Search

Numerous research based on offline Tamil recognition deals only with few Tamil characters since it becomes extremely complicated in distinguishing small variations in large handwritten document. The writer's complexity affects the overall formation of the characters. Such types of complexities are due to discontinuation of structures, unnecessary over loops, variation in shapes as well as irregular curves. This complex issue results in enhanced error value rate. Therefore, to conquer such issues, this paper proposes a novel approach to enhance the offline Tamil handwritten character recognition by utilizing four principal steps: pre-processing, segmentation, feature extraction and classification. For optimal segmentation of Tamil characters, this paper utilizes the Tsallis entropy approach-based atom search (TEAS) optimization algorithm. Then a Newton algorithm based deep convolution extreme learning (DELM) approach is utilized for the extraction and classification of input images. Finally, experiments are carried out for numerous Tamil handwritten recognition-based approaches. The proposed Tamil character recognition utilizes the datasets of isolated Tamil handwritten characters established by HP lab India to evaluate the efficiency of the system. [ABSTRACT FROM AUTHOR]

Global warming caused by CO2 emissions will directly harm the health and quality of life of people. Accurate prediction of CO2 emissions is highly important for policy-makers to formulate scientific and reasonable low-carbon environmental protection policies. To accurately predict the CO2 emissions of the world's major economies, this paper proposes a new fractional grey Bernoulli model (FGBM(1,1, t α )). First, this paper introduces the modeling mechanism and characteristics of the FGBM(1,1, t α ) model. The new model can be transformed into other grey prediction models through parameter adjustment, so the new model exhibits high adaptability. Second, this paper employs four carbon emission datasets to establish a grey prediction model, calculates model parameters with three optimization algorithms, adopts two evaluation criteria to evaluate the accuracy of the model results, and selects the optimization algorithm and model results that yield the highest model accuracy, which verifies that the FGBM(1,1, t α ) model is more feasible and effective than the other six grey models. Finally, this paper applies the FGBM(1,1, t α ) model to predict the CO2 emissions of the USA, India, Asia Pacific, and the world over the next 5 years. The forecast results reveal that from 2020 to 2024, the CO2 emissions of India, the Asia Pacific region, and the world will gradually rise, but that in USA will slowly decline over the next 5 years. [ABSTRACT FROM AUTHOR]

Agriculture and its allied sector provide the livelihoods of vast rural areas in India. It also plays a vital role in the growth of developing countries like India. Rice, one of the primary components of Indian agriculture plays a significant role in the food security of the country and the world. However, it is stricken by many diseases that affect the quality and quantity of rice yield. This paper proposes a custom CNN architecture for detecting and classifying common diseases found in rice plants by reducing the number of parameters associated with the network. The proposed CNN architecture has been trained using a dataset of four types of common rice plant diseases. In addition, 1400 on-field images of a healthy rice leaf image dataset have been introduced in the paper for the detection of disease-free plants as well. Independent experiments were carried out with and without the inclusion of the healthy leaf image dataset. The performance of the proposed model is evaluated with the use of Stochastic Gradient Descent with Momentum (SGDM) and Adaptive Moment Estimation (Adam) optimization techniques using several performance matrices. Experimental results from the dataset for the classification of four rice plant diseases show that the model using SGDM optimization gives maximum accuracy of 99.66% and the model using the Adam optimization gives maximum accuracy of 99.83% on the test set in the 7th epoch respectively. However, the model with Adam optimizer performed better than the model with SGDM optimizer when the healthy leaf image dataset is included giving a maximum accuracy of 99.66% and 97.61% in the 7th epoch respectively. [ABSTRACT FROM AUTHOR]

With the increasing hazard to the environment and progressions in renewable energy technologies, hybrid renewable energy systems can meet the energy demand. Total dependability can be attained by adding battery banks to hybrid systems. This paper aims to optimally design a multi-source grid isolated hybrid generation system for future smart cities in the state of Tamil Nadu, India. This off-grid power generation consists of wind turbine generators, photovoltaic panels, inverters, diesel generators, and batteries. The design intents at minimizing Net Present Cost, Unmet Load, and CO2 emissions which are conventionally contradictory to each other. This paper analyzes the optimal off-grid combination of components and control strategies, utilizing improved Hybrid Optimization using the Genetic Algorithm. The results obtained portrays that a mix of hybrid renewable energy generators at off-grid locations without diesel generators can be a cost-effective choice of new smart cities, and it is sustainable, environmentally viable, and techno-economically feasible. Subvention is imperative to take on the large-scale system due to the high capital cost. [ABSTRACT FROM AUTHOR]

Objective: Due to the alarming rise in COVID-19 cases every day in India, numerous academics are already developing a variety of math-based estimation models to forecast the pandemic future course. This paper makes use of publicly available data to anticipate certain COVID-19 trajectories in India. Methods: We used the Auto-Regressive Integrated Moving Average Model, a time series model, to anticipate the number of COVID-19 infected cases every day soon. People need to consume nutritious food that is well-balanced and contains the right number of calories, nutrients, as well as vitamins for strong development, bearing in mind that sustaining and repairing bodily tissues is the goal while preventing unfavorable illnesses and disease. Results: Recent studies have shown that healthy eating can help lower the possibilities of developing cancer, cardiovascular disease and for COVID patients. Therefore, an optimization strategy and LINDO software was used to solve the model. This study offers an exhaustive daily meal plan created especially for hospital patients, acting as a helpful resource for school administrators. With six different food choices, the diet plan makes sure that you get all the nutrients you need each day at a reasonable price. Conclusions: The model is solved using LINDO software in the study, which shows how effective it is when compared to other heuristic techniques like biological algorithms. It is determined through thorough investigation that the chosen meals are both financially and nutritionally feasible to serve in ho spital settings. Hospital patients make up the study's participants, and each day's total cost comes to Rs109.34. This cost makes it possible to deliver meals that are minimally more costly but of higher quality, improving the patients' overall nutritional value. [ABSTRACT FROM AUTHOR]

The rainfall prediction is one of the major problems in meteorological department since it is related to the economy and human life. In addition, the heavy rainfall is the major problem of natural disasters like drought and flood. The accuracy of the rainfall forecasting has been spotted for countries like India and economy is greatly dependent by agriculture. This paper introduces a new rainfall prediction model consists of Feature extraction and Prediction. The features are extracted by modified Empirical Mean Curve Decomposition namely Empirical Mean Median Curve Decomposition, and then subjected to statistical analysis. The results of the statistical features are processed by the classification process. Neural network (NN) is used for the rainfall prediction. To improve the accuracy of proposed work, an optimal weight is and predicted result are multiplied, then, the weight in NN is optimally selected by a Rider Exploitation based Whale Optimization Algorithm. Finally, the performance of the proposed work is compared and proved over the state‐of‐the‐art models in terms of prediction rate. [ABSTRACT FROM AUTHOR]

