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Optimal procurement and pricing policy for deteriorating items with price and time dependent seasonal demand and permissible delay in payment
In practice, items like food, nursery plants, medicines, etc. are seasonal and deteriorating in nature. For this type of products, permissible delay in payment is a common business policy, which is used to increase in the sell volume and to develop trust in buyer-seller relationship. In this paper, we developed an inventory model for time dependent deteriorating seasonal items with the permission of delay in payment. Shortages are permitted and partially back ordered. Our aim is to find optimal selling price and ordering quantity simultaneously. Concavity of profit function with respect to decision variables has been discussed analytically. A solution procedure followed by a numerical example and sensitivity analysis along with managerial insights are provided. Numerical analysis predicts that delay in payment profit policy is a better decision in order to maximise the profit or in order to get more profit. 2022 Inderscience Enterprises Ltd. -
Optimal procurement policy for growing items under permissible delay in payment
In the last decade, growing item industries have shown an increasing trend in production and it is expected that such industries will maintain this increasing pace in the future. Existing challenges of these industries, like mortality in the production phase and deterioration in the consumption phase, make procurement decisions more complex. In this article, we established an inventory model with mortality, deterioration, and price-dependent demand. To increase the sales volume and profit, a delay in payment policy is considered. A numerical example is presented to explain the solution procedure. The concavity of the profit function is discussed analytically for decision variables. It has been observed through sensitivity analysis that selling price is the most sensitive among decision variables and parameters. 2024 Inderscience Enterprises Ltd. -
Optimal Reactive Power Compensation in Indian Urban Electrical Distribution Networks Using Hybrid Starfish Optimization Algorithm
This paper presents an efficient hybrid optimization approach for optimal reactive power compensation (ORPC) problem in electrical distribution networks (EDNs) using a Hybrid Starfish Optimization Algorithm (HSFOA). A Voltage Stability Index (VSI) is integrated to identify critical buses and narrow the search space, improving solution quality and convergence efficiency. The proposed method determines the optimal locations and sizes of capacitor banks (CBs) and Distribution static synchronous compensators (DSTATCOMs) to minimize real power losses and enhance voltage stability. The effectiveness of the HSFOA is evaluated first on the IEEE 33-bus benchmark system. The results demonstrate that the proposed approach provides superior improvements compared to conventional techniques. Later, the approach is implemented on 106-bus and 85-bus real-time Indian urban distribution networks. For the 106-bus system, losses decrease from 644.768 kW (base case) to 495.273 kW with CBs and to 487.933 kW with DSTATCOMs, corresponding to 23.25% and 24.32% reductions. In the 85-bus system, real power losses are reduced by 34.56% with CBs and 34.44% with DSTATCOMs, while the VSI improves by 15.05% and 20.70%, respectively. Similar improvements were recorded for the IEEE 33-bus system. Overall, the findings confirm that HSFOA offers a robust and effective solution for optimal reactive power planning and enhanced operational performance in modern EDNs. This article is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. https://creativecommons.org/licenses/by-sa/4.0/ -
Optimal setting of arc welding robot and laser sensor variables for getting maximal weld quality, positional accuracy, and smooth trajectory
Abstract: For seam-finding applications, a robotic welding system and laser sensor can be coupled to achieve improved repeatability and shorter cycle times. This manuscript investigates the impact of several robot variables, including robot orientation, robot travel speed, and focal length of the laser sensor, on three key factors: positioning error, associated joint jerk-torque rate, and weld quality. An Enhanced Multi-Objective NSGA-II (EMONSGA-II) is proposed, which combines NSGA-II with Nelder Mead local search to find the best values for robot and sensor variables. The goal is to acquire the lowest values for joint jerk-torque rate, positional error, and maximum weld quality metrics. The maximized weld quality is represented by maximized ultimate strength, yield strength, and penetration of weld joint, as minimized weld bead height and width. Fuzzy logic has been used to transform the multi-performance weld characteristics into one term of the weld quality. The experiments have been performed using the Arc 50 series welding system with AccuFast point laser sensor integrated MOTOMAN MA 1440 arc welding robot system. Finally, the optimal setting of the robot and sensor parameters have been validated through experimentation to observe the weld quality and positional accuracy. The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2025. -
