Intuitionistic Trapezoidal Fuzzy Multiple Criteria Group Decision Making Method Based on Binary Relation

The aim of this paper is to develop a methodology for intuitionistic trapezoidal fuzzy multiple criteria group decision making problems based on binary relation. Firstly, the similarity measure between two vectors based on binary relation is defined, which can be utilized to aggregate preference information. Some desirable properties of the similarity measure based on fuzzy binary relation are also studied. Then, a methodology for fuzzy multiple criteria group decision making is proposed, in which the criteria values are in the terms of intuitionistic trapezoidal fuzzy numbers (ITFNs). Simple and exact formulas are also proposed to determine the vector of the aggregation and group set. According to the weighted expected values of group set, it is easy to rank the alternatives and select the best one. Finally, we apply the proposed method and the Cosine similarity measure method to a numerical example; the numerical results show that our method is effective and practical

Some Similarity Measures for Triangular Fuzzy Number and Their Applications in Multiple Criteria Group Decision-Making

We propose some similarity measures between two triangular fuzzy numbers (TFNs) based on the vector similarity measures in vector space, which can be used to aggregate the decision information with TFNs. A methodology for multiple criteria group decision-making (MCGDM) problems with triangular fuzzy information is proposed; the criteria values take the form of linguistic values, which can be converts to TFNs. According to the weighted similarity measures between each alternative and ideal alternative, it is easy to rank alternatives and select the most desirable alternative. Finally, we apply the proposed methods to an illustrative example of MCGDM; the numerical results show that our method is effective and practical. For comparison, we also apply our similarity measures method to solve the fuzzy decision-making problem in Wei (2011); our method has simpler computation and gets the same results more rapidly than the FLOWHM method

Mammoth : gearing Hadoop towards memory-intensive MapReduce applications

The MapReduce platform has been widely used for large-scale data processing and analysis recently. It works well if the hardware of a cluster is well configured. However, our survey has indicated that common hardware configurations in small and medium-size enterprises may not be suitable for such tasks. This situation is more challenging for memory-constrained systems, in which the memory is a bottleneck resource compared with the CPU power and thus does not meet the needs of large-scale data processing. The traditional high performance computing (HPC) system is an example of the memory-constrained system according to our survey. In this paper, we have developed Mammoth, a new MapReduce system, which aims to improve MapReduce performance using global memory management. In Mammoth, we design a novel rule-based heuristic to prioritize memory allocation and revocation among execution units (mapper, shuffler, reducer, etc.), to maximize the holistic benefits of the Map/Reduce job when scheduling each memory unit. We have also developed a multi-threaded execution engine, which is based on Hadoop but runs in a single JVM on a node. In the execution engine, we have implemented the algorithm of memory scheduling to realize global memory management, based on which we further developed the techniques such as sequential disk accessing, multi-cache and shuffling from memory, and solved the problem of full garbage collection in the JVM. We have conducted extensive experiments with comparison against the native Hadoop platform. The results show that the Mammoth system can reduce the job execution time by more than 40% in typical cases, without requiring any modifications of the Hadoop programs. When a system is short of memory, Mammoth can improve the performance by up to 5.19 times, as observed for I/O intensive applications, such as PageRank. Given the growing importance of supporting large-scale data processing and analysis and the proven success of the MapReduce platform, the Mammoth system can have a promising potential and impact

Some Similarity Measures for Triangular Fuzzy Number and Their Applications in Multiple Criteria Group Decision-Making

We propose some similarity measures between two triangular fuzzy numbers (TFNs) based on the vector similarity measures in vector space, which can be used to aggregate the decision information with TFNs. A methodology for multiple criteria group decision-making (MCGDM) problems with triangular fuzzy information is proposed; the criteria values take the form of linguistic values, which can be converts to TFNs. According to the weighted similarity measures between each alternative and ideal alternative, it is easy to rank alternatives and select the most desirable alternative. Finally, we apply the proposed methods to an illustrative example of MCGDM; the numerical results show that our method is effective and practical. For comparison, we also apply our similarity measures method to solve the fuzzy decision-making problem in Wei (2011); our method has simpler computation and gets the same results more rapidly than the FLOWHM method

A multi-product newsvendor problem with budget and loss constraints

The solution space of a multi-product newsvendor problem (MPNP) with two constraints is analyzed and divided by four cases. One constraint is a loss constraint, which is nonlinear and has the integral symbol, while the other is a budget constraint. The threshold values of different solution regions are calculated and the corresponding solving methods for each case are provided, reducing the complexity of problem solving. The paper suggests the loss-based marginal utility deleting method solves non-negative constraint problems, and the linear approximate approach deals with nonlinear constraint optimal problems with an integral symbol in the model. Numerical examples are given, showing that the solving approach is more effective

Specific Discrimination Polymerization for Highly Isotactic Polyesters Synthesis

Isotactic polymers have emerged with unique and excellent properties in material sciences. Specific discrimination polymerization provides an ideal pathway to achieve highly isotactic polymers from their racemic monomers, which is of great significance and a challenge in polymeric chemistry. Although an enantioselective catalyst-mediated asymmetric kinetic resolution polymerization (AKRP) process makes it possible, a general and well-defined strategy for catalyst design is still rarely reported. Here, based on a novel dual-ligand strategy, a new type of chiral (BisSalen)Al complex with high enantioselectivity has been described, in which perfect AKRP of racemic phenethylglycolide (Pegl) is achieved for the first time. The more confined asymmetric microenvironment formed by a dual ligand is the key to improve the enantioselectivity of the original catalyst. To illustrate the generality of this strategy, a series of (BisSalen)Al complexes with homo- or heterodual ligands were designed for the AKRP of Pegl

Hydrovoltaic effect-enhanced photocatalysis by polyacrylic acid/cobaltous oxide–nitrogen doped carbon system for efficient photocatalytic water splitting

The construction of efficient photocatalyst system by utilizing hydrovoltaic technology bring promise but a challenge for photocatalytic water splitting. Here, the authors report a hydrovoltaic effect-enhanced photocatalytic system that shows high efficiency and quick kinetics of water splitting