short term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. although various methodologies have been applied to forecast traffic parameters, several researchers have showed that compared with the individual methods, hybrid methods provide more accurate results . these results made the hybrid tools and approaches a more common method for ...

Flow-shop scheduling problem (FSP) deals with the scheduling of a set of jobs that visit a set of machines in the same order. The FSP is NP-hard, which means that an efficient algorithm for solving the problem to optimality is unavailable. To meet the requirements on time and to minimize the make-span performance of large permutation flow-shop scheduling problems in which there are sequence dep...

in this study, the stochastic cell formation problem with developing model within queuing theory with stochastic demand, processing time and reliability has been presented. machine as server and part as customer are assumed where servers should service to customers. since, the cell formation problem is np-hard, therefore, deterministic methods need a long time to solve this model. in this study...

in this paper, we consider portfolio selection problem in which security returns are regarded as fuzzy variables rather than random variables. we first introduce a concept of absolute deviation for fuzzy variables and prove some useful properties, which imply that absolute deviation may be used to measure risk well. then we propose two mean-absolute deviation models by defining risk as abs...

This paper presents a hybrid genetic algorithm to solve the uncapacitated location allocation problems as a combinatorial optimization problem. The proposed method incorporates a modified K-means algorithm that clusters the customers into groups based on the rectilinear distance, and then the initial population of solutions is calculated according to the derived centers of clusters. The hybrid ...

In real world scenario, large number of features represents a data, but all these are not useful.In this paper,hybrid algorithm comprising modified genetic (GA) for segmentation andnormalised sum square deviation (NSSD) feature selection is proposed. The proposed tested one standard dataset, which gives an average accuracy 94.3% neural network classifier. Keywords: Classification, segmentation,...

The composite stock cutting problem is defined as allocating rectangular and irregular patterns onto a large composite stock sheet of finite dimensions in such a way that the resulting scrap will be minimized. In this paper, we introduce a new hybrid algorithm called Mean Field Genetic Algorithm (MGA) which combines the benefit of rapid convergence property of Mean Field Annealing (MFA) and the...