Automatically weighted high-resolution mapping of multi-criteria decision analysis for sustainable manufacturing systems

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dc.contributor.author Pagone, Emanuele
dc.contributor.author Salonitis, Konstantinos
dc.contributor.author Jolly, Mark
dc.date.accessioned 2020-02-10T12:00:17Z
dc.date.available 2020-02-10T12:00:17Z
dc.date.issued 2020-02-01
dc.identifier.citation Pagone E, Salonitis K, Jolly M. (2020) Automatically weighted high-resolution mapping of multi-criteria decision analysis for sustainable manufacturing systems. Journal of Cleaner Production, Available online 1 February 2020, Article number 120272 en_UK
dc.identifier.issn 0959-6526
dc.identifier.uri https://doi.org/10.1016/j.jclepro.2020.120272
dc.identifier.uri http://dspace.lib.cranfield.ac.uk/handle/1826/15116
dc.description.abstract A common feature of Multi-Criteria Decision Analysis (MCDA) to evaluate sustainable manufacturing is the participation (to various extents) of Decision Makers (DMs) or experts (e.g. to define the importance, or “weight”, of each criterion). This is an undesirable requirement that can be time consuming and complex, but it can also lead to disagreement between multiple DMs. Another drawback of typical MCDA methods is the limited scope of weight sensitivity analyses that are usually performed for one criterion at the time or on an arbitrary basis, struggling to show the “big picture” of the decision making space that can be complex in many real-world cases. This work removes all the mentioned shortcomings implementing automatic weighting through an ordinal combinatorial ranking of criteria objectively set by four pre-defined weight distributions. Such solution provides the DM not only with a fast, rational and systematic method, but also with a broader and more accurate insight into the decision making space considered. Additionally, the entropy of information in the criteria can be used to adjust the weights and emphasise the differences between potentially close alternative en_UK
dc.language.iso en en_UK
dc.publisher Elsevier en_UK
dc.rights Attribution 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ *
dc.subject Manufacturing system en_UK
dc.subject Decision making en_UK
dc.subject Sustainable development en_UK
dc.subject Casting en_UK
dc.subject Lifecycle en_UK
dc.title Automatically weighted high-resolution mapping of multi-criteria decision analysis for sustainable manufacturing systems en_UK
dc.type Article en_UK


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