Sustainable oil selection for cleaner production in Indian foundry industries: A three phase integrated decision-making framework

2021 ◽  
pp. 127827
Author(s):  
Dilbagh Panchal ◽  
Prasenjit Chatterjee ◽  
Rohit Sharma ◽  
Rajiv Kumar Garg
2008 ◽  
Vol 07 (02) ◽  
pp. 257-260 ◽  
Author(s):  
XIANCHUN TAN ◽  
DACHENG LIU ◽  
CONGBO LI

Green manufacturing (GM in short) is beneficial to the alleviation of environment burdens. The optimization selection of tool is an important approach to improving environmental performance of cutting machining. The objective factors of decision-making problems for traditional tool selection are time, quality and cost. Based on the main idea of environmental consciousness, a decision-making framework model of GM is proposed; a multi-objective decision-making model for cutting tool selection is put forward. The objectives include Time (T), Quality (Q), Cost (C), Resources (R) and Environmental impact (E), where T aims to minimize the produce time, Q means to maximize the quality, C means to minimize the cost, R means to minimize the resource consumption and E means to minimize the environment impact respectively. Each objective is analyzed in detail and application of the Fuzzy Analysis Algorithm in the decision-making is discussed. A case study in which a practical decision-making problem of cutting tool selection for GM is analyzed and successful application of the above model shows that the model is practical.


2021 ◽  
Vol 2042 (1) ◽  
pp. 012091
Author(s):  
Nuoa Lei ◽  
Zhu Cheng ◽  
Zhi Cao ◽  
Eric Masanet

Abstract Intelligent load scheduling is an emerging approach that has the potential to facilitate extreme sustainable data center (DC) operation. However, scarcity of straightforward tools in the public domain challenges decision makers performing quantitative analysis of the DC load planning and its potential benefits. In this work, a novel integrated decision-making framework was developed to address this issue, which provides the basis for the multi-objective optimization of carbon-, water-, and economic-intelligent load scheduling. The proposed framework was demonstrated with a case study DC in California, which showed the usefulness of the proposed framework in informing sustainable DC operations.


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