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Static VAR compensators (SVC) have been recognized to be one of the most important flexible AC transmission systems devices used for mitigating the low-frequency electrochemical oscillations occurring in the system and for reactive power compensation, thereby improving the overall dynamic stability and efficiency of the system. The purpose of this paper is to optimize and dynamically tune the control parameters of the classical proportional integral and derivative (PID) controller of the SVC for a two-machine system by designing a new robust optimization technique.

The angular speed deviation between the two machines is used as an auxiliary signal to SVC for generation of the required damping output. To justify the efficacy of the system undertaken, a light load fault at time t = 1 s is projected to the system. The simulation is carried out in MATLAB/Simulink architecture.

The proposed technique helps in the enhancement of system efficiency, reliability and controllability and by effectively responding to the non-linearities taking place in a power grid network. The results obtained are indicative of the fact that the proposed modified brain storming optimization (MBSO) technique reduces system disturbances very quickly, increases the system response in terms of better rise time, settling time and peak overshoot and improves the efficiency of the system.

A detailed comparison of the MBSO technique is compared with the conventional brain storming optimization (BSO) and PID technique. Total harmonic distortion through fast Fourier transform is also compiled to prove that the values of the proposed MBSO method found out to be confined well within the prescribed IEEE-514 boundaries.

For decades, continuous research work is going on to maximize the power harvested from the sun; however, there is only a limited analysis on exploiting the microwatt output power from indoor lightings. Microelectronic system has power demand in the µW range, and therefore, indoor photovoltaics would be appropriate for micro-energy harvesting appliances. “Energy harvesting is defined as the transfer process by which energy source is acquired from the ambient energy, stored in energy storage element and powered to the target systems”. The theory of energy harvesting is: gathering energy from surroundings and offering technological solutions such as solar energy harvesting, wind energy collection and vibration energy harvesting. “The solar cell or photovoltaic cell (PV), is a device that converts light into electric current using the photoelectric effect”. Factors such as light source, temperature, circuit connection, light intensity, angle and height can manipulate the functions of PV cells. Among these, the most noticeable factor is the light intensity that has a major impact on the operations of solar panels.

This paper aims to design an enhanced prediction model on illuminance or irradiance by an optimized artificial neural network (ANN). The input attributes or the features considered here are temperatures, maxim, TSL, VI, short circuit current, open-circuit voltage, maximum power point (MPP) voltage, MPP current and MPP power, respectively. To enhance the performance of the prediction model, the weights of ANN are optimally tuned by a new self-improved brain storm optimization (SI-BSO) model.

The superiority of the implemented work is compared and proved over the conventional models in terms of error analysis and prediction analysis. Accordingly, the presented approach was analysed and its superiority was proved over other conventional schemes such as ANN, ANN-Levenberg–Marquardt (LM), adaptive-network-based fuzzy inference system (ANFIS) and brainstorm optimization (BSO). In addition, analysis was held with respect to error measures such as mean absolute relative error (MARE), mean square root error (MSRE), mean absolute error and mean absolute percentage error. Moreover, prediction analysis was also performed that revealed the betterment of the presented model. More particularly, the proposed ANN + SI-BSO model has attained minimal error for all measures when compared to the existing schemes. More particularly, on considering the MARE, the adopted model for data set 1 was 23.61%, 48.12%, 79.39% and 90.86% better than ANN, ANN-LM, ANFIS and BSO models, respectively. Similarly, on considering data set 2, the MSRE of the implemented model was 99.87%, 70.69%, 99.57% and 94.74% better than ANN, ANN-LM, ANFIS and BSO models, respectively. Thus, the enhancement of the presented ANN + SI-BSO scheme has been validated effectively.

This work has established an improved illuminance/irradiance prediction model using the optimization concept. Here, the attributes, namely, temperature, maxim, TSL, VI, Isc, Voc, Vmpp, Impp and Pmpp were given as input to ANN, in which the weights were chosen optimally. For the optimal selection of weights, a novel ANN + SI-BSO model was established, which was an improved version of the BSO model.