Optimal Shortest Path Routing over Wireless Sensor Networks Using Constrained Genetic Firefly Optimization Algorithm
In Wireless Sensor Networks (WSNs), a large number of sensor nodes are placed over a specific area in any real-life application. The sensor node is small, with limited battery life, memory, and computing capacity. Due to the limited power of the battery, WSNs must expand the system life by minimizing the energy usage. In the existing system, the methods have limitations related to optimal shortest routing path, node energy consumption, network reconfiguration, and so on. In order to overcome these issues, aConstrained Genetic FireFly Optimization Algorithm (CGFFOA) is proposed. The CGFFOA algorithm is designed to select the best shortest path routing through the selection of Cluster Head (CH) nodes based on the better energy utilization, delay, and high throughput sensor nodes. It is used to optimize the routing path based on the energy, hop count, inter and intra cluster delay, and lifetime. The simulation findings therefore conclude that, with regard to reduced energy consumption, higher throughput, and lower end-to-end delay, the proposed CGFFOA algorithm is preferable to existing methods such as Particle Swarm Optimization (PSO) and Dynamic Source Routing (DSR). 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Optimal Siting of Capacitors in Distribution Grids Considering Electric Vehicle Load Growth Using Improved Flower Pollination Algorithm
The optimal VAr compensation using capacitor banks (CBs) in radial distribution networks (RDNs) is solved in this paper while taking the growth of the load from electric vehicles (EVs) into consideration. This is accomplished by adapting an improved variant of the flower pollination algorithm (IFPA) with an enhanced local search capability. The primary objective of determining the locations and sizes of CBs is to minimize the distribution losses in the operation and control of RDNs. Additionally, the effect of CBs is shown by the increased net savings, greater voltage stability, and improved voltage profile. A voltage stability index (VSI) was used in the optimization process to determine the predefined search space for CB locations, and a double-direction learning strategy (DLS) was then considered to optimize the locations and sizes while maintaining a balance between the exploration and exploitation phases. Three IEEE RDNs were used to simulate various EV load increase scenarios as well as typical loading situations. According to a comparison with the literature, the IPFA produced global optimum results, and the proposed CBs allocation approach demonstrated enhanced performance in RDNs under all scenarios of EV load growth. 2022, University of Kragujevac, Faculty of Science. All Rights Reserved. -
Optimal Sizing and Placement of Distributed Generation in Eastern Grid of Bhutan Using Genetic Algorithm
Power system has to be stable and reliable for its users. Nevertheless, due to the aging and ignorance, it tends to be unreliable and unstable. Distributed Generation (DG) is a small-scale energy production which are usually connected towards the load. It helps in the reduction of power losses and improvement of profile of voltage in a distribution network. However, if a DG is not optimally placed and sized, it will rather lead to an increase in a power loss and also deteriorates the voltage profile. This report exhibits the importance of DG placement and sizing in a distribution network using Genetic Algorithm (GA). Apart from the optimum DG placement and sizing, different scenarios with numbers of DGs is also being analyzed in this report. On eastern grid of Bhutan, a detailed analysis for its performance is carried out through MATLAB platform to demonstrate and study the effectiveness and reliability of a methodology that is being proposed. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Optimal Stacked Sparse Autoencoder Based Traffic Flow Prediction in Intelligent Transportation Systems