This paper examines the application of surface drilling parameters and drilling optimization techniques, such as mechanical specific energy (MSE) and equivalent strength (EST), in detecting hydrate gas-bearing sediments during drilling operations. Gas hydrates are usually detected from 3D seismic imaging and later confirmed with Measurement-While-Drilling (MWD)/Logging-While-Drilling (LWD) data and collected core samples. Here, we describe an analysis of the time-based surface drilling parameters recorded from two wells drilled during the International Ocean Discovery Program (IODP) Expedition 372A offshore of New Zealand and the Indian National Gas Hydrate Program Expedition 02 (NGHP-02) offshore of India. The investigation revealed that drilling parameters, as well as MSE/EST methods, can and should be used to monitor and optimize the drilling process and to detect lithological/tectonic (fractures, fault zones, rock hardness, etc.) changes in the drilled substrata and signs of the dynamic changes in the downhole environment (tool vibration, washouts, pack-offs, etc.). However, surface drilling parameters with MSE models cannot explicitly determine the hydrate gas-bearing sediments. This qualitative analysis of whether the gas-bearing sediments consist of hydrates can only be accomplished with the use of the MWD/LWD suite, preferably located as close as possible to the bit. [ABSTRACT FROM AUTHOR]

This paper proposes a fuzzy interval optimization approach to solve the Environmental/ Economic Dispatch (EED) problem with uncertain parameters in the constraints and the objective functions. The objective functions considered are fuel cost and the gaseous emissions of the generating units. Two different types of fuel cost functions are considered in this study, namely, the conventional quadratic function and the augmented quadratic function to introduce more accurate modeling that incorporates the valve loading effects. The latter model presents non-differentiable and nonconvex regions that challenge most gradient-based optimization algorithms. In the proposed approach, objective functions are fuzzified and integrated to represent the fuzzy decision value. On the other hand, load uncertainties are modeled using fuzzy intervals. This fuzzy EED problem formulation provides a modeling flexibility, relaxation in constraints and allows the method to seek a practical solution. The obtained fuzzy multi-objective optimization problem is solved using non-dominated sorting genetic algorithm-II, known for its global searching capabilities, to get the best compromise among all the objectives. The performance of this solution is examined and applied to the standard IEEE 30-bus six-generator test system and the Indian power network of 82-bus by comparing its results with that of the existing methods. Different cases with different complexities have been considered in the study reported in this paper. [ABSTRACT FROM AUTHOR]

Direct injection diesel engine combustion system offers improvements in performance and fuel economy benefits. Four valves per cylinder, turbocharged and intercooled diesel engine became trustworthy for automobile application. Electronic diesel control, use of common rail with increase in injection pressures, and flexibility in injection control has changed the image of diesel engine. Evolutions in piston crown shape, intake ports with different swirl level helped to enhance mixing of air and fuel for better performance and emissions. Paper describes the work done on 4 cylinder diesel engine, upgraded to BS5 (Bharat stage 5) emission norms with 20% power increase. A systematic approach of engine development was fallowed. Engine performance prediction was done using AVL Boost software. Boost Model was validated with existing engine cylinder pressure. Combustion parameters have been varied to predict higher power. Vehicle model has been build using AVL cruise software and used to obtain steady-state load- speed points. Engine emission development has been done on engine test bench. Typical hardware like piston crown, turbocharger, exhaust gas re-circulation system with exhaust gas re-circulation cooler and various combustion parameters were tested and optimized. Suitable after-treatment system was selected and optimised for precious metal loading to reach Bharat stage 5 emissions. [ABSTRACT FROM AUTHOR]

Summary: Distributed energy resources (DERs) are a better option for fulfillment of load demand near the load centers. Optimal location and rating of DERs plays a vital role in the enhancement of distribution system performance in terms of power loss reduction, voltage profile enhancement, environmental friendliness, increasing reliability, and postponement of system upgrading. This paper addresses the Gbest‐guided artificial bee colony (GABC) optimization technique for the optimal integration of renewable energy sources (RESs) and electric vehicles (EVs) in distribution feeder to improve the power loss reduction, voltage level, cost reduction, and emissions reduction under load demand uncertainty. In this work, mathematical models of RESs, that is, wind turbine generation system (WTGS) and photovoltaic generation system (PVGS), are employed to determine the generated output power. The foremost aim of this research work is simultaneous abatement of total active power loss, entire cost, and emissions generation; therefore a multiobjective‐based fitness function is formed. Moreover, the impact of charging/discharging behavior of EVs on the distribution grid has also been considered in this study. The proposed methodology is applied on two test systems: (a) 28‐bus practical rural distribution network located at Kakdwip zone in the province of West Bengal, India, and (b) standard 69‐bus distribution feeders. Finally, from the achieved numerical outcomes, it is analyzed that, GABC algorithm generates better outcomes as compared to other optimization techniques. This indicates the viability and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper focuses on irrigation schemes under rotational water supply in arid and semiarid regions. It presents a methodology for developing plans for optimum allocation of land area and water, considering performance measures such as productivity, equity and adequacy. These irrigation schemes are characterized by limited water supply and heterogeneity in soils, crops, climate and water distribution network, etc. The methodology proposed in this paper, therefore, uses a previously developed simulation–optimization model (Area and Water Allocation Model, AWAM) that considers the heterogeneity of the irrigation scheme in the allocation process, and modifies this to take account of equity and adequacy of supply to irrigated areas. The AWAM model has four phases to be executed separately for each set of irrigation interval over the irrigation season: 1. generation of irrigation strategies for each crop–soil–region combination (CSR unit), 2. preparation of irrigation programmes for each irrigation strategy, 3. selection of specified number of irrigation programmes for each CSR unit and 4. optimum allocation of land area and water to different parts of the irrigation scheme (allocation units) for maximizing productivity. In the modified AWAM model, the adequacy is included at Phase-2 (by including only the irrigation programmes for full irrigation of each CSR unit) and equity is included at Phase-4 (by including the constraints for equity). The paper briefly discusses the applicability of the modified AWAM model for a case study of Nazare medium irrigation scheme in Southern India. The results of the case study indicated that the performance measures of productivity, equity and adequacy conflict with each other. [ABSTRACT FROM AUTHOR]

This paper presents optimal power flow management in real time for grid connected photovoltaic (PV)–battery system. The objective is the real time, dynamic and optimal scheduling of battery storage which reduces grid power consumption and achieves the peak load shaving. For this purpose, two strategies are developed; one is rule-based and other is optimization algorithm based. Two strategies are compared, pros and cons of those strategies are established. Though rule-based technique is simple & easy to implement, the optimization technique provides more optimal regulation of battery which provides the lesser electricity cost. Forecasted PV/load powers are used to get optimal value of battery power. In order to handle uncertainties due to forecasted data, fuzzy-based corrective strategy is developed to modify the reference battery power as per the present values of PV/load powers. The proposed analysis is carried out for over one exemplary day. The simulations are presented using typical load/PV data of India and Finland as a part of research collaboration. Experimental results are also carried out using proposed scheme. [ABSTRACT FROM AUTHOR]

