Avakh Darestani, Soroush

Poormohammad Sarabi, Elnaz

English

Nowadays, maximizing the production by reducing the associated risks in the supply chain and enhancing the final product quality by selecting the best providers are among the most fundamental challenges encountered within the equipment manufacturing industry worldwide. The lack of timely delivery of machines to customers and unregulated purchase of goods associated with the delivery of the machines are among the many problems faced by the manufactures. The proposed research aims to evaluate a Decision Support System (DSS) for selecting the most appropriate logistic service provider out of three service providers companies. Three companies X1, X2 and X3 were weighted and ranked using two decision-making methods, namely Fuzzy best-worst Method (FBWM) and Multiple Objective Optimizations on the basis of Ratio Analysis plus full Multiplicative Form (MULTIMOORA), considering eight criteria and their corresponding sub-criteria, respectively. Once finished with constructing the decision matrix, the analytical data being obtained from the two methods were processed using Microsoft Excel and the Lingo software. According to the results, it is concluded that Company X3 is the best logistics service provider

Coventry University Pure Portal

Developing a decision support system for logistics service provider selection employing fuzzy MULTIMOORA &amp; BWM in mining equipment manufacturing

https://pure.coventry.ac.uk/ws/files/39811452/Final_ver_6_original.pdf

Developing a decision support system for logistics service provider selection employing fuzzy MULTIMOORA & BWM in mining equipment manufacturing

Abstract

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Coventry University Pure Portal

Developing a decision support system for logistics service provider selection employing fuzzy MULTIMOORA &amp; BWM in mining equipment manufacturing

Abstract

Similar works

Full text

Available Versions

Coventry University Pure Portal

Developing a decision support system for logistics service provider selection employing fuzzy MULTIMOORA & BWM in mining equipment manufacturing