Fuzzy linear assignment problem: an approach to vehicle fleet deployment

This paper proposes and examines a new approach using fuzzy logic to vehicle fleet deployment. Fleet deployment is viewed as a fuzzy linear assignment problem. It assigns each travel request to an available service vehicle through solving a linear assignment matrix of defuzzied cost entries. Each cost entry indicates the cost value of a travel request that "fuzzily aggregates" multiple criteria in simple rules incorporating human dispatching expertise. The approach is examined via extensive simulations anchored in a representative scenario of taxi deployment, and compared to the conventional case of using only distances (each from the taxi position to the source point and finally destination point of a travel request) as cost entries. Discussion in the context of related work examines the performance and practicality of the proposed approach

Female chromosome X mosaicism is age-related and preferentially affects the inactivated X chromosome

To investigate large structural clonal mosaicism of chromosome X, we analysed the SNP microarray intensity data of 38,303 women from cancer genome-wide association studies (20,878 cases and 17,425 controls) and detected 124 mosaic X events42Mb in 97 (0.25%) women. Here we show rates for X-chromosome mosaicism are four times higher than mean autosomal rates; X mosaic events more often include the entire chromosome and participants with X events more likely harbour autosomal mosaic events. X mosaicism frequency increases with age (0.11% in 50-year olds; 0.45% in 75-year olds), as reported for Y and autosomes. Methylation array analyses of 33 women with X mosaicism indicate events preferentially involve the inactive X chromosome. Our results provide further evidence that the sex chromosomes undergo mosaic events more frequently than autosomes, which could have implications for understanding the underlying mechanisms of mosaic events and their possible contribution to risk for chronic diseases

Experimental condition selection in whole-genome functional classification

Microarray technologies enable the quantitative simultaneously monitoring of expression levels for thousands of genes under various experimental conditions. This is new technology has provided a new way of learning gene functional classes on a genome-wide. Previously, lots of unsupervised clustering methods and supervised classification have shown power in assigning functional annotations based on gene coexpression. However, due to the noisy and highly dimensional nature of microarray data and the inherent heterogeneity of gene functional classes, the whole-genome learning of gene functional classes from microarray data has remained a great challenge for scientists. Currently, most of the methods do not discriminate the different attribution of experimental conditions in the learning process, which impaired the ability of learning functional classes and prevented these methods from discovering the links between the experimental conditions and gene functional classes. In this study, we perform a selection of experiment conditions during the systematically learning of ∼100 functional classes categorized in MIPS's comprehensive yeast genome database. In particular, a hybridization of genetic algorithm and k-nearest neighbors classifier has been adopted here. Through a comparison of the results with other previous methods our studies indicate promising improvements in learning performance. Further, by identifying the critical experimental conditions, significant links between the experiments and the functional classes were uncovered

Parameter identification using memetic algorithms for fuzzy systems

In recent years, fuzzy modelling has become very popular because of its ability to assign meaningful linguistic labels to fuzzy sets in the rule base. However, in order to achieve better performance in fuzzy modelling, parameter identification often needs to be performed. In this paper, we address this optimization problem using memetic algorithms (MAs) for Sugeno and Yasukawa's (SY) qualitative (fuzzy) model. MAs are essentially variants of Genetic Algorithms incorporated with local search methods (or memes) that could better improve the search control accuracy. In addressing the parameter identification problem, MAs are utilized to perform search exploitation within the neighbourhood of the prior knowledge extracted via the Improved SY fuzzy modelling approach. The use of MAs in performing parameter identification is examined empirically, and found to produce better solutions attributable to the extraction and proper use of prior knowledge

Performance of multiagent taxi dispatch on extended-runtime taxi availability: A simulation study

10.1109/TITS.2009.2033128IEEE Transactions on Intelligent Transportation Systems111231-23

Towards an automated multiagent taxi-dispatch system

10.1109/COASE.2007.4341673Proceedings of the 3rd IEEE International Conference on Automation Science and Engineering, IEEE CASE 20071045-105

A collaborative multiagent taxi-dispatch system

10.1109/TASE.2009.2028577IEEE Transactions on Automation Science and Engineering73607-61

From sequence to structure to literature: the protocol approach to bioinformation.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing747-75