Paper Info
Title
Intuitionistic Fuzzy Multi-Stage Multi-Objective Fixed-Charge Solid Transportation Problem In A Green Supply Chain
Abstract
This research mainly focuses on presenting an innovative study of a multi-stage multi-objective fixed-charge solid transportation problem (MMFSTP) with a green supply chain network system under an intuitionistic fuzzy environment. The most controversial issue in recent years is that greenhouse gas emissions such as carbon dioxide, methane, etc. induce air pollution and global warming, thus motivating us to formulate the proposed research. In real-world situations the parameters of MMFSTP via a green supply chain network system usually have unknown quantities, and thus we assume trapezoidal intuitionistic fuzzy numbers to accommodate them and then employ the expected value operator to convert intuitionistic fuzzy MMFSTP into deterministic MMFSTP. Next, the methodologies are constructed to solve the deterministic MMFSTP by weighted Tchebycheff metrics programming and min-max goal programming, which provide Pareto-optimal solutions. A comparison is then drawn between the Pareto-optimal solutions that are extracted from the programming, and thereafter a procedure is performed to analyze the sensitivity analysis of the target values in the min-max goal programming. Finally, we incorporate an application example connected with a real-life industrial problem to display the feasibility and potentiality of the proposed model. Conclusions about the findings and future study directions are also offered.
Year
DOI
Venue
2021
10.1007/s13042-020-01197-1
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
Keywords
DocType
Volume
Fixed-charge solid transportation problem, Supply chain network, Green supply chain, Multi-objective decision making, Intuitionistic fuzzy number, Pareto-optimal solution
Journal
12
Issue
ISSN
Citations 
3
1868-8071
2
PageRank 
References 
Authors
0.37
0
3
Name
Order
Citations
PageRank
Sudipta Midya1212.02
Sankar Kumar Roy210414.63
Vincent F. Yu342427.32