In this paper, a multi-mode bi-objective resource investment problem in which the first objective function tries to minimize the completion time of the project and the second one tries to minimize the total resource costs is considered. Due to problem complexity, two modified meta-heuristic algorithms namely NSGA-II and MOPSO are applied to solve the model. To compare the algorithms, a set of test problem is considered. Furthermore, a MADM approach called TOPSIS is applied to compare the algorithms' results. To compare the algorithms' performance, six well-known metrics, as well as the graphical comparison are considered. Finally, the computational results declare that NSGA-II has better performance regarding the performance metrics.

Nonlinear dimensionality reduction methods have demonstrated top-notch performance in many pattern recognition and image classification tasks. Despite their popularity, they suffer from highly expensive time and memory requirements, which render them inapplicable to large-scale datasets. To leverage such cases we propose a new method called "Path-Based Isomap". Similar to Isomap, we exploit geodesic paths to find the low-dimensional embedding. However, instead of preserving pairwise geodesic distances, the low-dimensional embedding is computed via a path-mapping algorithm. Due to the much fewer number of paths compared to number of data points, a significant improvement in time and memory complexity with a comparable performance is achieved. The method demonstrates state-of-the-art performance on well-known synthetic and real-world datasets, as well as in the presence of noise.

This paper studies a speci c resource-constrained project scheduling problem under uncertainty. To do so, the problem is investigated in fuzzy environment and the goal is to maximize the net present value of the project cash ows. The problem is rst mathematically formulated. Then, a hybrid Genetic Algorithm is proposed and tuned to solve this NP-hard problem. The performance of the proposed algorithm is evaluated with comparing two well-known metaheuristic algorithms through a set of instances. Finally, comprehensive computational results are illustrated and the results are analyzed and discussed.

A Bernoulli Mixture Model (BMM) is a finite mixture of random binary vectors with independent dimensions. The problem of clustering BMM data arises in a variety of real-world applications, ranging from population genetics to activity analysis in social networks. In this paper, we analyze the clusterability of BMMs from a theoretical perspective, when the number of clusters is unknown. In particular, we stipulate a set of conditions on the sample complexity and dimension of the model in order to guarantee the Probably Approximately Correct (PAC)-clusterability of a dataset. To the best of our knowledge, these findings are the first non-asymptotic bounds on the sample complexity of learning or clustering BMMs.

Association mapping of genetic diseases has attracted extensive research interest during the recent years. However, most of the methodologies introduced so far suffer from spurious inference of the disease-causing sites due to population inhomogeneities. In this paper, we introduce a statistical framework to compensate for this shortcoming by equipping the current methodologies with a state-of-the-art clustering algorithm being widely used in population genetics applications. The proposed framework jointly infers the disease causal factors and the hidden population structures. In this regard, a Markov Chain-Monte Carlo (MCMC) procedure has been employed to assess the posterior probability distribution of the model parameters. We have implemented our proposed framework on a software package whose performance is extensively evaluated on a number of synthetic datasets, and compared to some of the well-known existing methods such as STRUCTURE. It has been shown that in extreme scenarios, up to 10 − 15% of improvement in the inference accuracy is achieved with a moderate increase in computational complexity.

Abstract. Choice-based network revenue management concentrates on importing choice models within the traditional revenue management system. Multinomial logit is a popular and well-known model which is the basic choice model in revenue management. Empirical results indicate inadequacy of this model for predicting itinerary shares; therefore, more realistic models, such as nested logit, can be proposed for substituting it. Incorporating complex choice models in the optimization module based on statistical tests without considering the complexity of the obtained mathematical model would lead to increase in the complexity of a system without obtaining signi cant improvement. Considering the in uence of discrete choice model on the structure of optimization model, it is necessary to analyze the interaction between speci c discrete choice and optimization models. In this paper, a knowledge acquisition subsystem is introduced for providing intelligence and considering the most suitable choice models. We develop the feedforward multilayer perceptron arti cial neural network for forecasting revenue improvement percent obtained by using more realistic choice models. The obtained results demonstrate that the new system will decrease the complexity of the system, simultaneously, while preserving revenue of the rm. According to the computational results, by increasing the resource restriction, the process of incorporating more realistic choice model will be more important.

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In this paper, some codes are designed for a binary chip-asynchronous CDMA system which guarantee errorless communication in the absence of noise. These codes also show good performance for noisy channels. In addition, lower and upper bounds for the sum channel capacity are derived for finite and asymptotic cases with the assumption of both noiseless and noisy channels. The results are derived assuming that user delays are known at the receiver end. The performance of proposed codes in the noisy case is also compared to both Gold sequences and a similar class of binary sequences with constrained amount of correlation.