinary approach too. Basically, MADM is structured for a stable environment while most decisions need to be made, dynamically. Time is so much important especially in the new century in comparison with the past. Decisions making about future are usually so complicated and MADM can be helpful in that process. Importance of making future based decisions is undeniable in trying to answer to decision needs. This research will present a comprehensive review on the literature of MADM and new orientations in considering future in MADM models and necessity of them will be checked also carefully. Eventually, importance of seeing both MADM and Futures Studies together as a unit model will be discussed in this study.

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Robust Inventory System Optimization Based on Simulation and Multiple Criteria Decision Making

Modelling and Simulation in Engineering ◽

10.1155/2014/305120 ◽

2014 ◽

Vol 2014 ◽

pp. 1-17 ◽

Cited By ~ 3

Author(s):

Ahmad Mortazavi ◽

Alireza Arshadi khamseh

Keyword(s):

Decision Making ◽

Inventory Management ◽

Multiple Criteria Decision Making ◽

Principal Component ◽

System Optimization ◽

Multiple Attribute Decision Making ◽

Multiple Criteria ◽

Inventory System ◽

Multiple Attribute ◽

Cyclic Changes

Inventory management in retailers is difficult and complex decision making process which is related to the conflict criteria, also existence of cyclic changes and trend in demand is inevitable in many industries. In this paper, simulation modeling is considered as efficient tool for modeling of retailer multiproduct inventory system. For simulation model optimization, a novel multicriteria and robust surrogate model is designed based on multiple attribute decision making (MADM) method, design of experiments (DOE), and principal component analysis (PCA). This approach as a main contribution of this paper, provides a framework for robust multiple criteria decision making under uncertainty.

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The Multi-Aspect Criterion in the PMADM Outline and Its Possible Application to Sustainability Assessment

Sustainability ◽

10.3390/su10124451 ◽

2018 ◽

Vol 10 (12) ◽

pp. 4451 ◽

Cited By ~ 12

Author(s):

Sarfaraz Hashemkhani Zolfani ◽

Edmundas Kazimieras Zavadskas ◽

Payam Khazaelpour ◽

Fausto Cavallaro

Keyword(s):

Decision Making ◽

Initial Phase ◽

Sustainability Assessment ◽

Multiple Criteria Decision Making ◽

Multiple Attribute Decision Making ◽

Decision Making Process ◽

New Approach ◽

The Past ◽

Multiple Attribute ◽

The Future

Over the past few centuries, the process of decision-making has become more complicated in different respects. Since the initial phase of Multiple Criteria Decision Making (MCDM) around fifty years ago, Multiple Attribute Decision Making (MADM) has continued developing over the years as a sub-concept of MCDM. Noticeably, the importance of the decision-making process is increasingly expanding to such an extent that it necessarily blends into the undeniable processes of MADM actual models. Novel methods with different perspectives have been introduced considering the dynamic MADM concepts of time and future in classical frameworks; however, they do not overcome challenges in practice. Recently, Prospective MADM (PMADM) as a specific approach has presented future-oriented models using already known approaches of MCDM, and it has innovative items which show barriers of classic model of MADM. However, PMADM practically needs more conceptual bases to illustrate and plan the future of real decision-making problems. The Multi-Aspect Criterion is a new concept in mapping the future of the PMADM outline. In this regard, two examples of sustainability will be analyzed, and different requirements and aspects associated with PMADM will be discussed in this study. This new approach can support the PMADM outline in more detail and deal with a decision-making structure that can be considered as novel to industry experts.

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A HYBRID DYNAMIC MADM MODEL FOR PROBLEM-IMPROVEMENT IN ECONOMICS AND BUSINESS

Technological and Economic Development of Economy ◽

10.3846/20294913.2013.837114 ◽

2013 ◽

Vol 19 (4) ◽

pp. 638-660 ◽

Cited By ~ 63

Author(s):

Kua-Hsin Peng ◽

Gwo-Hshiung Tzeng

Keyword(s):

