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Erschienen in: Neural Computing and Applications 7/2020

06.09.2018 | Original Article

Group decision-making and grey programming approaches to optimal product mix in manufacturing supply chains

verfasst von: R. Rajesh

Erschienen in: Neural Computing and Applications | Ausgabe 7/2020

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Abstract

Group decision-making is a significant task that involves trade-offs, risks and the interplay of various factors. Optimization models developed from group decision-making are often built on uncertainties and negotiated parameters and may not yield factual solutions. Manufacturing decision-making environment is subject to several uncertainties, and grey theory is an effective tool to improve the exactitude of decision-making in such situations. An application of grey programming in manufacturing decision-making environment has been implemented in this research. Grey programming models can potentially avoid the loss of data while formulating optimization models from group decisions. A grey programming model for optimizing profits has been constructed and solved for an indeterminate product mix problem of a case electronics manufacturing industry. For the case, the optimization models were constructed and solved for the ideal model, critical model and the most typical model. And the results favor the decision on the introduction of a new product variant in the smartphone segment. In the most favorable and unfavorable conditions, it is seen that the introduction of new variant in the smartphone section is meritorious when compared to the tablet or laptop segments. The solution to the case endorses flexibility of grey programming in uncertain decision-making environments. As the decisions from the group can be signified in intervals using grey numbers, managers are recommended to comprise the benefits of grey programming models into their optimization problems for increasing flexibility and reliability of solutions.

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Metadaten
Titel
Group decision-making and grey programming approaches to optimal product mix in manufacturing supply chains
verfasst von
R. Rajesh
Publikationsdatum
06.09.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 7/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3675-y

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