An integrated fuzzy decision support system for multicriterion decision-making problems

In order to compete effectively in today's rapidly changing world economy, decision making should be made fast and accurate, especially when many tangible and intangible factors are considered together. Making decisions to implement advanced manufacturing technologies (AMTs) is a good example. However, not all AMT implementations are successful because most of the conventional investment justification criteria are built on tangible factors, such as cost reduction, only. Intangiabl long-term benefits such as improvement in quality, increase in flexibility, and fast delivery are always disregarded. The objective of this paper is to present an integrated approach, which incorporates different approaches (e.g. strategic, economic and analytic evaluations) for assessing the benefits among several alternative proposals, such as implementing AMT, by a fuzzy multicriterion decision-making method.

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A double-DEA framework to support decision-making in the choice of advanced manufacturing technologies

Management Decision ◽

10.1108/md-09-2016-0644 ◽

2018 ◽

Vol 56 (2) ◽

pp. 488-507 ◽

Cited By ~ 5

Author(s):

Corrado lo Storto

Keyword(s):

Decision Making ◽

Manufacturing Systems ◽

Composite Indicator ◽

High Order ◽

The Body ◽

Advanced Manufacturing ◽

Content Type ◽

Advanced Manufacturing Technologies ◽

Dea Models ◽

Manufacturing Technologies

Purpose The purpose of this paper is to propose a methodological framework that combines several data envelopment analysis (DEA) models to deal with the problem of evaluating and ranking advanced manufacturing technologies (AMTs) without introducing any subjectivity in the analysis. Design/methodology/approach The methodology follows a two-phase procedure. First, the relative efficiency of every technology is calculated by implementing different DEA cross-efficiency models generating the same number of high-order indicators as efficiency vectors. Second, high-order indicators are used as outputs in a SBM-DEA super-efficiency model to obtain a comprehensive DEA-like composite indicator. Findings The framework is implemented to evaluate a sample of flexible manufacturing systems. Comparing it to other methods, results show that the methodology provides reliable information for AMTs selection and effective support to management decision-making. Originality/value This paper contributes to the body of knowledge about the utilization of DEA to select AMTs. The framework has several advantages: a discriminating power higher than the basic DEA models; no subjective judgment relative to weights necessary to aggregate single indicators and choice of aggregation function; no need to perform any transformation normalizing original data; independence from the unit of measurement of the DEA-like composite indicator; and great flexibility and adaptability allowing the introduction of further variables in the analysis.

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Facilitating Multiple-Objective Decision-Making for Advanced Manufacturing: A Knowledge Representation and Computational Active Learning-Based Simulation Framework

Volume 4: 20th Design for Manufacturing and the Life Cycle Conference; 9th International Conference on Micro- and Nanosystems ◽

10.1115/detc2015-47579 ◽

2015 ◽

Author(s):

Shuhui Qu ◽

Weiwen Jian ◽

Tianshu Chu ◽

Jie Wang

Keyword(s):

Decision Making ◽

Active Learning ◽

Knowledge Representation ◽

Rapid Development ◽

Multiple Objective ◽

Advanced Manufacturing ◽

Simulation Framework ◽

Advanced Manufacturing Technologies ◽

Multiple Objective Decision Making ◽

Manufacturing Technologies

Due to the rapid development of information technology and the impetus for more efficient and adaptive manufacturing processes, the concept of advanced manufacturing has become an increasingly prominent research topic across academia and industry in recent years. One critical aspect of advanced manufacturing is how to optimally cope with the complexities of multiple-objective decision-making to implement advanced manufacturing technologies with currently available enterprise resources and the realistic manufacturing conditions of a company. Generally, to successfully fulfill an advanced manufacturing plan, decision-makers must align short-term objectives with long-term strategies. In addition, the decision-making process usually has to prioritize multiple-objective goals under a considerable number of uncertainties. This requirement presents new challenges for both planning and implementing advanced manufacturing technologies, and thus calls for new approaches for to better support such tasks. This paper proposes a knowledge representation and computational active learning-based framework for dealing with complex, multiple-objective decision-making problems for advanced manufacturing under realistic conditions. Through this study, we hope to shed light on using a simulation framework for multiple-objective decision support, thereby providing an alternative for manufacturing enterprises, which could lead to an acceptable optimal decision with reasonable cost and accuracy. First, we describe the scope of an advanced manufacturing system for industrial manufacturing. Next, we introduce systematic analysis of the complexities of the decision-making to implement advanced manufacturing. Finally, we propose a simulation model for the decision-making and formulate a computational active learning-based framework to efficiently compute goal priorities for multiple-objective decision-making. We validate the framework by presenting a simulation of decision-making.

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Research on the Investment Decision-Making on the Application of Advanced Manufacturing Technologies in Enterprises

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.323.60 ◽

2011 ◽

Vol 323 ◽

pp. 60-64

Author(s):

Gang Li

Keyword(s):

Decision Making ◽

Fuzzy Theory ◽

Investment Decision ◽

Business Environment ◽

Economic Benefits ◽

Advanced Manufacturing ◽

Investment Decision Making ◽

Advanced Manufacturing Technologies ◽

Manufacturing Technologies ◽

High Level

Adopting advanced manufacturing technologies (AMTs) is believed to be the key to survival for many companies in the current highly volatile business environment. For adoption of AMT involves a very high level of investment, it is very important to make scientific decision to select AMT. Firstly, the literature of decision-making methods of selecting AMT is reviewed. Secondly, the analysis framework of investment decision-making on AMT is proposed, which includes strategic benefits analysis, economic benefits analysis and risk analysis. Finally, a methodology of evaluating and selecting AMT is put forward by using the concepts of fuzzy theory and hierarchical analysis.

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