Decision Making ◽

Real World ◽

Additive Model ◽

Multiple Criteria Decision Making ◽

Multiple Attribute Decision Making ◽

Multiple Criteria ◽

The Real ◽

Multiple Attribute ◽

Aspiration Levels ◽

Real World Problems

A typical multiple attribute decision making (MADM) model is a scientific analytical model for evaluating and improving a set of alternatives based on multiple criteria. However, this study identified some important new concepts and limitations/defects of traditional MADM for solving the real-world problems. First, the traditional MADM model assumes that criteria considered are independent and hierarchical in structure; however, the real-world problems often involve interdependent criteria, and thus interdependent models are required. Second, relatively good solutions from existing alternatives are replaced by the aspiration levels. Third, the trend has shifted from how to “rank” or “select” the most preferable alternatives, to how to “improve” their performances. Fourth, information fusion/aggregation, such as fuzzy integrals, basically, a non-additive/super-additive model, has been developed for performance aggregation. Therefore, to overcome the defects of the conventional MADM method and solve complex and dynamic real world problems, a Hybrid Dynamic Multiple Criteria Decision Making (HDMADM) method is needed. Finally, this study presented real cases to demonstrate the effectiveness of the HDMADM method for overcoming the defects of the conventional MADM method.

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Multiple Criteria Decision Making Research of Flight Control System Design

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.648.334 ◽

2013 ◽

Vol 648 ◽

pp. 334-343

Author(s):

Rui Nie ◽

Bai Nan Zhang ◽

Bao Ning Liu ◽

Wei Guo Zhang ◽

Jing Yuan

Keyword(s):

Neural Network ◽

Decision Making ◽

Flight Control ◽

Multiple Criteria Decision Making ◽

Multiple Attribute Decision Making ◽

Multiple Objective ◽

Flight Control System ◽

Attribute Weights ◽

Multiple Attribute ◽

Multiple Objective Decision Making

Flight control system; multiple criteria decision making; multiple objective decision making; multiple attribute decision making; neural network; mean impact value index. Abstract. It is complex and difficult to tune the parameters of the flight controller. To solve such problem, a multiple objective decision making (MODM) method by using the reference model which is built based on the criteria, is proposed. In order to resolve defects of the multiple attribute decision making (MADM) that the arbitrary of the subjective attribute weights and ignoring the objective message of the objective attribute weights, a subjective attribute weights based on the BP neural network by using the MIV (mean impact value) index is proposed. Finally, a combining method based on the TOPSIS is used to give the final attribute weights. The simulation results show that the method could obtain a set of trade-off solutions which satisfy the requirements of the MODM and could tune the controller effectively.

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Synergies of Text Mining and Multiple Attribute Decision Making: A Criteria Selection and Weighting System in a Prospective MADM Outline

Symmetry ◽

10.3390/sym12050868 ◽

2020 ◽

Vol 12 (5) ◽

pp. 868

Author(s):

Sarfaraz Hashemkhani Zolfani ◽

Arman Derakhti

Keyword(s):

Decision Making ◽

Text Mining ◽

Latent Semantic Analysis ◽

Semantic Analysis ◽

Multiple Attribute Decision Making ◽

General Concept ◽

Futures Studies ◽

Weighting System ◽

Multiple Attribute ◽

Multi Attribute Decision Making

In this study, a new way of criteria selection and a weighting system will be presented in a multi-disciplinary framework. Weighting criteria in Multi-Attribute Decision Making (MADM) has been developing as the most attractive section in the field. Although many ideas have been developed during the last decades, there is no such great diversity that can be mentioned in the literature. This study is looking from outside the box and is presenting something totally new by using big data and text mining in a Prospective MADM outline. PMADM is a hybrid interconnected concept between the Futures Studies and MADM fields. Text mining, which is known as a useful tool in Futures Studies, is applied to create a widespread pilot system for weighting and criteria selection in the PMADM outline. Latent Semantic Analysis (LSA), as an influential method inside the general concept of text mining, is applied to show how a data warehouse’s output, which in this case is Scopus, can reach the final criteria selection and weighting of the criteria.

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Multiple-Attribute Decision Making ELECTRE II Method under Bipolar Fuzzy Model

Algorithms ◽

10.3390/a12110226 ◽

2019 ◽

Vol 12 (11) ◽

pp. 226 ◽

Cited By ~ 7

Author(s):

Shumaiza ◽

Muhammad Akram ◽

Ahmad N. Al-Kenani

Keyword(s):

