Explore the vast range of titles available.

The paper presents an algorithm for integrated scheduling, dispatching, and conflict-free routing of jobs and AGVs in FMS environment using a hybrid genetic algorithm. The algorithm generates an integrated schedule and detail routing paths while optimizing makespan, AGV travel time, and penalty cost due to jobs tardiness and delay as a result of conflict avoidance. The multi-objective fitness function use adaptive weight approach to assign weights to each objective for every generation based on objective improvement performance. Fuzzy expert system is used to control genetic operators using the overall population performance improvements of the last two previous generations. Computational experiments was conducted on the developed algorithm coded in Matlab to test the effectiveness of the algorithm. Integrated scheduling of jobs in FMS which are in synchrony with AGV dispatching, scheduling, and routing proved to ensure the feasibility and effectiveness of all the solutions of the integrated constituent elements.

Dual fuel engines constitute a viable solution for enhancing the environmental sustainability of the shipping operations. Although these engines comply with the Tier III NOx emissions regulations when operating at the gas mode, additional measures are required to ensure such compliance at the diesel mode. Hence, this study aimed to optimise the settings of a marine four-stroke dual fuel (DF) engine equipped with exhaust gas recirculation (EGR) and air bypass (ABP) systems by employing simulation and optimisation techniques, so that the engine when operating at the diesel mode complies with the ‘Tier III’ requirements. A previous version of the engine thermodynamic model was extended to accommodate the EGR and ABP systems modelling. Subsequently, a combination of optimisation techniques including multiobjective genetic algorithms (MOGA) and design of experiments (DoE) parametric runs was employed to identify both the engine and the EGR/ABP systems settings with the objective to minimise the engine brake specific fuel consumption and reduce the NOx emissions below the Tier III limit. The derived simulation results were employed to analyse the EGR system involved interactions and their effects on the engine performance and emissions trade-offs. A sensitivity analysis was performed to reveal the interactions between considered engine settings and quantify their impact on the engine performance parameters. The derived results indicate that EGR rates up to 35% are required, so that the investigated engine with EGR and ABP systems, when operating at the diesel mode, achieves compliance with the ‘Tier III’ NOx emissions, whereas the associated engine brake specific fuel consumption penalty is up to 8.7%. This study demonstrates that the combination of EGR and ABP systems can constitute a functional solution for achieving compliance with the stringent regulatory requirements and provides a better understating of the underlined phenomena and interactions of the engine subsystems parameters variations for the investigated engine equipped with EGR and ABP systems.

The rising environmental problems due to fossil fuels’ consumption have pushed researchers and technologists to develop sustainable power systems. Due to properties such as abundance and nontoxicity of the working fluid, the supercritical carbon (sCO2) dioxide Brayton cycle is considered one of the most promising technologies among the various sustainable power systems. In the current study, a mathematical model has been developed and coded in Matlab for the recompression of the supercritical carbon dioxide Brayton cycle sCO2-BC. The real gas properties of supercritical carbon dioxide (sCO2) were incorporated into the program by pairing the NIST’s Refporp with Matlab© through a subroutine. The impacts of the various designs of the cycle’s individual components have been investigated on the performance of sCO2−BC. The impact of various sedative cycle parameters, i.e., compressor’s inlet temperature (T1), and pressure (P1), cycle pressure ratio (Pr), and split mass fraction (x), on the cycle’s performance (ηcyc) were studied and highlighted. Moreover, an optimization study using the genetic algorithm was carried out to find the abovementioned cycle’s optimized values that maximize the cycle’s per-formance under provided design constraints and boundaries.

Purpose The simulated temperature profile of the printed circuit board assembly (PCBA) during reflow soldering process deviates from the actual profile. To reduce this relative deviation, a new strategy based on the Kriging response surface and the Multi-Objective Genetic Algorithm (MOGA) optimizing method is proposed. Design/methodology/approach The simulated temperature profile of the PCBA during reflow soldering process deviates from the actual profile. To reduce this relative deviation, a new strategy based on the Kriging response surface and the MOGA optimizing method is proposed. Findings Several critical influencing parameters such as temperature and the convective heat transfer coefficient of the specific temperature zones are selected as the correction parameters. The hyper Latins sampling method is implemented to distribute the design points, and the Kriging response surface model of the soldering process is constructed. The updated model is achieved and validated by the test. The relative derivation is reduced from the initial value of 43.4%–11.8% in terms of the time above the liquidus line. Originality/value A new strategy based on the Kriging response surface and the MOGA optimizing method is proposed.

This chapter contains sections titled: 1Bibliography

In this chapter, the authors consider microarray data consisting of three sets of two‐class cancer samples. The chapter describes the relevant preliminaries on rough‐set theory, multiobjective genetic algorithms (MOGAs), and microarray gene expression data. The redundancy reduction to better handle the high‐dimensional data, the basic notions used in the evolutionary‐rough feature selection algorithm, and the algorithm itself are described. The performance of the algorithm is demonstrated on microarray gene expression data involving very high‐dimensional attributes. Comparative study and analysis of the results are also included. Gene expression data typically consist of a small number of samples with very large number of features, of which many are redundant. The chapter considers two‐class problems, particularly diseased and normal samples, or two varieties of diseased samples. Nondominated sorting genetic algorithm (NSGA‐II) is modified, in the chapter, to effectively handle large data sets.

Issue Title: Advances in Pharmacophores and 3-D Screening This paper describes the extension of our earlier multiobjective method for generating plausible pharmacophore hypotheses to incorporate partial matches. Diverse sets of molecules rarely adopt exactly the same binding mode, and so allowing the identification of partial matches allows our program to be applied to larger and more diverse datasets. The method explores the conformational space of a series of ligands simultaneously with their alignment using a multiobjective genetic algorithm (MOGA). The principles of Pareto ranking are used to evolve a diverse set of pharmacophore hypotheses that are optimised on conformational energy of the ligands, the goodness of the overlay and the volume of the overlay. A partial match is defined as a pharmacophoric feature that is present in at least two, but not all, of the ligands in the set. The number of ligands that map to a given pharmacophore point is taken into account when evaluating an overlay. The method is applied to a number of test cases extracted from the Protein Data Bank (PDB) where the true overlay is known.[PUBLICATION ABSTRACT]

Pharmacophore methods provide a way of establishing a structure activity relationship for a series of known active ligands. Often, there are several plausible hypotheses that could explain the same set of ligands and, in such cases, it is important that the chemist is presented with alternatives that can be tested with different synthetic compounds. Existing pharmacophore methods involve either generating an ensemble of conformers and considering each conformer of each ligand in turn or exploring conformational space on-the-fly. The ensemble methods tend to produce a large number of hypotheses and require considerable effort to analyse the results, whereas methods that vary conformation on-the-fly typically generate a single solution that represents one possible hypothesis, even though several might exist. We describe a new method for generating multiple pharmacophore hypotheses with full conformational flexibility being explored on-the-fly. The method is based on multiobjective evolutionary algorithm techniques and is designed to search for an ensemble of diverse yet plausible overlays which can then be presented to the chemist for further investigation.