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Green Productivity Tools and Techniques
Published in Guttila Yugantha Jayasinghe, Shehani Sharadha Maheepala, Prabuddhi Chathurika Wijekoon, Green Productivity and Cleaner Production, 2020
Guttila Yugantha Jayasinghe, Shehani Sharadha Maheepala, Prabuddhi Chathurika Wijekoon
A decision matrix helps to analyze several options by considering different factors. There is no standard way to design the table or scale, and thus the GP team can design their own table and scale for developing the decision matrix. Figure 3.16 shows an example of a decision matrix.
Shoreline change detection using DSAS technique: Case of North Sinai coast, Egypt
Published in Marine Georesources & Geotechnology, 2019
Karim Nassar, Wael Elham Mahmod, Hassan Fath, Ali Masria, Kazuo Nadaoka, Abdelazim Negm
A decision matrix is a simple tool that can be very useful in making complex decisions, especially in cases where there are many alternatives and many criteria of varying importance to be considered. In the present study, decision matrices have been created based on LRR results in the period of (1989–2016) for zones I, II, and III to be used as a qualitative and quantitative tool to enable the Egyptian government to take the appropriate procedures toward the existing coastal issues in the North Sinai coast. The final decision is adopted based on dividing each zone into a group of subregions (Figures 101112) and evaluating each according to bilateral evaluations. The first one is according to the severity of the erosion and accretion (Table 3). Nevertheless, the second evaluation has comprised the risk level of the near-shore land use services susceptible to erosion and sedimentation hazards which were virtually studied previously by Frihy and El-Sayed (2013) and Mahapatra, Ramakrishnan, and Rajawat (2015), as they classified the risk sensitivity into high, moderate, and low. This classification has originally been studied on the basis of the potential socioenvironmental losses that could be incurred by nearby coastal services as a result of coastal degradation and progradation. In this research, a maximum likelihood supervised classification of the land cover is done for each zone (Figures 101112). Based on this classification, the risk index for each subregion is determined by virtue of the land use coefficients obtained from Mahapatra, Ramakrishnan, and Rajawat (2015).
MCDM approach for selection of raw material in pulp and papermaking industry
Published in Materials and Manufacturing Processes, 2020
Meenu Singh, Millie Pant, R. D. Godiyal, Arvind Kumar Sharma
For the fibers with more than one sample values, an interval value with 95% confidence is generated based on one-tailed t-test that represents the maximum value a fiber can have for a particular criterion. The data is then used as numerical performance values in the initial decision matrix for calculating criteria weights and for ranking the alternatives.