Production planning under dynamic product environment: a multi-objective goal programming approach

Production planning is a complicated task that requires cooperation among multiple functional units in any organization. In order to design an efficient production planning system, a good understanding of the environment in terms of customers, products and manufacturing processes is a must. Although such planning exists in the company, it is often incorrectly structured due to the presence of multiple conflicting objectives. The primary difficulty in modern decision analysis is the treatment of multiple conflicting objectives. A formal decision analysis that is capable of handling multiple conflicting goals through the use of priorities may be a new frontier of management science. The objective of this study is to develop a multi objective goal programming (MOGP) model to a real-life manufacturing situation to show the trade-off between different some times conflicting goals concerning customer, product and manufacturing of production planning environment. For illustration, two independent goal priority structures have been considered. The insights gained from the experimentation with the two goal priority structures will guide and assist the decision maker for achieving the organizational goals for optimum utilization of resources in improving companies competitiveness. The MOGP results of the study are of very useful to various functional areas of the selected case organization for routine planning and scheduling. Some of the specific decision making situations in this context are: (i). the expected quality costs and production costs under identified product scenarios, (ii).under and over utilization of crucial machine at different combinations of production volumes, and (iii). the achievement of sales revenue goal at different production volume combinations. The ease of use and interpretation make the proposed MOGP model a powerful communication tool between top and bottom level managers while converting the strategic level objectives into concrete tactical and operational level plans.

Effect of diaphragm discontinuity in the seismic response of multi-storeyed building

Many buildings in the present scenario have irregular configurations both in elevation and plan. This in future may subject to devastating earthquakes. It is necessary to identify the performance of the structures to withstand against disaster for both new and existing buildings. Now a days openings in the floors is common for many reasons like stair cases, lighting architectural etc., these openings in diaphragms cause stresses at discontinues joints with building elements. Discontinuous diaphragms are designed without stress calculations and are thought-about to be adequate ignoring any gap effects. In this thesis an attempt is made to try to know the difference between a building with diaphragm discontinuity and a building without diaphragm discontinuity

Speech Mode Classification using the Fusion of CNNs and LSTM Networks

Speech mode classification is an area that has not been as widely explored in the field of sound classification as others such as environmental sounds, music genre, and speaker identification. But what is speech mode? While mode is defined as the way or the manner in which something occurs or is expressed or done, speech mode is defined as the style in which the speech is delivered by a person. There are some reports on speech mode classification using conventional methods, such as whispering and talking using a normal phonetic sound. However, to the best of our knowledge, deep learning-based methods have not been reported in the open literature for the aforementioned classification scenario. Specifically, in this work we assess the performance of image-based classification algorithms on this challenging speech mode classification problem, including the usage of pre-trained deep neural networks, namely AlexNet, ResNet18 and SqueezeNet. Thus, we compare the classification efficiency of a set of deep learning-based classifiers, while we also assess the impact of different 2D image representations (spectrograms, mel-spectrograms, and their image-based fusion) on classification accuracy. These representations are used as input to the networks after being generated from the original audio signals. Next, we compare the accuracy of the DL-based classifies to a set of machine learning (ML) ones that use as their inputs Mel-Frequency Cepstral Coefficients (MFCCs) features. Then, after determining the most efficient sampling rate for our classification problem (i.e. 32kHz), we study the performance of our proposed method of combining CNN with LSTM (Long Short-Term Memory) networks. For this purpose, we use the features extracted from the deep networks of the previous step. We conclude our study by evaluating the role of sampling rates on classification accuracy by generating two sets of 2D image representations – one with 32kHz and the other with 16kHz sampling. Experimental results show that after cross validation the accuracy of DL-based approaches is 15% higher than ML ones, with SqueezeNet yielding an accuracy of more than 91% at 32kHz, whether we use transfer learning, feature-level fusion or score-level fusion (92.5%). Our proposed method using LSTMs further increased that accuracy by more than 3%, resulting in an average accuracy of 95.7%

Investigation of Particulate Matter Size, Concentration and Mass Emissions from Small Handheld 2-Stroke Spark Ignition Engines

Quality of air, change in the climate and exposure of humans to pollutants have become major concerns globally over the past decade. Particulate matter has been linked to many adverse health effects. Internal combustion engines are major source of PM emissions. Knowing the adverse health effects of particulate matter, the regulatory agencies are in the process of introducing strict regulations to limit the quantity of PM emitted by off-road small handheld gasoline engines. Two-stroke small engines typically emit more smoke as they burn oil-gasoline mixture compared to four-stroke engines running on gasoline only. Current regulations in the United States for these engines regulate only HC+NOx and CO emissions. In spite of their contribution to atmospheric pollution and negative health effects, the PM emissions from handheld, two-stroke engines are yet to be regulated. This led to this study of particulate matter emissions from these engines.;The main objective of this study was to measure particulate matter size, concentration and mass distributions from 2-stroke handheld 25cc weed whacker engine and to evaluate the effect of heat treatment on these emissions in removal of volatile fractions. The exhaust sample was heat treated to different temperatures (200 °C, 150 °C, and 100 °C) before measuring the distributions to better understand what fraction of particulate matter is solid or volatile. Tests were performed in Center for Alternate Fuels, Engines and Emissions Laboratory (CAFEE) at West Virginia University. A Scanning Mobility Particle Sizer (SMPS) Model was used for measuring the particle size distribution and concentrations. The engine was operated at two steady-state modes (wide open throttle) WOT and Idle mode. The WOT mode resulted in a count median diameter (CMD) of 14.1nm when heat treated the sample to 200 ºC compared to 32.1nm for sample in CVS whereas the idle mode resulted in CMD of 5.94nm and 31.1nm respectively. This indicated the existence of volatile particles. These Nano-particles are proved to be harmful to health. Results obtained from the data for the sample in CVS and compared to the data for the heat treated samples, show that the influence of volatile fraction on PM size distribution is reduced with increase in sample conditioning temperatures. The density function used for mass distribution calculations by SMPS does not take diameter of the particle into consideration and so these calculations were compared with the mass distribution calculated by using IPSD method or effective particle density method since previous studies indicated that at ultra-low emission levels this method proved to give more precise results. This comparison resulted in a good correlation in the particle mass distribution given by SMPS