An efficient hybrid evolutionary heuristic using genetic algorithm and simulated annealing algorithm to solve machine loading problem in FMS

In this paper, a machine loading problem in a flexible manufacturing system (FMS) is discussed, with bi-criterion objectives of minimising system imbalance and maximising system throughput in the occurrence of technological constraints such as available machining time and tool slots. A mathematical model is used to select machines, assign operations and the required tools in order to minimise the system's imbalance while maximising the throughput. An efficient evolutionary algorithm by hybridising the genetic algorithm (GA) and simulated annealing (SA) algorithm called GASA is proposed in this paper. The performance of the GASA is tested by using 10 sample dataset and the results are compared with the heuristics reported in the literature. The influence of genetic operators on the evolutionary search in GASA is studied and reported. Two machine selection heuristics are proposed and their influence on the quality of the solution is also studied. Extensive computational experiments have been carried out to evaluate the performance of the proposed evolutionary heuristics and the results are presented in tables and figures. The results clearly support the better performance of GASA over the algorithms reported in the literature.     

Importance of viscosity parameters in electrospinning: Of monolithic and core–shell fibers

Electrospun polymeric fibers are attractive candidates in the development of scaffolds for the tissue engineering and for providing new systems for delivery of bioactive molecules. Co-axial fibers have emerged as an efficient tool to protect the core material from the adverse conditions of electrospinning process, to spin difficult-to-process fluids and to generate fibers with much more control of the delivery of encapsulated bioactive molecules. Currently, there is very little reported work on the optimization of the processing parameters of electrospinning, especially core–shell electrospinning. This study extends the understanding of the role of solution viscosity as a vital material parameter for electrospinning of fibers. The spinning solutions were characterized for viscosity and optical imaging of the compound Taylor cone for spinnability, and the fibers were imaged by Scanning Electron Microscopy (SEM). Our experimental results, using PLGA as the model polymer, confirm that the solution concentration be above the entanglement concentration (C_e) to obtain uniform beadless monolithic fibers; for core–shell fibers, the shell solution must fulfill the above criterion for spinnability and, further, the ratio of the viscosities of core and shell solutions (η_core/η_shell) has to be greater than a threshold value to get a stable compound Taylor cone and therefore to obtain uniform beadless core–shell fibers. Addition of surfactant led to reduction of the threshold η_core/η_shell (from 0.55 to 0.18) for the PVA–PLGA system.     

Information channel diagrams: an approach for modelling information flows

In this article, the ‘information channel diagram’ (ICD) approach is introduced as a diagrammatical tool for modelling information flow during the delivery phase of organisations in which goods are deployed or delivered to customers. An initial review and evaluation of current tools for modelling information flow will be conducted based on the characteristics of information flow during the delivery phases in organisations. Diagrammatic primitives and a prescribed modelling methodology for developing an ICD will be presented, and a case scenario of the delivery phase of an organisation within the health care sector will be applied to demonstrate the use of the ICD. The article concludes by discussing some applications, generalisation potential and limitations of the ICD approach.     

Solving machine loading problems in a flexible manufacturing system using a genetic algorithm based heuristic approach

The machine-loading problem of a flexible manufacturing system (FMS) has been recognized as one of the most important planning problems. In this research, a Genetic Algorithm (GA) based heuristic is proposed to solve the machine loading problem of a random type FMS. The objective of the loading problems is to minimize the system unbalance and maximize the throughput, satisfying the technological constraints such as availability of machining time, and tool slots. The proposed GA-based heuristic determines the part type sequence and the operation-machine allocation that guarantee the optimal solution to the problem, rather than using fixed predetermined part sequencing rules. The efficiency of the proposed heuristic has been tested on ten sample problems and the results obtained have been compared with those of existing methods.     

Manganese–Schiff base complex immobilized silica materials for electrocatalytic oxygen reduction

Curtailment of platinum catalysts loading in fuel cell is a recent central issue. As substitutes, these days several organic metal chelate compounds having featured moieties of M–N₄ or M–N₂O₂ (M = transition metal ion) are being used as cathode catalysts in fuel cells. Here, in this study, we report in detail the electrocatalytic activity of manganese–Schiff base complexes for oxygen reduction reaction in 0·05 M HClO₄ at room temperature. Actually, [Mn(salen)]⁺: [N,N′-bis(salicylaldehyde) ethylenediimino manganese(III)]⁺ and [Mn(salophen)]⁺: [N,N′-bis(salicylaldehyde)-1,2-phenylenediimino manganese(III)]⁺ were introduced into/onto the MCM-41 type silica spheres and used for the electrocatalytic reduction of oxygen. Synthesized materials were characterized by UV–Vis, FT–IR and electrochemical techniques. Significant low overpotential for oxygen reduction in 0·05 M HClO₄ on [Mn(salen)]⁺- and [Mn(salophen)]⁺-incorporated silica-modified glassy carbon electrodes was observed.     