Among the many different methods used to generate new product ideas, group brainstorming has been one of the most popular. However, brainstorming has fallen into disfavor with many practitioners and researchers on the basis of such factors as the necessity for a skilled group leader, the potential for conflicts among members which can disrupt the process, and the possibility of one or more members dominating the discussion. Brainwriting, which is the silent, written generation of ideas by a group, is proposed as an alternative to brainstorming. Six different group brainiwriting techniques are described and suggestions given for the most appropriate use of each. It is concluded that both brainwriting and brainstorming will be useful in different situations and should be viewed as supplemental rather than primary sources of new product ideas. Furthermore, it is noted that idea generation is only part of the process. The best ideas in the world will be of little value if they are not implemented successfully.

The purpose of this paper is to propose a new algorithm chaotic pigeon-inspired optimization (CPIO), which can effectively improve the computing efficiency of the basic Itti’s model for saliency-based detection. The CPIO algorithm and relevant applications are aimed at air surveillance for target detection.

To compare the improvements of the performance on Itti’s model, three bio-inspired algorithms including particle swarm optimization (PSO), brain storm optimization (BSO) and CPIO are applied to optimize the weight coefficients of each feature map in the saliency computation.

According to the experimental results in optimized Itti’s model, CPIO outperforms PSO in terms of computing efficiency and is superior to BSO in terms of searching ability. Therefore, CPIO provides the best overall properties among the three algorithms.

The algorithm proposed in this paper can be extensively applied for fast, accurate and multi-target detections in aerial images.

CPIO algorithm is originally proposed, which is very promising in solving complicated optimization problems.

The purpose of this paper is to model the key variables of logistics outsourcing relationship between shippers and logistics service providers (LSPs) and to study their influence on productivity and competitiveness of the shipper company.

In this paper, an interpretive structural modeling (ISM) based approach has been used to model the variables of logistics outsourcing relationship. Various variables, used by researchers and practiced by the shippers for effective management of logistics outsourcing relationship have been identified. These variables have been classified as enablers and outcome variable. Enablers are those variables that boost the “relationship bond” between shippers and LSPs, while outcome variables are the resultant variables arising out of outsourcing relationship between shippers and LSPs.

A key finding of this modeling helps shippers as well as LSPs to take various initiatives, in order to have prosperous, outsourcing relationship between shippers and LSPs. Top management from both shippers as well as LSPs should focus, on improving on the enablers such as trust or commitment, direct assistance, long term contract, evaluation of supplier performance, practices of TQM and JIT to add distinctive values, and top management support.

In this research, an interpretation in terms of driving and dependence powers has been carried out for enablers and outcome variables of logistics outsourcing relationship. Those variables found possessing strong driving power in the ISM model, need to be taken care on a priority basis because of their impact on dependent variables. A variable emerging with high dependence contributes to productivity enhancement and competitiveness in a logistical supply chain.

The research paper presents analysis and prioritization of barriers influencing the improvement in the effectiveness of manufacturing system. The purpose of this paper is to develop an integrated fuzzy-based multi-criteria decision-making (F-MCDM) framework to assist management of the case company in the selection of most effective manufacturing system. The framework helps in prioritizing the manufacturing systems on the basis of their effectiveness affected by the barriers.

In this paper, on the basis of experts’ opinion, five barriers have been identified in a brain-storming session. The problem of prioritization of manufacturing system is a multi-criteria decision-making (MCDM) problem and hence is solved by using the F-MCDM approach using dominance matrix.

Manufacturing systems’ effectiveness for Indian industries is influenced by barriers. The prioritization of manufacturing systems depends on qualitative factor decision-making criteria. Among the manufacturing systems, leagile manufacturing system is given the highest priority followed by lean manufacturing system, agile manufacturing system, flexible manufacturing system and cellular manufacturing system.