The Markowitz portfolio optimization model, popularly known as the Mean-Variance model, assumes that stockreturns follow normal distribution. But when stock returns do not follow normal distribution, this model wouldbe inadequate as it would prescribe sub-optimal portfolios. Stock market literature often deliberates that stock returns are non-normal. In such context the Markowitz model would not be sufficient to estimate the portfolio risks. The purpose of this paper is to expand the original Markowitz portfolio theory (mean-variance) via adding the higher order moments like skewness (third moment about the mean) and kurtosis (fourth moment about the mean) in the return characteristics. The research paper investigates the impact of including higher moments using multi-objective programming model for portfolio stock selection and optimization. The empirical results indicate that the inclusion of higher moments had a considerable impact in estimating the returns behavior of portfolios. The portfolios optimized using all the four moments, generated higher returns for the given level of risk in comparison to the returns of the Markowitz model during the study period 2000-2011. The results of this study would be immensely useful to fund managers, portfolio managers and investors as it would help them in understanding the Indian stock market behavior better and also in selecting alternative portfolio selection models. [ABSTRACT FROM AUTHOR]

The Widening and strengthening of Existing National Highway Development In India has been executed by National Highway Authority of India, Ministry of Road Transport and Highways (MORT&H), Government of India. Existing road condition, traffic volume and composition to predict road deterioration as per the urban and rural road conditions was not so satisfactory in terms of IRI value. The Highway Development & Management (HDM) software simulates total life cycle conditions and costs for an analysis period under a user specified scenario of circumstances. A network economic evaluation is the most challenging use of the model, but the effort is well justified due to the potential savings on transportation costs achieved by comparing various alternatives and performing a budget constraint optimization. The methodology used by the author calls for HDM analysts to first identify the possible road works and then determine which road works to evaluate for each road class as a function of the traffic and other characteristics of the road class. For example, the works selected could be: (i) preventive treatment or surface treatment works for roads in good condition, (ii) surface treatment or asphalt concrete resurfacing works for roads in fair condition, and (iii) strengthening or reconstruction works for road in poor condition. It is important that the selected road works per road class be feasible from a purely technical point of view to produce realistic results.HDM allows for the definition of condition-responsive and scheduled project alternatives, for example, the maintenance schedule can be set up to execute an overlay when roughness reaches 4.0 IRI or execute an overlay every 15 years. [ABSTRACT FROM AUTHOR]

This paper presents the development of an operating policy model for a multi-reservoir system for hydropower generation by addressing forecast uncertainty along with inflow uncertainty. The stochastic optimization tool adopted is the Bayesian Stochastic Dynamic Programming (BSDP), which incorporates a Bayesian approach within the classical Stochastic Dynamic Programming (SDP) formulation. The BSDP model developed in this study considers, the storages of individual reservoirs at the beginning of period t, aggregate inflow to the system during period t and forecast for aggregate inflow to the system for the next time period t+1, as state variables. The randomness of the inflow is addressed through a posterior flow transition probability, and the uncertainty in flow forecasts is addressed through both the posterior flow transition probability and the predictive probability of forecasts. The system performance measure used in the BSDP model is the square of the deviation of the total power generated from the total firm power committed and the objective function is to minimize the expected value of the system performance measure. The model application is demonstrated through a case study of the Kalinadi Hydroelectric Project (KHEP) Stage I, in Karnataka state, India. [ABSTRACT FROM AUTHOR]

In this study, we focus on ripple effect mitigation capability of the Indian pharmaceutical distribution network during disruptions like COVID-19 pandemic. To study the mitigation capabilities, we conduct a multi-layer analysis (network, process, and control levels) using Bayesian network, mathematical optimisation, and discrete event simulation methodologies. This analysis revealed an associative relationship between ripple effect mitigation capabilities and network design characteristics of upstream supply chain entities. Using stochastic optimisation and Lagrangian relaxation, we then find ideal candidates for regional distribution centres at the downstream level. We then integrate these downstream locations with other supply chain entities for building the network optimisation and simulation model to analyse overall performance of the system. We demonstrate utility of our proposed methodology using a case study involving distribution of N95 masks to ‘Jan Aushadhi’ (peoples’ medicines) stores in India during COVID-19 pandemic. We find that supply chain reconfiguration improves service level to 95.7% and reduces order backlogs by 10.7%. We also find that regional distribution centres and backup supply sources provide overall flexibility and improve occupational health and safety. We further investigate alternate mitigation capabilities through fortification of suppliers’ workforce by vaccination. We offer recommendations for policymakers and managers and implications for academic research. [ABSTRACT FROM AUTHOR]

This paper emphasizes the usage of Retired Electric Vehicle Batteries (REVB) to support a system with a Centralized Charging Station (CCS) for charging electric vehicle batteries. This paper also focuses on power flow from the Grid to the Centralized Charging Station (G2CCS), power flow from solar PV to the Centralized Charging Station (PV2CCS), PV to the Grid (PV2G), and power flow from Retired Electric Vehicle Batteries to the Centralized Charging Station (REVB2CCS), Retired EV batteries to the Grid (REVB2G), various schemes for EV's in India, and barriers to the adoption of EV respectively. The review of the modelling and optimization is mainly based on retired EV batteries for EVs charging with recent trends, and future possibilities of retired EV batteries are also discussed. This review paper provides valuable references for researchers and assists them in continuous improvement in this field. [ABSTRACT FROM AUTHOR]

Electrical energy is one of the key components for the development and sustainability of any nation. India is a developing country and blessed with a huge amount of renewable energy resources still there are various remote areas where the grid supply is rarely available. As electrical energy is the basic requirement, therefore it must be taken up on priority to exploit the available renewable energy resources integrated with storage devices like fuel cells and batteries for power generation and help the planners in providing the energy-efficient and alternative solution. This solution will not only meet electricity demand but also helps reduce greenhouse gas emissions as a result the efficient, sustainable and eco-friendly solution can be achieved which would contribute a lot to the smart grid environment. In this paper, a modified grey wolf optimizer approach is utilized to develop a hybrid microgrid based on available renewable energy resources considering modern power grid interactions. The proposed approach would be able to provide a robust and efficient microgrid that utilizes solar photovoltaic technology and wind energy conversion system. This approach integrates renewable resources with the meta-heuristic optimization algorithm for optimal dispatch of energy in grid-connected hybrid microgrid system. The proposed approach is mainly aimed to provide the optimal sizing of renewable energy-based microgrids based on the load profile according to time of use. To validate the proposed approach, a comparative study is also conducted through a case study and shows a significant savings of 30.88% and 49.99% of the rolling cost in comparison with fuzzy logic and mixed integer linear programming-based energy management system respectively. • The article proposes a hybrid MG with renewable energy sources and energy storage systems (battery and fuel cell). • Develop a novel energy management system using modified grey wolf optimizer (MGWO) with multi objective functions. • Analyzes the load demand and the cost of electricity from the power grid for effective designing of MG at test locations. • A significant savings of 30.88% and 49.99% of the rolling cost in comparison with FL and MILP based EMS respectively. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Civil Engineering is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Several researchers have modified the original Social Force Model (SFM) for pedestrians by including vehicles and simulating interaction behaviors in shared space layouts. The present study is conducted at a signalized intersection where the performance and functionality are defined by spontaneous order. The SFM was modified, and two-wheelers were added along with cars and pedestrians to analyze their interactions. The Hybrid Calibration Method was adopted to calibrate the interaction parameters utilizing an optimization technique and real-time data from the study site. It can be concluded from the parameter values that the strength of interaction reduced as the range between the road agents increased. The findings can aid urban planners in visualizing the interaction behavior of pedestrians, cars, and two-wheelers. Moreover, the feasibility of introducing the shared space concept in India as an alternative to the conventional traffic planning system can also be determined during the designing phase. [ABSTRACT FROM AUTHOR]

