Theory Of Multiple Criteria Decision Research Articles

CLUS-MCDA: A novel framework based on cluster analysis and multiple criteria decision theory in a supplier selection problem

Abstract In past recent years, by increasing in the considerations on the significance of data science many studies have been developed concerning the big data structured problems. Along with the information science, in the field of decision science, multi-attribute decision-making (MADM) approaches have been considerably applied in research studies. One of the most important procedures in supply chain management is selecting the optimal supplier to maintain the long-term productivity of the supply chain. There has been a vast amount of research which utilized MADM approaches to tackle the supplier selection problems, but only a few of these research considered big data structured problems. The current study presents a comprehensive novel approach for improving Multiple Criteria Decision Analysis (MCDA) based on cluster analysis considering crisp big data structure input which is called CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) algorithm. The proposed method is based on consolidating a data mining technique i.e. k -means clustering method and a MADM approach which is MULTIMOORA method. CLUS-MCDA method is a fast and practical approach which has been developed in this research which is implied in a supplier selection problem considering crisp big data structured input. A real-world case study in MAMUT multi-national corporation has been presented to show the validity and practicality of the CLUS-MCDA approach which calculated considering the business areas and criteria based on expert comments of mentioned organizations and previous literature on supplier selection problem.

The Inverse Bin-Packing Problem Subject to Qualitative Criteria

A new formulation of the reverse bin-packing problem is suggested. One distinct feature of the new formulation is that it takes into account of a decision-maker’s preferences for a set of objects that are evaluated by multiple quality criteria. The aspects of this problem are discussed that relate to the theory of multiple criteria decision making. The known methods for solving the classic and the reverse bin-packing problems (the multiple knapsack problem) are reviewed.

Association Rules Evaluation by a Hybrid Multiple Criteria Decision Method

Various rules can be generated from databases by using association rule algorithms, but only a small number of these rules may be selected for implementation due to the limitations of resources. Accordingly, evaluating the quality of these rules becomes a hot topic in the data mining field. Based on multiple criteria decision theory, a framework for evaluating the mined association rules using TOPSIS method with combination weights is proposed, which takes into account both objective interestingness measures and the users’ domain information. An example of market basket analysis is applied to illustrate the applicability of this method.

Comparing with your main competitor: the single most important task of knowledge management performance measurement

The single most important task of knowledge management (KM) performance measurement is comparing your company with its main rivals. Most of the metrics and methods of knowledge measurement that have been developed are concentrated on measuring the knowledge within the organization, which may be nice to know, but is not critical. In this paper, we propose a methodology for comparing a firm's knowledge management performance with its major rivals using the Analytical Network Process (ANP) to obtain a clear direction of the effort required to gain or maintain a competitive advantage. The ANP approach employed in the present study is a theory of multiple criteria decision making (MCDM), and is good at dealing with tangible and intangible information. Our methodology is designed to make a detailed comparison of a firm's KM performance with that of its main rivals, in order to be able to provide effective information for improving its KM and to increase its decision-making quality. This paper makes three important contributions: (1) it develops a comprehensive model, which incorporates a variety of issues for conducting KM performance measurements in comparison with major rivals; (2) case experience is provided to help us understand the advantages and disadvantages of the methodology for KM performance measurement from a practical point of view, and (3) the results obtained from exploring the case firm present changes that the case firm can make, implying that the case firm must reinforce its knowledge creation and internalization so as to improve its position in comparison with its most competitive rivals. The method proposed by this paper is generic in nature and is applicable to benefit any firm.

On the application of multiple criteria decision making to a problem in defence systems acquisition

Multiple criteria decision theory (MCDT) approaches to choice making are receiving increased attention due to the increasing importance society places on incorporating the non-commensurate and conflicting objectives of a situation into the choice making process. Process algorithms for multiple objective optimization theory (MOOT) and multiple attribute utility, theory (MAI5T) motivated a combined approach which utilizes, in an efficient manner, the complementary aspects of both processes. An appropriate application for the multiple criteria approach is a specific military equipment acquisition involving aircraft retrofit. The retrofit of a particular aircraft with equipment designed for a mission which the aircraft was not originally designed to fly typically requires a large systems effort. Specifically, the retrofit of an aircraft with sophisticated electronic warfare (EW) equipment has historically involved inefficiencies and inadequacies including schedule and budgetary overruns and a lack of initiall...

Multiple Criteria Theory and Airport Site Evaluation

This paper presents the application of multiple criteria decision theory to the selection of a new airport site. Utility values for various criteria are developed using ranking and rating techniques. Data on the ten criteria for the nine alternative sites were presented to a Site Evaluation Committee to obtain effectiveness measures. Nonparametric statistical tests are applied to determine the degree of agreement among the citizen committee members (both airport professionals and nonairport professionals) and to validate the process upon which the ultimate decision for the site location is made.