Decision support system for multicriteria machine selection for flexible manufacturing systems
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An extended Fuzzy-AHP approach to rank the influences of socialization-externalization-combination-internalization modes on the development phase
2017, Applied Soft Computing JournalCitation Excerpt :Keeping this in mind, Thomas L. Saaty introduced a simple yet systematic Analytic Hierarchy Process (AHP) method. It is a useful, and practical method and has been used as a tool for weight estimation in deriving valid argumentative decisions for machine selection in flexible manufacturing system [25], resource allocation by prioritizing information [26], supplier selection [10,27], reverse logistics and product recovery [28], selection of innovative educational project [29] etc. AHP is fundamentally built on two basic concepts: 1) formulation of the problem in a hierarchical structure, and 2) use of pairwise comparison.
Selecting the most appropriate tractor using Analytic Hierarchy Process – An Iranian case study
2016, Information Processing in AgricultureCitation Excerpt :In their study, there were four main criteria: machine procedures, lead time, labor cost, and operation shift; and three alternatives: conventional machines, NC machines, and flexible manufacturing cells. Tabucanon et al. [37] developed a decision support framework designed to aid decision-makers in selecting the most appropriate machines for flexible manufacturing systems (FMS). The framework consists of two main stages.
[INVITED] Lasers in additive manufacturing
2016, Optics and Laser TechnologyCitation Excerpt :If traditional and AM systems could operate together in this way it would also have a favourable environmental impact. A decision-making framework or model to assist in selecting the manufacturing method and machine to apply to a component, based on size, material, geometric features and production factors, would be highly beneficial for such integrated systems [8,108–110]. The benefit of AM operating with traditional manufacturing as well as an independent industry become clear from the current scale of the two industries.
A methodological framework for the inclusion of modern additive manufacturing into the production portfolio of a focused factory
2015, Journal of Manufacturing SystemsCitation Excerpt :However, given the fact that resources for enterprises are finite investments need to support the overall business strategy [54,55]. To that end, based on our experience with a number of SMEs, we have developed a methodological tool that employs at its core two effective and widely used tools in manufacturing decision-making processes, which complement each other well (e.g. [56–63]). On the one hand, Multi-Criteria Decision Aid (MCDA) considers quantitative and qualitative criteria with the possibility to appoint different weighting factors according to importance of the selected criteria, while on the other hand, data envelopment analysis (DEA) offers crucial insights about the different alternatives’ efficiencies, thus creating benchmarks.
Multicriteria model for maintenance benchmarking
2014, Journal of Manufacturing SystemsCitation Excerpt :The use of AHP in manufacturing systems is widespread. The technique is used to justify investment in flexible manufacturing systems (FMS) [9,58], for choosing machines for FMS [42,57], the best FMS design [10,50], the most appropriate computer-integrated manufacturing (CIM) alternatives [64], for facility layout design [26], for real-time scheduling and part routing in advanced manufacturing systems [8], etc. Much of the literature is dedicated to justifying the choice or introduction of advanced manufacturing technology (AMT).