In an Organization, mapping the competency of personnel with different level of expertise, skill set, and experience in professional fields is a tough, complex but essential task. In this work, we have considered an Engineering College with moderate number of faculties with different level of experience, expertise and research exposure. Here we have considered the load assignment to the faculties at the beginning of a semester as the competency mapping task. Each faculty having capabilities of teaching different subjects out of the total set of papers needs to take about two theory papers with or without laboratory component. The decisive factors for subject assignment may be depth of knowledge, sincerity, class management, contribution towards research, text book publication. Further preference of the faculty member should be considered with top priority unless there are some valid constraints. Again the teaching personnel in a department hold different designations and different administrative responsibility, therefore each of them cannot be assigned equal hours of teaching load. The All India Council for Technical Education (AICTE) guidelines is considered as a baseline for assignment of teaching load. The decisive factors are considered as objectives to be optimized and multi-objective particle Swarm optimization (MOPSO) is employed to perform the competency mapping task. The simulation results show the effectiveness of this approach. [ABSTRACT FROM AUTHOR]

In this paper, a recently developed stochastic programming technique that includes reliability constraints is used to solve the operations optimization problem of the Parambikulam-Aliyar project (PAP), a multireservoir system in India. The use of reliability constraints as chance constraints in reservoir operations optimization have been around for some time, but is still a challenging problem because either the results are not good or they cannot be applied in problems with more than one or two reservoirs when such techniques depend on discretization. The new implementation of chance constraints based on a previous model extended to multireservoir systems provides better results than so far known. This work is easy to apply because it requires only a standard nonlinear programming solver. [ABSTRACT FROM AUTHOR]

Purpose: Arsenic, a toxic metalloid in drinking water, has become a major threat for human beings and other organisms. In the present work, attempts have been made to remove arsenate from the synthetic as well as natural water of Ballia district, India by electrocoagulation method. Efforts have also been made to optimize the various parameters such as initial arsenate concentration, pH, applied voltage, processing time, and working temperature. Method: Electrocoagulation is a fast, inexpensive, selective, accurate, reproducible, and eco-friendly method for arsenate removal from groundwater. The present paper describes an electrocoagulation method for arsenate removal from groundwater using iron and zinc as anode and cathode, respectively. Results: The maximum removal of arsenate was 98.8% at 2.0 mg L, 7.0, 3.0 V, 10.0 min, and 30°C as arsenate concentration, pH, applied voltage, processing time, and working temperature, respectively. Relative standard deviation, coefficient of determination ( r), and confidence limits were varied from 1.50% to 1.59%, 0.9996% to 0.9998%, and 96.0% to 99.0%, respectively. The treated water was clear, colorless, and odorless without any secondary contamination. The developed and validated method was applied for arsenate removal of two samples of groundwater of Ballia district, U.P., India, having 0.563 to 0.805 mg L, arsenate concentrations. Conclusions: The reported method is capable for the removal of arsenate completely (100% removal) from groundwater of Ballia district. There was no change in the groundwater quality after the removal of arsenate. The treated water was safe for drinking, bathing, and recreation purposes. Therefore, this method may be the choice of arsenate removal from natural groundwater. [ABSTRACT FROM AUTHOR]

An application of Goal Programming (GP) methodology with its three approaches namely Min-Max Goal Programming (MMGP), Weighted Goal Programming (WGP) and PreemptiveGoal Programming (PGP) to a system of reservoir for optimal monthly operation has been presented in this paper. The objective of the present work is to find out an improved optimal operation model for Mahanadi Reservoir Project (MRP) system and to study the results in light of implicit fundamental philosophy associated with the models. The goal programming approach possesses significant advantage due to the fact that it may be based on physical operating criteria. The system goals and constraints are expressed deterministically. A constraint must be strictly satisfied, while for a goal it is desired to achieve the solution as close as possible to the specified target. The MMGP model is based on the philosophy of minimization of maximum range of deviation of all decision variables from their target value uniformly. WGP focus on the weights assigned to decision variables according to their relative importance. Whereas, PGP model deals sequentially with each goal according to their order of priorities. All the three GP models have been developed and applied to the MRP Complex which comprises of six multipurpose reservoirs with two inter basin linkage canals in the state of Chhattisgarh, India. The system goals and constraints are expressed deterministically for the application of model in perfect mode. The input data set was kept same to facilitate a justifiable comparison of performance of GP models. The strengths and limitations of all the three GP models have been analysed and the basic salient features of models in light of results obtained are discussed and presented. The best operating policy has been resulted from PGP model as compared to other two models. [ABSTRACT FROM AUTHOR]

In this paper the texture profile analysis for the cooked Bori nuggets (traditional Indian food) prepared from black gram batter having different initial moisture content of 62 to 72% (wb) and air incorporation of 14.14 to 18.12% (v/v) is discussed. The initial moisture content and the volume of air incorporation into the batter had significant effect at either 5% or 1% level of significance on the springiness, chewiness, hardness, cohesiveness and gumminess of the cooked nuggets. The optimum initial moisture content of 65.10 to 67.15% (wb) in the batter and air incorporation of 15.91 to 17.23% (v/v) in it are recommended in obtaining the maximum and minimum levels of the response quality parameters. The optimum values of these response parameters would be 0.453, 12.22, 60.31 N, 0.36 and 22.36 for springiness, chewiness, hardness, cohesiveness, and gumminess, respectively. [ABSTRACT FROM AUTHOR]

Implementing control structures such as gates at suitable locations in the urban drainage network enables its improved utility by actively controlling and optimizing the flow during a flood event. The successful operation of the control structure is fully governed by the suitability and efficiency of the control strategy adopted, which in turn depends on the response characteristics of the urban drainage system under different flow conditions. For an extreme rainfall event, it is necessary to model the transient flow in the urban drainage system. However, such an exercise is mostly restricted by the limited data and lack of in-depth survey of urban drainage systems in most of the developing countries like India. Taking this into consideration, an alternate approach has been presented in this paper for modeling the transient flowin the urban drainage system under limited data availability. The results obtained have also been compared with the finite difference model using four-point implicit scheme in time domain. The developed model can be used for the computation of temporal distribution of water surface profile with special consideration of lateral inflow. The application has been demonstrated by applying it to a representative catchment in Mumbai, India. [ABSTRACT FROM AUTHOR]

