Recently, various vertical handover decision algorithms have been proposed, such as multiplicative exponent weighting (MEW), simple additive weighting (SAW), technique for order preference by similarity to ideal solution (TOPSIS), grey relational analysis (GRA), and fuzzy multiple attribute decision making (MADM) algorithms [7, 13, 14].
This work presents a new scheme, called GRAMAKA, which combines the GRA algorithm [12] with multiple attributes and knapsack algorithm.
In this paper, a new uplink bandwidth allocation scheme, grey relational analysis with multiple attributes and knapsack algorithms (GRAMAKA), which combines GRA with multiple attributes [13] and the knapsack algorithm [14], is proposed to improve resource utilization while satisfying the user's requirements of throughput and fairness.
With the aid of multiple attribute group decision making, we design an algorithm to determine the values of losses used in TFDTRS.
This algorithm compares observations across multiple attribute values, allowing for both community utilization patterns and hospital location to be evaluated simultaneously during cluster formation.
This novel Multiple Attribute Decision Making (MADM) model takes the advantages of the U2P-Miner algorithm, the interval data weighting method, and the Linear Assignment Method (LAM).
Decisions on attribute weights are important problems in multiple attribute decision making.
In multiple attribute solutions, the authors improve upon the QoS perceived by the mobile node in the network selection process through the use of multiple attribute decision maker (MADM) algorithms which leverage multiple network conditions and performance metrics.
WP is a relative simple multiple attribute utility methods.
Further, by using the 2-step K-means + Ward's algorithm, the new methodology is built upon a well-understand and accepted procedure to identify clusters when observations have multiple attribute values and similarity across all attributes is required.