Recently, intelligent transportations system (ITS) has gained significant internet due to the higher needs for road safety and competence in interconnected road network. As a vital portion of the ITS, traffic flow prediction (TFP) is offer support in several dimensions like routing, traffic congestion, and so on. To accomplish effective TFP outcomes, several predictive approaches have been devised namely statistics, machine learning (ML), and deep learning (DL). This study designs an optimal stacked sparse autoencoder based traffic flow prediction (OSSAE-TFP) model for ITS. The goal of the OSSAE-TFP technique is to determine the level of traffic flow in ITS. In addition, the presented OSSAE-TFP technique involves the traffic and weather data for TFP. Moreover, the SSAE based prediction model is designed for forecasting the traffic flow and the optimal hyperparameters of the SSAE model can be adjusted by the use of water wave optimization (WWO) technique. To showcase the enhanced predictive outcome of the OSSAE-TFP technique, a wide range of simulations was carried out on benchmark datasets and the results portrayed the supremacy of the OSSAE-TFP technique over the recent state of art methods. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Optimal Switching Operations of Soft Open Points in Active Distribution Network for Handling Variable Penetration of Photovoltaic and Electric Vehicles Using Artificial Rabbits Optimization
Global warming, rising fuel prices, and limited conventional fuel supplies are driving the use of renewable energy, battery energy storage, and electric vehicles, transforming traditional electrical distribution networks into active distribution networks. Stochastic technologies can present operational and control challenges, especially for radially configured active distribution networks. In this scenario, strengthening the existing active distribution networks is necessary. This study optimally integrates soft open points for dynamic network reconfiguration to handle uncertainty in active distribution networks. The location, size, and reconfiguration of the soft open points were obtained for the hourly load profile, which included electric vehicle fleet load penetration and PV distributed generation. The proposed multi-objective function uses active power loss, voltage profile, and reliability indices. The proposed multivariable optimization problem was solved using artificial rabbits optimization. The simulations were performed on a modified IEEE 33-bus radial distribution system. The computational efficiency of artificial rabbits optimization is competitive with other prominent algorithms. The proposed approach of optimal soft open points and dynamic network reconfiguration is utilized to cope with uncertainty and run the present active distribution networks with better technical and reliability characteristics. 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Optimising Education for Sustainable Development through Secondary School Teachers with Relevant Subjects, Standards and Training: Quantitative Review
India aims to become a developed nation by 2047, emphasizing the role of Education for Sustainable Development (ESD) in achieving Sustainable Development Goals. This study examines the beliefs of Kerala secondary school teachers regarding ESD, investigating how teaching standards, subjects, and prior ESD training shape their perspectives. A survey of 400 teachers utilized the revised ESD Belief Scale, incorporating demographic considerations. The research examined demographic variables, including teaching level, subject specialization, and previous ESD training. Quantitative analysis encompassed descriptive statistics, t-tests, and ANOVA to evaluate beliefs across various groupings. Findings reveal that educators predominantly recognise the significance of ESD in fostering sustainable decision-making and lifelong learning. The discipline taught, especially social sciences in contrast to science, technology, engineering, and mathematics, is the primary determinant of educators' beliefs towards ESD. Teachers recognise the benefits of ESD, although they encounter obstacles, including restricted curricular integration and implementation. This research addresses ESD within the secondary curriculum in a unique manner, filling a notable gap in both theoretical and empirical literature. The implementation of an updated belief scale and subgroup analyses provides policymakers and curriculum developers with novel perspectives. It is recommended that curriculum reform incorporate ESD throughout all courses, accompanied by specialized teacher training to enhance awareness, skills, and pedagogical techniques for the effective implementation of ESD. Secondary educators in Kerala predominantly advocate for the integration of ESD, particularly within the social sciences. Future policy and research must emphasise curricular innovation and longitudinal assessment to further Indias sustainable development objectives. 2026 Sijo VARGHESE & P.M. MATHEW. Published by the Asian Society of Human Services (ASHS). -
Optimising lead qualification through machine learning: A customer data-driven approach