The selection of an appropriate manufacturing system plays a vital role for sustainable growth of the manufacturing company. In the present work, barriers which influence the effectiveness of manufacturing system have been identified. On the basis of degree of influence of barriers on the effectiveness of the manufacturing system, five alternative manufacturing systems are prioritized. The framework will help the management of the case company to take reasonable decision for the adoption of the appropriate manufacturing system.

The results of the research work are very useful for the manufacturing companies interested in analyzing the alternative manufacturing systems on the basis of their effectiveness and their sensitivity toward various barriers. The management of Indian manufacturing company will take decision to adopt a manufacturing system whose effectiveness is least sensitive toward barriers. Effectiveness of such manufacturing system will improve with time without having retardation due to barriers. With improved effectiveness of the manufacturing system, the manufacturing company would be able to survive with global competition. The result of the present work is based on the inputs from the case company and may vary for the other manufacturing company. In the present work, only five alternative manufacturing systems and five barriers have been considered. To obtain the better result, MCDM approach with more number of alternative manufacturing systems and barriers might be considered.

The research work is based on the fuzzy analytic hierarchy process framework and on the case study conducted by the authors. The work carried out is original in nature and based on the real-life case study.

To define creativity in precise terms is not only difficult but may also be restrictive, because many aspects involved in this type of behaviour still lack an objective and quantitative means of measurement. In general, research writers define creativity as a mental synthesis among cognitive factors which are distant and unrelated, or as the discovery of new factors which enlarge our perceptual field. This is what J.P. Guilford calls “the divergent thought” to point out the impulse to explore which is typical of this behaviour. In this sense, the creative phase of our mental activity is seen as distinct from the rational phase. The former is stimulated by an emotional need to explore new ways and the latter more concentrated on the rational use of logic in the problem solution process.

The purpose of this paper is to discuss and highlight the results of a study that identified critical risks involved in the procurement of community‐based infrastructure projects in Zambia.

Brainstorming was chosen as the method for identifying risks in the study. It was applied in the form of group discussions with project management committees at community level to identify potential risks affecting their particular projects.

The identified critical risks were classified into six categories: project initiation; community contribution and participation; budget and finance; skilled labour; materials procurement and technical supervision; and quality control. Several significant risk factors were identified in all these six categories in community‐based construction projects. The paper concludes that indeed there are critical risks in community‐based construction projects that require forward planning, assessment and mitigation.

The consequences of not assessing and managing construction risks in both conventional and community‐based procurement systems are that projects may experience time and cost overruns and lead to poor quality structures. It is therefore important to identify risks in community projects.

Chief executives disclose various organisational variables as stifling creativity and organisational change. It is argued that, as a result, organisations suffer losses in terms of, for example, effectiveness and positive image. The creative individual feels frustrated. Such measures as the use of creativity management meetings, brain‐storming sessions, training and development, and reward and recognition of creativity could resolve some of these inhibitors.

Our multiparadigm software toolset (consisting of BATEV, DEBATER and CyberDELPHI software tools) enables a holistic forecasting exercise, combining some good aspects of various foresight paradigms, based on both panel activities (scenarios, recommendations, policy proposals, etc.) and a large scale e‐collaboration among wide expert base. The e‐collaboration encompasses “via‐net” Delphi survey and software mediation and facilitation, based upon intelligent group decision support paradigm. The combination of multiple paradigms supplies a firm foundation for addressing complex technology foresight problems more objectively. Following a brain‐storming process (assisted by BATEV technology repository, and DEBATER intelligent decision support system for technology assessment) in which potential future opportunities for scientific and technological advances are identified, panels engage in an extensive and collaborative consultation process, using the CyberDELPHI e‐collaboration software. The CyberDELPHI implementation is very useful in reducing the time required to evaluate the experts’ responses and also helps to encourage experts to give as much detail as possible. It has been used succesfully in the preliminary phase of a Regional Programme on Technology Foresight for Latin America, launchad by UNIDO (United Nations Industrial Development Organisation) and ICS (International Centre for Science and High Technology).