Paper Info
Title
Decision-Making Model Of Machine Tool Remanufacturing Alternatives Based On Dual Interval Rough Number Clouds
Abstract
Remanufacturing of machine tools has become one of the crucial topical subjects as it can effectively reduce the manufacturing cost compared with traditional new product development. The assessment and selection of different remanufacturing alternatives are vital for successfully implementing the remanufacturing of machine tools. Although numerous fuzzy theory-based methods have been developed to evaluate and select the reasonable remanufacturing alternatives in the machine tool remanufacturing process, they still have some drawbacks, such as requiring extra assumptions, ignoring the impact of internal relationships among distinct criteria, and lacking the mechanism of manipulating diverse uncertain information. To cover such limitations, this study develops an applicable decision-making method to assist managers in extracting the essential remanufacturing alternatives for product improvement. First, a new concept named dual interval rough number clouds (DIRNCs) is developed by combining the merit of interval rough numbers (IRNs) and interval cloud model in handling uncertain information. Then, an applicable decision-making support model of remanufacturing alternatives is constructed based on DIRNCs, two non-linear weighting methods, and a technique for order performance by similarity to ideal solution (TOPSIS). Finally, an assessment of remanufacturing alternatives of machine tools is presented to illustrate the model, whose effectivity is demonstrated by comparing extant models. Results display that the developed DIRNC-based model is more advantageous than triangular fuzzy-based, cloud model-based, fuzzy rough-based, and IRN-based methods.
Year
DOI
Venue
2021
10.1016/j.engappai.2021.104392
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Keywords
DocType
Volume
Machine tools, Remanufacturing alternative decision, Interval rough numbers, Interval cloud model, Weighting method
Journal
104
ISSN
Citations 
PageRank 
0952-1976
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Guangquan Huang134.09
Liming Xiao233.07
Genbao Zhang3810.98