Decision-making and performance measurement models with applications to robot selection
Computers and Industrial Engineering
Design and Analysis of Experiments
Design and Analysis of Experiments
Multiple-attribute decision making methods for plant layout design problem
Robotics and Computer-Integrated Manufacturing
Computer-aided machine-tool selection based on a Fuzzy-AHP approach
Expert Systems with Applications: An International Journal
The use of grey relational analysis in solving multiple attribute decision-making problems
Computers and Industrial Engineering
A systematic modelling and simulation approach for JIT performance optimisation
Robotics and Computer-Integrated Manufacturing
Development of a decision support system for machining center selection
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Automation, Production Systems, and Computer-Integrated Manufacturing
Automation, Production Systems, and Computer-Integrated Manufacturing
Simulation based experimental design to identify factors affecting performance of AVS/RS
Computers and Industrial Engineering
Selection of industrial robots using compromise ranking and outranking methods
Robotics and Computer-Integrated Manufacturing
International Journal of Computer Integrated Manufacturing
Computers and Industrial Engineering
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The selection of Computer-Integrated Manufacturing (CIM) technologies becomes more complex as the decision makers in the manufacturing organization have to assess a wide range of alternatives based on a set of attributes. Although, a lot of Multi-Attribute Decision-Making (MADM) methods are available to deal with selection applications, this paper aims to explore the applicability of an integrated TOPSIS and DoE method to solve different CIM selection problems in real-time industrial applications. Four CIM selection problems, which include selection of (a) an industrial robot, (b) a rapid prototyping process, (c) a CNC machine tool and (d) plant layout design, are considered in this paper. TOPSIS method and Design of Experiment (DoE) are used together to identify critical selection attributes and their interactions of all these cases by fitting a polynomial to the experimental data in a multiple linear regression analysis. This mathematical model development process involves TOPSIS experiments with the model. The regression meta-model greatly reduced the cost, time and amount of the calculation step in application the TOPSIS model. Application results were validated and shown that they provide good approximations to four decision making problem's results in the literature.