Optimization of fermentation condition for co-production of ethanol and 2,3-butanediol (2,3-BD) from hemicellolosic hydrolysates by Klebsiella oxytoca XF7

Microbial production of ethanol and 2,3-butanediol (2,3-BD) from agro-residues has been attracting interest because of their applications in various industries, including generation of biofuel molecules. In the present investigation, the hemicellulosic fraction of corncob was hydrolyzed by indigenous holocellulase from novel psychrotolerant Aspergillus niger SH3 resulting in high xylose release (34.61?g?L^?1), followed by the bioconversion of xylose to ethanol and 2,3-BD. Taguchi design was adopted to optimize the process which resulted in 5.25- and 3.31-fold increase in 2,3-BD (12.18?±?0.53?g?L^?1) and ethanol (4.08?±?0.03?g?L^?1), as compared with un-optimized condition. For the first time, co-production of ethanol and 2,3-BD from the corncob hemicellulosic hydrolysate was performed using a newly isolated Klebsiella oxytoca XF7 strain, under the optimized fermentation conditions. These results suggest that K. oxytoca XF7 is a promising candidate for co-production of ethanol and 2,3-BD, with high xylose conversion efficiency (96.65%), facilitating the economical production of biofuel molecules.     

Inactivation efficacy of plasma-activated water: influence of plasma treatment time,exposure time and bacterial species

This study aimed to evaluate the influence of plasma treatment time, bacterial exposure time to PAW and bacterial species on the inactivation efficacy of plasma-activated water (PAW), with additional investigation of the inactivation mechanisms of PAW. Six bacterial species, including Listeria innocua, Staphyloccus aureus, Escherichia coli, Pseudomonas fluorescens, Shewanella putrefaciens and Aeromonas hydrophila were selected as the representative bacteria. The initial bacterial concentration was around 7 log CFU ml⁻¹ after mixing with PAW, and the inactivation efficacy was measured after different exposure times during the 4 °C storage. Scanning electron microscopy (SEM) images of the bacteria after PAW treatment were carried out to inspect the cell structure damage, and physicochemical properties of PAW, including pH, conductivity and long-living reactive species of H₂O₂, , and , were examined. The results showed that the inactivation efficacy of PAW was positively correlated with plasma treatment time and bacterial exposure time, and for the species examined in this study, the Gram-negative species were more sensitive to PAW than the Gram-positive species. Cell structure damage, including shrinkage, distortion, or holes, was observed after PAW treatment. The pH of PAW was acidified to 2.5–2.9, and conductivity was significantly increased to 518.0 μs cm⁻¹. and H₂O₂ were reduced during the 48 h storage, while an increased concentration was observed for . This study demonstrated that the processing parameters of plasma treatment time, exposure time and characteristics of bacteria can significantly affect the inactivation efficacy of PAW.     

A multilevel hybrid anomaly detection scheme for industrial wireless sensor networks

Real-time sensing plays an important role in ensuring the reliability of industrial wireless sensor networks (IWSNs). Sensor nodes in IWSNs have inherent limitations that give rise to different anomalies in the network. These anomalies can lead to disastrous and harmful situations or even serious system failures. This article presents a formulation to the design of an anomaly detection scheme for detecting the anomalous node along with the type of anomaly. The proposed scheme is divided into two major parts. First, spatiotemporal correlation within a cluster is obtained for the normal and anomalous behavior of sensor nodes. Second, the multilevel hybrid classifier is used by combining the sequential minimal optimization support vector machine (SMO-SVM) as a binary classifier with optimally pruned extreme learning machine (OP-ELM) as a multiclass classifier for detection of an anomalous node and type of anomalies, respectively. Mahalanobis distance-based lightweight K-Medoid clustering is used to build a new set of training datasets that represents the original training dataset, by significantly reducing the training time of a multilevel hybrid classifier. Results are analyzed using standard WSN datasets. The proposed model shows high accuracy, i.e., 94.79% and detection rate, i.e., 94.6% with a reduced false positive rate as compared to existing hybrid methods.