In this paper a metaheuristic technique called Ant Colony Optimization (ACO) is proposed to derive operating policies for a multi-purpose reservoir system. Most of the real world problems often involve non-linear optimization in their solution with high dimensionality and large number of equality and inequality constraints. Often the conventional techniques fail to yield global optimal solutions. The recently proposed evolutionary algorithms are also facing problems, while solving large-scale problems. In this study, it is intended to test the usefulness of ACO in solving such type of problems. To formulate the ACO model for reservoir operation, the problem is approached by considering a finite time series of inflows, classifying the reservoir volume into several class intervals, and determining the reservoir release for each period with respect to a predefined optimality criterion. The ACO technique is applied to a case study of Hirakud reservoir, which is a multi-purpose reservoir system located in India. The multiple objectives comprise of minimizing flood risks, minimizing irrigation deficits and maximizing hydropower production in that order of priority. The developed model is applied for monthly operation, and consists of two models viz., for short-time horizon operation and for long-time horizon operation. To evaluate the performance of ACO, the developed models are also solved using real coded Genetic Algorithm (GA). The results of the two models indicate that ACO model performs better, in terms of higher annual power production, while satisfying irrigation demands and flood control restrictions, compared to those obtained by GA. Finally it is found that ACO model outperforms GA model, especially in the case of long-time horizon reservoir operation. [ABSTRACT FROM AUTHOR]

This paper presents a Multi-objective Evolutionary Algorithm (MOEA) to derive a set of optimal operation policies for a multipurpose reservoir system. One of the main goals in multiobjective optimization is to find a set of well distributed optimal solutions along the Pareto front. Classical optimization methods often fail in attaining a good Pareto front. To overcome the drawbacks faced by the classical methods for Multi-objective Optimization Problems (MOOP), this study employs a population based search evolutionary algorithm namely Multi-objective Genetic Algorithm (MOGA) to generate a Pareto optimal set. The MOGA approach is applied to a realistic reservoir system, namely Bhadra Reservoir system, in India. The reservoir serves multiple purposes irrigation, hydropower generation and downstream water quality requirements. The results obtained using the proposed evolutionary algorithm is able to offer many alternative policies for the reservoir operator, giving flexibility to choose the best out of them. This study demonstrates the usefulness of MOGA for a real life multi-objective optimization problem. [ABSTRACT FROM AUTHOR]

This paper presents a linear programming model for optimal water allocations in a large river basin system. The model is applied to the transboundary Subernarekha River in India, which has two main stream reservoirs, two barrages, and three small command area reservoirs. The Tripartite Agreement (TPA) among the cobasin riparian states for sharing of its waters is analyzed. The main objective is to find the maximum annual benefits from irrigation and hydropower subject to various constraints on the system. Design constraints as per the TPA relating to the water shares among riparian states (i.e., Bihar, Orissa, and West Bengal) at individual dams and barrages are also considered. Optimal water allocations are obtained based on the 75% water year dependable flow as specified in the TPA. Water shortage and water surplus years have also been considered to determine optical water allocations during the dry and wet years, respectively. The stream reservoirs Chandil and Icha would fail to meet the water demands during the low flow years, whereas the Kharkai and Galudih barrages would essentially satisfy water demands according to the TPA provisions. Results indicate that Bihar is likely to benefit most from the TPA. [ABSTRACT FROM AUTHOR]

An application of the min-max goal programming methodology to a system of multipurpose reservoirs for optimal monthly operation has been presented in this paper. The goal programming approach possesses significant advantages because of the fact that it may be based on physical operating criteria. The system goals and constraints are expressed deterministically. A constraint must be strictly satisfied, while for a goal it is desired to achieve the solution, which is as close as possible to the specified target. The min-max goal programming model is developed and applied to the Mahanadi Reservoir Project (MRP) Complex comprising of six multipurpose reservoirs in the state of Madhya Pradesh, India. The MRP Complex operations resulting from the use of the min-max goal programming model are compared to the operations resulting from three other reported optimization models with the same data set for the same operation period. The set of operations resulting from various models are comparable in their effectiveness, and in most aspects the min-max goal programming model operations are better.Key words: optimization, reservoir operation, goal programming, min-max goal programming.L'application d'une méthodologie de programmation pour la minimisation-maximisation d'objectifs à un système de réservoirs à usages multiples pour une opération mensuelle optimale est présentée dans cet article. L'approche de programmation d'objectifs possède un avantage significatif dû au fait qu'elle peut être basée sur des critères d'opération physiques. Les objectifs et contraintes du système sont exprimés de façon déterministe. Une contrainte doit être strictement satisfaite, mais pour un objectif, il est désiré d'atteindre la solution la plus proche possible de la cible spécifiée. Le modèle de programmation pour la minimisation-maximisation d'objectifs est développé et appliqué au complexe du projet de reservoirs de Mahanadi (« Mahanadi Reservoir Project (MRP) »), qui comprend six réservoirs à usages multiples dans l'état de Madhya Pradesh, en Inde. Les opérations du complexe MRP, provenant de l'utilisation du modèle de programmation pour la minimisation-maximisation d'objectifs, sont comparées aux opérations déterminées à partir de trois modèles d'optimisation connus, avec les mêmes séquences de données, et pour la même période d'opération. Les séquences d'opérations provenant des différents modèles sont équivalentes en terme d'efficacité, et pour la majorité des aspects, les opérations obtenues par le modèle de programmation pour la minimisation-maximisation d'objectifs sont meilleures.Mots clés : optimisation, opération de réservoir, programmation d'objectifs, programmation pour la minimisation-maximisation d'objectifs.[Traduit par la Rédaction] [ABSTRACT FROM AUTHOR]

Ensemble forecasts from multiple models have gained enormous popularity as it provides a more efficient forecast as compared to the individual counterpart. The linear weighted combination method is most widely utilized for its simplicity and efficiency. Despite hard efforts by various researchers, two considerable challenges still exist: (1) Systematic and robust techniques for selecting suitable forecasting models and (2) Techniques to find appropriate combination weights. To address these challenges a novel framework for optimization-based ensemble technique, 'OptiSembleForecasting' has been proposed in this study. The three components of the proposed framework are (a) Principal Component Analysis-based error index, (b) Model Confidence Set algorithm and (c) Optimization techniques. A total of thirteen forecasting models consisting of five deep learning, five machine learning and three stochastic models and twenty optimization techniques have been implemented in the proposed framework. To examine the effectiveness of the proposed technique, wholesale price of three commodities (TOP: Tomato, Onion, and Potato) each with two major markets in India has been considered. The empirical evaluation of the predictive accuracy of different models with that of the proposed techniques has been carried out by means of root mean square error and mean absolute percentage error. The findings of this study demonstrated the superiority of the proposed algorithm. Moreover, a R-package, namely 'OptiSembleForecasting', has been developed to make the implementation of this technique simple and user-friendly. [ABSTRACT FROM AUTHOR]

