A Priority-based genetic algorithm for a flexible job shop scheduling problem
In this study, a genetic algorithm (GA) with priority-based representation is proposed for a flexible job shop scheduling problem (FJSP) which is one of the hardest operations research problems. Investigating the effect of the proposed representation schema on FJSP is the main contribution to the literature. The priority of each operation is represented by a gene on the chromosome which is used by a constructive algorithm performed for decoding. All active schedules, which constitute a subset of feasible schedules including the optimal, can be generated by the constructive algorithm. To obtain improved solutions, iterated local search (ILS) is applied to the chromosomes at the end of each reproduction process. The most widely used FJSP data sets generated in the literature are used for benchmarking and evaluating the performance of the proposed GA methodology. The computational results show that the proposed GA performed at the same level or better with respect to the makespan for some data sets when compared to the results from the literature.
Dendritic cells (DCs) vaccination is a promising way to contend cancer metastases especially in the case of immunogenic tumors. Unfortunately, it is only rarely possible to achieve a satisfactory clinical outcome in the majority of patients treated with a particular DC vaccine. Apparently, DC vaccination can be successful with certain combinations of features of the tumor and patients immune system that are not yet fully revealed. Difficulty in predicting the results of the therapy and high price of preparation of individual vaccines prevent wider use of DC vaccines in medical practice. Here we propose an approach aimed to uncover correlation between the effectiveness of specific DC vaccine types and personal characteristics of patients to increase efficiency of cancer treatment and reduce prices. To accomplish this, we suggest two-step analysis of published clinical trials results for DCs vaccines: first, the information extraction subsystem is trained, and, second, the extracted data is analyzed using JSM and AQ methodology.
In this paper author suggests a new hybrid decision support system for operation with a class of semistructured tasks with underdetermined variables. Author defined the general tasks of prediction and estimation for a class of semistructured tasks. Use of interval neural networks and genetic algorithms for such tasks is justified. Author developed the algorithm to train interval neural networks. The diagram of the offered decision support system is described. Use of technologies for parallel computation on GPU kernels is justified. Author developed an effective algorithm of the developed algorithms parallel computation. Two examples of use of the developed system are given: prediction of the sea ice area in the Northern hemisphere and prediction of client solvency for credit institutions.
Cuing a location in space produces a short-lived advantage in time. This early advantage, however, switches to a reaction time and has been termed inhibition of return (IOR). IOR behaves differently for different response modalities, suggesting that it may not be a unified effect. Ion with random, continuous cue-target Euclidean distance and cue-target onset asynchrony. These data were then used to train multiple diffusion models of saccadic and manual reaction time for these cuing experiments. Diffusion models can generate accurate distributions of reaction time. If saccadic and attentional IOR are based on similar processes, then differences in distribution will be better explained by adjusting parameter values such as signal and noise. Although experimental data show differences in the timing of the IOR, modularity, best-fit models are shown to have similar model parameters for the gradient of IOR, suggesting similar mechanisms for saccadic and manual IOR.
In this paper we observe the opportunity to offer new methods of solving NP-hard problems which frequently arise in the domain of information management, including design of database structures and big data processing. In our research we are focusing on the Maximum Clique Problem (MCP) and propose a new approach to solving that problem. The approach combines the artificial neuro-network paradigm and genetic programming. For boosting the convergence of the Hopfield Neural Network (HNN) we propose the genetic algorithm as the selection mechanism for terms of energy function. As a result, we demonstrate the proposed approach on experimental graphs and formulate two hypotheses for further research.
We present an approach based on a two-stage ltration of the set of feasible solutions for the multiprocessor job-shop scheduling problem. On the rst stage we use extensive dominance relations, whereas on the second stage we use lower bounds. We show that several lower bounds can eciently be obtained and implemented.
This proceedings publication is a compilation of selected contributions from the “Third International Conference on the Dynamics of Information Systems” which took place at the University of Florida, Gainesville, February 16–18, 2011. The purpose of this conference was to bring together scientists and engineers from industry, government, and academia in order to exchange new discoveries and results in a broad range of topics relevant to the theory and practice of dynamics of information systems. Dynamics of Information Systems: Mathematical Foundation presents state-of-the art research and is intended for graduate students and researchers interested in some of the most recent discoveries in information theory and dynamical systems. Scientists in other disciplines may also benefit from the applications of new developments to their own area of study.