Fuzzy sets and systems
Gholamreza Jamali; Ramin Pabarja; Khodakaram Salimifard; Ahmad Ghorbanpur
Abstract
The Lean, Agile, Resilience, and Green (LARG) supply chains are more competitive than conventional ones. Evaluating its performance under current conditions and developing suitable strategies is crucial to enhance LARG. This study aims to create an assessment model for LARG in Iran's hospital medical ...
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The Lean, Agile, Resilience, and Green (LARG) supply chains are more competitive than conventional ones. Evaluating its performance under current conditions and developing suitable strategies is crucial to enhance LARG. This study aims to create an assessment model for LARG in Iran's hospital medical equipment supply chain, especially in Hamadan. The Fuzzy Inference System (FIS) evaluates LARG across four dimensions: lean, agile, resilient, and green. Key indicators obtained from a comprehensive review of the literature and other published reports in the field of LARG were also confirmed by a focused group of experts in the medical equipment supply chain field. The findings indicate that the value LARG of the medical equipment supply chain is 0.787. Key indicators for the evaluation of LARG in the hospital medical equipment supply chain include reducing overall supply chain costs, optimizing inventory management, shortening supply chain development cycle time, increasing the introduction of new products, promoting information sharing among supply chain members, establishing flexible supply bases and sourcing, reducing fossil fuel consumption, and implementing waste management practices such as reuse and recycling of recyclable materials. This research provides managers with valuable insights into the current state of LARG and serves as a reference for formulating LARG strategies and practices. The study's results enable supply chain actors, particularly in Iran's Hamadan Province, to comprehend the key indicators for improving LARG performance in the hospital medical equipment supply chain. The proposed model can be adapted to other industries and service sectors by adjusting the indicators and assessing data availability.
A. Rahimi Ghazikalayeh; M. Amirafshari; H.M. Mkrchyan; M. Taji
Volume 2, Issue 3 , September 2013, , Pages 35-46
Abstract
Equipment selection is one of the most important aspects of open pit design. The selection of equipment for mining applications is not a well-defined process and because it involves the interaction of several subjective factors or criteria, decisions are often complicated and may even embody contradictions. ...
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Equipment selection is one of the most important aspects of open pit design. The selection of equipment for mining applications is not a well-defined process and because it involves the interaction of several subjective factors or criteria, decisions are often complicated and may even embody contradictions. The aim of this study is introducing a multi-criteria decision making method for selecting the most appropriate combination of drilling, loading and haulage equipment using a state of the art comprehensive model. The proposed method consists of two stages, first is determining the weight of each criteria which affects the decision using fuzzy analytic network process (FANP), the next step is calculating the score of each possible combination of mining equipment using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The proposed methodology is applied for Sungun copper mine which is the largest open-cast copper mine in Iran and is among the most important copper deposits in Middle East and finally the most appropriate combination of mining equipment is determined for this open-pit mine.