India's expanding population has necessitated the development of alternate transportation methods with electric vehicles (EVs) being the most indigenous and need for the current scenario. The major hindrance is the undue influence on the power distribution system caused by incorrect charging station setup. Renewable Energy Sources (RES) have a lower environmental impact than the non-renewable sources of energy and due to which Plug-in Hybrid Electric Vehicles (PHEV) charging stations are installed in the highest-ranking buses to facilitate their effective placements. Based on meta-heuristic optimization, this study offers an effective PHEV charging stations allocation approach for RES applications. The primary objective of the developed system is to create a charging network at a reasonable cost while maintaining the operational features of the distribution network. These troublesare handled by applying meta-heuristic algorithms and optimum planning based on renewable energy systems to satisfy the outcomes of the variables. As a result, by adding charging station parameters, this research proposes to conceptualize the distribution of optimal charging stationsas multiple-objectives of the problem. Furthermore, the PHEV RES and charging station location problem is handled in this study by deploying a novel hybrid algorithm termed as Atom Search Woven Aquila Optimization Algorithm (AT-AQ) that includes the ideas of both Aquila Optimizer (AO) and Atom Search Optimization (ASO) Algorithms. In reality, Aquila Optimizer is a unique population-based optimization approach energized by Aquila's behaviour when seeking prey and it solves the problems of slow convergence and local optimum trapping. According to the findings of the experiments, the proposed model outperformed the other methods in terms of minimized cost function. [ABSTRACT FROM AUTHOR]

Oral cancer is a frequent and challenging cancer that has a high fatality rate. It is the fifth most common cancer in India, with 130,000 deaths each year. There are various diagnostic techniques for oral cancer, but they are limited to accurately detecting cancer cells. As a result, deep transfer learning algorithms such as ResNet50, MobileNetV2, VGG19, VGG16, and DenseNet were applied to non-malignant and oral cancerous photographs acquired from histopathologic and real-time datasets in this study. The images were first preprocessed with techniques like Gaussian blur to eliminate noise signals. Then, features were extracted with morphological operations to produce extreme points on the images, subsequently cropped. The retrieved pictures were fed into deep transfer learning models along with optimization techniques as ADAM, SGD, and RMSprop optimizers to obtain optimal results. Accuracy, loss, RMSE, precision, and AUC were used to analyze the findings. During the validation phase, the hybrid optimization techniques using ADAM, RMSprop, and SGD on DenseNet produced effective results, with accuracy of 92.41%, loss of 0.70, and RMSE of 0.29 for the real-time dataset, accuracy of 95.41%, loss of 0.10, and RMSE of 0.03 for oral cancer images, and accuracy of 92.41%, loss of 0.30, and RMSE of 0.09 for non-cancerous images taken from the histopathologic dataset. [ABSTRACT FROM AUTHOR]

Abstract: Bhutan sells electricity across the border to India. However large sections of its population do not have access to electricity. The harsh mountainous terrain coupled with sparse and dispersed settlements in Bhutan makes grid extension difficult, costly and economically unviable. Therefore, it is worth looking at renewable energy options to meet electricity demand in remote locations. This study aims to identify the least-cost technologies that could be used in the rural areas of Bhutan. The study analyses the energy needs of rural households, the resources available, and current policies and programs on rural electrification. The Hybrid Optimization Model HOMER for designing distributed generation (DG) systems both on and off-grid was used to find the least-cost technologies for different potential resources and to compare the cost of deploying these technologies with conventional electrification through grid extension. This paper identifies possible rural electrification options for Bhutan and compares them with extending the grid to the remote areas. [Copyright &y& Elsevier]

Abstract: In this paper, the parameter-wise optimization training process is implemented to achieve an optimal configuration of focused time lagged recurrent neural network (FTLRNN) models by embedding the gamma, laguarre, and multi-channel tapped delay line memory structure. The aim is to examine the prediction ability of the proposed models in order to predict one-day-ahead electric power load simultaneously as usual to oppose 1–24h forecast in sequel with a special emphasis on seasonal changes over a year. An improved delta-bar-delta algorithm is used to accelerate the training of neural networks and to improve the stability of the convergence. Experimental results indicate that the FTLRNN with time delay neural network (TDNN) clearly outperformed the gamma and laguarre based short-term memory structure in various performance metrics such as mean square error (MSE), normalized MSE, correlation coefficient (r) and mean absolute percentage error (MAPE) during evaluation process. Empirical results show that the proposed dynamic NN model consistently performs well on daily, weekly, and monthly average basis in terms of prediction accuracy. It is noticed from the literature review that an optimally configured FTLRNN with multi-channel tapped delay line memory structure is not currently available to solve short-term electrical power load prediction. The proposed method gives acceptable errors in all seasons, months and on daily basis. The average prediction error on three weeks is obtained as low as 1.67%. [ABSTRACT FROM AUTHOR]

Summary: Nowadays the whole world is striving to achieve energy security and sustainable development through sustainable utilization of cleaner energy resources. But due to the rapidly rising population caused by the unprecedented urban development, it has created imbalances of energy supply and demand. Hybrid energy systems have emerged as a promising source to augment existing energy infrastructure for sustainable energy supply and to reduce the impact of challenges posed by the energy scarcities. There have been continuous requests from the urban sector for additional energy resources, but the energy generation involves a complex phenomenon. In India also, there are abundances of rising energy demands from various sectors over the regions, and these demands cannot be fulfilled further due to the limitations of the energy generation grids. As a result, an approach is required to offer additional energy resources beyond the use of diesel generator (which increases environmental pollution). Recently due advancement in hybrid energy systems consisting of non‐renewable & renewable or renewable & renewable or non‐renewable & non‐renewable, it provides a scope to understand the various configuration of the hybrid energy system for past trends, present status and future scenarios. The investment in energy technologies is critical to be evaluated with techno‐economic feasibility analysis for resource efficiency optimization. Hence, the main objective of this work is to propose an optimal system configuration for urban residential standalone hybrid energy systems to fulfil the electrical energy requirement, which is technically and economically reliable & feasible. The current work accounts for scaling up the existing energy system with proper implementation. The simulations results show that expected average energy requirements of 5 kWh/day with a peak of 818 W for a typical household can be met with proposed configuration (with components like solar PV, wind turbine, power generator [diesel] and battery backup system). The data simulation and optimization technique for a monthly profile of wind, solar, and excess energy generation over HOMER simulation toolkit offer the potential to generate excess electricity of 19.3%. Techno‐economic assessment of the system shows that the proposed system offers more economical and performance benefits. This study also articulates the need to propose a better energy system structure with basic energy grid extension as a cost‐effective solution for sustainable development. Similar kind of assessment will offer a useful decision support system for tropical countries to guide future researchers in energy studies. [ABSTRACT FROM AUTHOR]