Decision Making ◽

Optimization Technique ◽

Multiple Criteria Decision Making ◽

Fuzzy Model ◽

Multiple Attribute Decision Making ◽

Business Location ◽

Outranking Relation ◽

Fuzzy Environment ◽

Multiple Attribute ◽

Electre I

The core aim of this paper is to provide a new multiple-criteria decision making (MCDM) model, namely bipolar fuzzy ELimination and Choice Translating REality (ELECTRE) II method, by combining the bipolar fuzzy set with ELECTRE II technique. It can be used to solve the problems having bipolar uncertainty. The proposed method is established by defining the concept of bipolar fuzzy strong, median and weak concordance as well as discordance sets and indifferent set to define two types of outranking relations, namely strong outranking relation and weak outranking relation. The normalized weights of criteria, which may be partly or completely unknown for decision makers, are calculated by using an optimization technique, which is based on maximizing deviation method. A systematic iterative procedure is applied to strongly outrank as well as weakly outrank graphs to determine the ranking of favorable actions or alternatives or to choose the best possible solution. The implementation of the proposed method is presented by numerical examples such as selection of business location and to chose the best supplier. A comparative analysis of proposed ELECTRE II method is also presented with already existing multiple-attribute decision making methods, including Technique for the Order of Preference by Similarity to an Ideal Solution (TOPSIS) and ELECTRE I under bipolar fuzzy environment by solving the problem of business location.

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AN INTRODUCTION TO PROSPECTIVE MULTIPLE ATTRIBUTE DECISION MAKING (PMADM)

Technological and Economic Development of Economy ◽

10.3846/20294913.2016.1150363 ◽

2016 ◽

Vol 22 (2) ◽

pp. 309-326 ◽

Cited By ~ 22

Author(s):

Sarfaraz HASHEMKHANI ZOLFANI ◽

Reza MAKNOON ◽

Edmundas Kazimieras ZAVADSKAS

Keyword(s):

Decision Making ◽

Multiple Criteria Decision Making ◽

Multiple Attribute Decision Making ◽

Future Research ◽

New Approach ◽

Multiple Attribute ◽

The Future ◽

Science Community ◽

Future Topics

In recent years futures science has received a great deal of attention and has gained worldwide credibility in the science community as the science of tomorrows. The countless applications of futures studies in various fields have been a major breakthrough for mankind. Undoubtedly, decision making is one of the most significant aspects of shaping the future and an integral part of any credible future research. Multiple Criteria Decision Making (MCDM) in general and Multiple Attribute Decision Making in particular (MADM), are among the most remarkable subparts of the decision making process. The most recent model developed using the MADM method is the Dynamic MADM. The model does not specifically concentrate on the future actions and approaches and remains to be fully explored. This research presents a new concept and a new approach in the MADM field which is called the Prospective Multiple Attribute Decision Making (PMADM). The PMADM model can very well cover the DMADM concept but instead chooses to focus on future topics. The study also introduces two new approaches. The first research aims to elaborate the basis of this model and then evolves to deal with the future limiters as they potentially pop up and change the course of future actions. The new model based on future limiters is separated and categorized into two sections; one of which is looked upon without the probabilities rate and the other one with the probabilities rate. This approach is deemed priceless due to its major applicability in the ranking of the MADM methods such as: TOPSIS, VIKOR, COPRAS, ARAS, WASPAS and etc. Finally, a case study with the various applications of PMADM model in WASPAS methodology is put forth and illustrated.

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Elicitation of Preference Structure in Engineering Design

Acta Polytechnica ◽

10.14311/694 ◽

2005 ◽

Vol 45 (3) ◽

Author(s):

J. K. Tan

Keyword(s):

Decision Making ◽

Engineering Design ◽

Multiple Criteria Decision Making ◽

Pairwise Comparison ◽

Multiple Attribute Decision Making ◽

Design Solution ◽

Preference Structure ◽

Efficient Design ◽

Multiple Attribute ◽

Artificial Neural Network Ann

Engineering design processes, which inherently involve multiple, often conflicting criteria, can be broadly classified into synthesis and analysis processes. Multiple Criteria Decision Making addresses synthesis and analysis processes through multiple objective optimisation to generate sets of efficient design solutions (i.e. on Pareto surfaces) and multiple attribute decision making to analyse and select the most preferred design solution(s). MCDM, therefore, has been widely used in all fields of engineering design; for example it has been applied to such diverse areas as naval battle ships criteria analysis/selection and product appearance design. Given a list of design alternatives with multiple conflicting criteria, preferences often determine the final selection of a particular set of design alternative(s). Preferences may also be used to drive the design/design optimisation processes. Various methods have been proposed to model preference structure, for example simple weights, multiple attribute utility theory, pairwise comparison, etc. Preference structure is often non-linear, discontinuous and complex. An Artificial Neural Network (ANN) learning-based preference elicitation method is presented in this paper. ANNs efficiently model the non-linearity, complexity and discontinuity nature of any given preference structure. A case study is presented to illustrate the learning-based approach to preference structure elicitation.

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