Lead generation is the process of turning an outside person or business into a customer of the business. Traditionally, marketing personnel must conduct significant follow-ups in order to convert even one potential consumer. Converting bad client leads can cause businesses to burn through cash reserves. As a result of this, it is now necessary to develop an automated system that can correctly anticipate whether or not a lead should be explored (converted to a customer or not). In this study, an attempt is made to evaluate historical data for leads produced by other businesses in order to train and validate a machine learning (ML)/deep learning (DL) model and test it against real-world characteristics to categorise them as hot leads (convert to customers) or cold leads (failed leads). This can be achieved by employing ML algorithms, low codeno code libraries, such as PyCaret in Python, and can be used to make predictions regarding probable lead creation, propensity to convert generated leads and optimal actions on the leads by communications teams. Supervised ML algorithms such as logistic regression, decision trees, random forests and other models using a Python library were built to score leads for identifying potential conversions. With good and broad lead-scoring models in place, businesses can optimise their CTI actions on the basis of lead prioritisation and let go of non-prospect leads at the right time to cut costs and enable efficiency. The result of this study reveals that 52 per cent of the sample of 74,779 leads are cold leads and 48 per cent are hot leads that are sales qualified. The leads are qualified using the lead score matrix. This method can aid digital businesses to remove unqualified leads and manage leads better, and therefore improve the quality of the leads sent to clients. This, in turn, will improve conversion rates for individual customers. These increased conversion rates will enhance the business strategy of digital marketing firms. Henry Stewart Publications. -
Optimising QoS with load balancing in cloud computing applying dual fuzzy technique
Cloud computing has become a necessity when the internet usage has increased drastically. This research paper objective is to optimise quality of service in cloud computing using dual fuzzy technique. With the competition to provide the best quality service at cloud data centre, we are analysing the parameters of average response time, average completion time, average CPU utilisation and job success. Cloud-sim simulator along with the mathematical model is used to provide reliable and valid result. To achieve the best result, the load in data centre needs to be efficiently distributed, so that it is managed to process maximum service requests with the best service response time and very few failures. In this paper, we applied dual fuzzy technique for the load balancing in the cloud data centre and the findings were extensive and support the proposed technique. With this technique, cloud computing service provider can provide better quality service. Copyright 2021 Inderscience Enterprises Ltd. -
Optimization and Design of a Sustainable Industrial Grid System
Electricity is a multifaceted form of energy and is used globally, with a continuously growing demand. Electrical power grids are there for more than 150 years. The generated electrical power is delivered to different industrial, commercial, and residential sectors, thereby fulfilling the ever-growing demand. In this research paper, the design and optimization of an industrial grid for various electrical loads is discussed. The electrical grid ensures a stable power supply to the loads by providing quality power with the minimum total harmonic distortion (THD) possible. A complete study of the short circuit current has been done in two different electrical grid systems, as it is seen that the short circuit current depends on the impedance of the transformer which feeds the load. These two designs of a single diagram will be simulated by using a power system analyzer, the Electrical Transient Analyzer Program (ETAP) software. The different electrical parameters, like choosing the optimised rated generator, cables, and transformers, are done. Load flow analysis is performed on both the design to evaluate the THD, short circuit fault, as well as to choose the right protection circuit for the system. 2022 Samat Iderus et al. -
Optimization and sensitivity analysis of heat transport of hybrid nanoliquid in an annulus with quadratic Boussinesq approximation and quadratic thermal radiation
The quadratic convective flow of hybrid nanoliquid in an annulus subjected to quadratic thermal radiation is studied for the first time. The impact of suction/injection and the uniform movement of the rings are considered. Nonlinear equations are handled numerically by adopting the shooting technique. An optimization procedure is performed by using response surface methodology. The maximum heat transport is observed for chosen values of effective parameters (thermal radiation parameter (5 ? Rt? 15) , temperature ratio parameter (1.1 ? ?w? 5.1) and nanoparticle volume fraction of copper (1 % ? ?Cu? 3 %)) at three different levels (low(? 1), middle(0) and high(+ 1)). In addition, a slope of the data point is evaluated for the friction coefficient and the Nusselt number. The results showed that the impact of quadratic thermal radiation on velocity and temperature distributions is more significant than linear thermal radiation. Further, an increase in quadratic convection and quadratic thermal radiation leads to an improvement in the friction coefficient of the skin on the inner surface of the outer annulus. Furthermore, the sensitivity of the friction coefficient is positive for the appearance of quadratic thermal radiation. 2020, SocietItaliana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature. -