A grid connected renewable energy based hybrid microgrid, incorporating solar PV, biomass generators, and Gravity Energy storage system (GESS) has been proposed in this article. This is done to provide uninterrupted energy to a hilly village of North‐East India. A bi‐directional energy transaction with the central grid is considered for handling uncertainties and to gain monetary benefit through energy sale. The GESS is only charged through solar PV and biomass generators, to reduce the carbon footprint. The outward energy transaction with the central grid helps in reducing the energy cost for consumers inside the microgrid. Transaction price of energy from the central grid is considered from historical data available in the website of Indian Energy Exchange Limited (IEX). For different combinations of loading pattern and GESS initial storage, the Levelized cost of energy (LCOE) is found to vary in between INR 2.71/kWh to INR 3.41/kWh, which is lower than the current Nagaland state government approved tariff of INR 3.55/kWh. Any subsidy from governmental or non‐governmental organizations has not been considered in the present study while calculating the installation cost of Solar PV and Biomass generators to portray the actual scenario. Whale optimization algorithm (WOA) has been used to find the optimal solution of the proposed system. [ABSTRACT FROM AUTHOR]

In this article, a new optimization technique known as Kho-Kho optimization (KKO) algorithm is presented. This proposed technique is a population based meta-heuristic method which is inspired from the strategies used by players in a well known tag-team game played in India, i.e. Kho-Kho. The performance and superiority of the proposed method with respect to other existing methods is evaluated using twenty nine benchmark functions and real-time optimization problems related to power system i.e. combined emission economic dispatch and combined heat and power economic dispatch problem. • This paper presents a new Kho-Kho optimization algorithm (KKO). • KKO is inspired by a well-known tag-team game played in India known as Kho-Kho. • KKO mimics the strategies used by a chasing team to catch the runners of a running team. • Twenty-nine benchmark functions are used to test the performance of the proposed algorithm. • Further, KKO is used to solve real-time optimization problems of power system. [ABSTRACT FROM AUTHOR]

• Objective analysis of psoriasis disease over a larger dataset of 780 images. • Implementation of swarm intelligence algorithms to obtain optimum clusters. • Effective psoriasis lesion detection using four swarm intelligence techniques. • Quantitative analysis of the result using different metrics. • Calculation of computational complexity for each swarm intelligence algorithms. In psoriasis skin disease, psoriatic cells develop rapidly than the normal healthy cells. This speedy growth causes accumulation of dead skin cells on the skin's surface, resulting in thick patches of red, dry, and itchy skin. This patches or psoriatic skin legions may exhibit similar characteristics as healthy skin, which makes lesion detection more challenging. However, for accurate disease diagnosis and severity detection, lesion segmentation has prime importance. In that context, our group had previously performed psoriasis lesion segmentation using the conventional clustering algorithm. However, it suffers from the constraint of falling into the local sub-optimal centroids of the clusters. The main objective of this paper is to implement an optimal lesion segmentation technique with aims at global convergence by reducing the probability of trapping into the local optima. This has been achieved by integrating swarm intelligence based algorithms with conventional K-means and Fuzzy C-means (FCMs) clustering algorithms. There are a total of eight different suitable combinations of conventional clustering (i.e., K-means and Fuzzy C-means (FCMs)) and four swarm intelligence (SI) techniques (i.e., seeker optimization (SO), artificial bee colony (ABC), ant colony optimization (ACO) and particle swarm optimization (PSO)) have been implemented in this study. The experiments are performed on the dataset of 780 psoriasis images from 74 patients collected at Psoriasis Clinic and Research Centre, Psoriatreat, Pune, Maharashtra, India. In this study, we are employing swarm intelligence optimization techniques in combination with the conventional clustering algorithms to increase the probability of convergence to the optimal global solution and hence improved clustering and detection. The performance has been quantified in terms of four indices, namely accuracy (A), sensitivity (SN), specificity (SP), and Jaccard index (JI). Among the eight different combinations of clustering and optimization techniques considered in this study, FCM + SO outperformed with mean JI = 0.83, mean A = 90.89, mean SN = 92.84, and mean SP = 88.27. FCM + SO found statistical significant than other approaches with 96.67 % of the reliability index. The results obtained reflect the superiority of the proposed techniques over conventional clustering techniques. Hence our research development will lead to an objective analysis for automatic, accurate, and quick diagnosis of psoriasis. [ABSTRACT FROM AUTHOR]

The authors discuss their development of JalTantra, a web system that aids government engineers in India in designing piped water systems that provide an adequate quality of service to meet citizens' needs for drinking water at a cost below that mandated by strict government norms. Government bodies responsible for drinking water distribution in India face the challenging task of designing schemes that provide a quality of service that is adequate to meet the needs of citizens at a cost below the strict government norms. Engineers at these government bodies must undertake the design process using tools that are not optimal and consider only pipe diameter selection, which is only one component of the entire scheme design. As such, much of the design process is undertaken in an ad hoc and heuristic manner, relying on the experience and intuition of the engineers. We developed JalTantra, a web system that aids these government engineers in sizing both pipe diameters and the various other water network components, such as tanks, pumps, and valves. We use an integer linear program model, which allows us to solve the problem optimally and quickly. [ABSTRACT FROM AUTHOR]

The Widening and strengthening of Existing National Highway Development In India has been executed by National Highway Authority of India, Ministry of Road Transport and Highways (MORT&H), Government of India. Existing road condition, traffic volume and composition to predict road deterioration as per the urban and rural road conditions was not so satisfactory in terms of IRI value. The Highway Development & Management (HDM) software simulates total life cycle conditions and costs for an analysis period under a user specified scenario of circumstances. A network economic evaluation is the most challenging use of the model, but the effort is well justified due to the potential savings on transportation costs achieved by comparing various alternatives and performing a budget constraint optimization. The methodology used by the author calls for HDM analysts to first identify the possible road works and then determine which road works to evaluate for each road class as a function of the traffic and other characteristics of the road class. For example, the works selected could be: (i) preventive treatment or surface treatment works for roads in good condition, (ii) surface treatment or asphalt concrete resurfacing works for roads in fair condition, and (iii) strengthening or reconstruction works for road in poor condition. It is important that the selected road works per road class be feasible from a purely technical point of view to produce realistic results.HDM allows for the definition of condition-responsive and scheduled project alternatives, for example, the maintenance schedule can be set up to execute an overlay when roughness reaches 4.0 IRI or execute an overlay every 15 years. [ABSTRACT FROM AUTHOR]