Optimization Based Rice Leaf Disease Classification in Federated Learning
Numerous farmers worldwide are impacted by diseases connected to rice leaves that frequently endanger the sustainability of the rice industry. Diseases that affect the leaves of rice plants severely limit their ability to produce rice, and they are typically brought on by bacteria, viruses, or fungi. This paper proposes an innovative classification scheme for rice leaf diseases based on Federated Learning (FL). Here, FL framework comprises two entities, namely nodes and servers. Every node does initial local training using local data. Moreover, produced local model is then updated on server. Model aggregation is done at the server since several nodes update their local models and send them to it. The nodes download the global model that server has generated as a result. The nodes update their training using transferred global model and local model. The following series of actions are taken in the training model. The input image is mainly obtained from a database and pre-processed with a Kalman filter to eliminate noise. Then, numerous operations for data augmentation are applied. In addition, feature extraction is done and generated features are used in LeNet for rice leaf diseases classification. LeNet is trained using the Spotted Hyena Archimedes Optimizer (SHAO). The developed method shows better precision of 91.3%, recall of 92.2%, f-measure of 91.7%, loss function of 3.3%, Mean Square Error (MSE) of 7.3%, and Root MSE of 27.1%. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Optimization Enabled Ensemble Learning for Leukemia Classification Using Microarray Data
Leukemia classification involves identification and categorization of various leukemias, a cluster of blood malignancies influencing white blood cells. Proper classification is crucial for selecting the appropriate treatment modalities and predicting outcomes in patients. Historically, leukemia classification was based on clinical and morphological characteristics, but new developments in genomics like microarray and next-generation sequencing tools have facilitated more accurate molecular classifications. Machine learning (ML) and deep learning (DL) methods have transformed leukemia classification by enabling automation of analysis in large and intricate datasets to ensure more accurate and efficient leukemia subtype classification. The primary goal of this research is to suggest a new leukemia classification method using microarray data. Leukemia microarray data first undergoes preprocessing, after which feature selection is performed through Serial Exponential-Secretary Bird Optimization Algorithm (SE-SBOA). SE-SBOA is an optimization method that embeds the exponential weighted moving average concept (EWMA) into Secretary Bird Optimization Algorithm (SBOA). The method helps to find the best feature subset, improving model performance at lower complexity. Lastly, leukemia classification is done using the proposed ensemble method that combines Graph Neural Network (GNN), Multi-Layer Perceptron (MLP) and Random Forest. Utilizing the advantages of GNN, MLP and Random Forest, the model proposed herein attains higher classification accuracy and proves to outperform traditional methods. Experimental results demonstrate that the SE-SBOA-based Ensemble Learning technique outperformed standard methods, attaining an accuracy of 95.9%, a precision of 96.1%, a recall of 96.2%, and an F1-score of 96.2%. 2025, Innovative Information Science and Technology Research Group. All rights reserved. -
Optimization Ensemble Learning Techniques for Reliable Crop Yield Prediction using ML
The agricultural sector's increasing reliance on technology has paved the way for advanced data-driven methodologies, with crop yield prediction emerging as a critical focus. This study dives into the complex landscape of crop yield prediction, employing a comprehensive approach that involves data preprocessing, model development, and performance evaluation. This research goes into enhancing crop yield prediction through a thorough data-driven approach. Beginning with comprehensive data preprocessing, including outlier analysis and feature scaling, the study ensures dataset integrity. Ensemble learning, employing Gradient Boosting Regressor, Random Forest Regressor and Decision Tree Regressor, captures intricate relationships within the dataset. Model performance, assessed through R-squared scores, demonstrates promising predictive capabilities. Subsequent outlier analysis and hyperparameter tuning yield substantial improvements, contributing valuable insights for agricultural decision-making. The research not only advances crop yield prediction but also offers practical guidance for integrating machine learning into agriculture, promising transformative outcomes for sustainable practices. The research also highlights how significant interpretability is to machine learning models so that stakeholders can comprehend and embrace them. This allows for a smooth integration of the models into current agricultural practices and encourages openness and reliability in decision-making. 2024 IEEE. -
Optimization in the Flow of Scientific Newspapers
The evolutions that occurred in the past decades have provoked variations in the market as well as academic and research. Given this scenario, the research explored in this article was aimed to analyze the contribution of the management of PMBOK methods for the optimization of Scientific Editorial Flow. The methodology used presented a quantitative approach, of descriptive character based on a survey, made available on social networks and Facebook groups, through the google forms platform. The sample is given by Snowball, this type of sampling enables the researcher to study specific groups and is difficult to reach. The analysis was by descriptive statistics, using the Likert scale, as well as the weighted average and fashion responses. It was identified that the Critical Success Factors of a Project that can contribute to the optimization of the editorial flow of a Scientific Periodical are efficient communication, empowerment, change management, client involvement, supplier involvement and conflict management. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Optimization of Abrasive Wear Parameters of Halloysite Nanotubes Reinforced Silk/Basalt Hybrid Epoxy Composites using Taguchi Approach
The demand for environmentally friendly and sustainable materials for nonstructural and structural applications grows by the day. Polymeric composites reinforced with fillers and fibres are considered to have increased strength and desirable wear resistance. Abrasive wear of industrial and agricultural based components are currently one of the most serious issue. Therefore, the current research reports on the influence of Halloysite-Nanotubes (HNTs) loading on the three body abrasive behavior of bi-directional silk fibre (SF) and basalt fibre (BF) reinforced epoxy (Ep) composites. Rubber wheel with dry sand abrasion testing in accordance with ASTM G65-16e1 was performed with four control parameters such as filler content, load, abrading distance and silica sand size. The tests were planned as per orthogonal array of Taguchi (L27). Significant impact of control factors were identified using ANOVA (Analysis of variance). The results demonstrated that adding HNTs to SF-BF/Ep nanocomposites significantly improved the wear resistance and the combination of A2, B1, C3 and D1 control factors yields the lower specific wear rate (SWR). Findings exhibit that the load and abrading distance were the most significant parameters affecting the abrasive wear of SF-BF/Ep nanocomposites followed by filler content and silica sand size. Microstructural features were observed via scanning-electron-microscopy (SEM). 2022 Published by Faculty of Engineering. -
Optimization of anti-corrosion performance of novel magnetic polyaniline-Chitosan nanocomposite decorated with silver nanoparticles on Al in simulated acidizing environment using RSM
The suitability of newly synthesized magnetic polyaniline-Chitosan nanocomposite decorated with silver nanoparticles (Ag@PANI-CS-Fe3O4) as a robust corrosion inhibitor for Aluminum (Al) in a 5 M HCl environment has been investigated via Weight Loss (WL), Alternating Current (AC)-Impedance Spectroscopy (IS), Potentiontiodynamic polarization (Tafel plots), and Scanning Electron Microscopy (SEM) techniques. The protection efficiency (PE) was mathematically modeled using the Response Surface Methodology (RSM) to fit an empirical relation in terms of temperature, nanocomposite concentration, and time using the face-centered central composite design. The model was accurate with a coefficient of determination (R2 = 99.27%). The negative Gibb's free energy of adsorption (?Gads) values confirmed the spontaneity of Freundlich adsorption isotherm process on Al in 5 M HCl solution. The optimization simulation yielded maximum protection efficiency (of 97.88%) at 5 mg/L nanocomposite concentration, 1 h time, and an intermediate temperature of 304.8 K. Furthermore, the sensitivity of PE was evaluated to find that the low temperature 303 K is favorable for PE, whereas higher temperature will act adversely on PE. The results obtained by the RSM model are in agreement with the experimental observations. 2021 Elsevier B.V.