Chhana cake, locally termed chhana podo, is a baked traditional dairy product of India. The present study was undertaken for optimization of process parameters pertaining to production of chhana podo. Independent variables, namely, moisture content of feed-mix: 52.5 - 62.5% (wb), baking temperature: 60 - 180°C, baking time: 1 - 9 h and height of feed-mix: 1 - 5 cm were selected heuristically and their effect on dependent variables, namely, hardness, whiteness index, yellowness index, tint of crust and crumb, moisture content and expansion ratio of chhana podo were studied. Although quadratic models fitted to responses exhibited relative deviation percent (Rd) ranging from 1.214 to 5.406%; lack of fit was significant for all responses except crust yellowness index and crust tint. Neural network modeling was adopted (Rd for training = 1.739%, Rd for validation = 1.845%) and relative importance of factors on responses were found. Optimum conditions obtained from genetic algorithm were: moisture content of feed-mix = 57.43% (wb), baking temperature = 151.4°C, baking time = 4.35 h, height of mix = 2.9 cm. [ABSTRACT FROM AUTHOR]

India is fast realizing its need to reduce its dependence on carbon intensive, nonrenewable energy sources and shift its focus to making greater use of renewable energy sources such as solar power. Jawaharlal Nehru National Solar Mission (JNNSM) is one of the several programs launched by Government of India to capitalize on country's enormous solar potential. One of the aims of JNNSM includes construction of concentrated solar power (CSP) demonstration projects, particularly in the north-western region which is blessed with high direct normal irradiation (DNI). In this study, system advisor model (SAM) has been used to develop technical performance and energy generation cost estimates for different parabolic trough designs. The different plant designs include two solar-only plants (differing in solar multiples) without storage or hybridization, one solar-only plant with storage capacity of 4 h, and one solar trough plant integrated with natural gas hybridization. Trough plant steam cycle performance is modeled in SAM using Jodhpur, Rajasthan as the reference site. This analysis aims to provide vital technical criteria and levelized energy cost estimates that can prove instrumental for the design of CSP demonstration plants in Rajasthan. © 2012 American Institute of Chemical Engineers Environ Prog, 32: 1247-1254, 2013 [ABSTRACT FROM AUTHOR]

Sourcing strategy design in a supply chain is vital to gain competitive advantage. In recent years, supply chain risks are growing significantly and supplier failure is identified as one of the top supply chain risks. Researchers attempt to mitigate the negative impacts of supplier failure by applying strategies such as local versus global sourcing, single versus dual/multiple-sourcing, performance-based supply contracts, and optimizing the order allocation among suppliers. Global sourcing is a widely recognized strategy among firms, and it involves a trade-off between reliable, high-cost local suppliers and unreliable, low-cost offshore suppliers. The global sourcing is associated with the risks of exchange rate volatility, trade restrictions, longer lead time, and problems with supplier reliability. Sourcing strategy design considering price, exchange rate risks, and supplier delivery reliability is an important research topic and needs attention. In this work, a hybrid optimization and simulation approach is proposed to design the supply chain sourcing strategy. In the optimization approach, a multi-objective binary particle swarm algorithm is developed for minimizing the total cost and maximizing the supplier delivery reliability. Selected scenarios from the optimization results are modeled using Witness simulation software to evaluate the robustness of sourcing strategies under price, exchange rate and demand risks. The proposed approach is exemplified using a real-life case study of a plastic product manufacture in India. [ABSTRACT FROM AUTHOR]

Polymer-based composite materials posses superior properties such as high strength-to-weight ratio, stiffness-to-weight ratio, and good corrosive resistance and therefore, are preferred for high-performance applications such as in the aerospace, defense, and sport goods industries. Drilling is one of the indispensable methods for building products with composite panels. In this present work, an investigation has been carried out to evaluate mechanical and machinability characteristics of hybrid composites, E-glass, and natural coir fiber abundantly available in India. A regression model is developed for correlating the interactions of some drilling parameters such as drill bit diameter, spindle speed and feed rate, and their effects on responses such as thrust force, torque, and tool wear during drilling of glass-coir fiber reinforced hybrid composites by Design of Experiment techniques. Results indicated that feed rate is playing major role on the responses than other two variables. [ABSTRACT FROM AUTHOR]

The optimal design of a groundwater remediation system using the pump-and-treat method is a complex task involving modeling physical phenomena such as groundwater flow and contaminant transport and optimizing several goals of concern, while satisfying bounds on certain parameters. Simulation tools such as the finite-element method (FEM) coupled with optimization tools such as the genetic algorithm (GA) has been found to be an efficient and easy to use methodology to solve such arduous problems. In this study, a simulation model using the FEM for groundwater flow and contaminant transport has been developed and coupled with a multiobjective optimization model based on the nondominated sorting genetic algorithm II (NSGA II). The model is used to minimize the cost optimization function as well as the time period for the remediation of the aquifer subject to bounds on pumping rates, groundwater heads, and concentration levels of the contaminant. The coupled FEM-NSGA II model has been applied for the remediation of a real field aquifer near Vadodara, India for a combination of flushing and pumping to demonstrate the effectiveness of the proposed multiobjective technique for achieving the optimal pumping policy. Total dissolved solids are considered to be the main pollutant. Three alternative remediation design scenarios with different pumping well locations have been compared and the best remediation design scenario has been identified to be the one that gives the best Pareto-optimal front. The FEM-NSGA II in general evolves a set of well-spread and consistent Pareto-optimal solutions, which represent the best designs that identify the trade-off between the expected cost and the time period of remediation for all the remediation design scenarios while satisfying the constraints, thus, giving the decision maker a wide set of choices. [ABSTRACT FROM AUTHOR]

A set of nested models was applied to provide useful strategy to evaluate the storage, water yield and the operational performance of the multipurpose Mula reservoir in India. Insufficient yield from the reservoir for the purpose of water supply and irrigation has led to the need for reevaluation. These nested models were applied in tandem using linear programming (LP), dynamic programming (DP), artificial neural networks (ANN), hedging rules (HRs), and simulation. An LP-based yield model (YM) has been used to reevaluate the annual yields available from the reservoir for water supply and irrigation. The yields obtained from the YM have been refined by two DP models, viz; the controlled output DP (CODP) and the controlled inventory DP (CIDP). The prespecified annual release reliabilities and the yield deficits were similar to that used in the YM. In this approach, the ANN models use a hybrid model in the stochastic generation of monthly inflows to the reservoir for studying its operational performance. With the ANN, both the sigmoidal (DPN) and the radial basis function network (RBN) evaluated reservoir performance using results of the DP model. Continuous HR (CHR) and discrete HR (DHR) were also used for modeling the reservoir performance. The results show that the YM estimates the annual multiyields accurately. The data generated by the hybrid models preserve the characteristics of the historical data. The RBN is better than the DPN models. In the CHR, the sets of hedging trigger obtained from the YM gave an acceptable performance. The DHR reduces the number of reservoir empty conditions and hazards of droughts. The study reveals that the project may fail to fulfill the irrigation demands. [ABSTRACT FROM AUTHOR]