A deterministic inventory model has been developed for deteriorating items and Particle Swarm Optimization (PSO) having a ramp type demands with the effects of inflation with two-warehouse facilities. The owned warehouse (OW) has a fixed capacity of W units; the rented warehouse (RW) has unlimited capacity. Here, we assumed that the inventory holding cost in RW is higher than those in OW. Shortages in inventory are allowed and partially backlogged and Particle Swarm Optimization (PSO) it is assumed that the inventory deteriorates over time at a variable deterioration rate. The effect of inflation has also been considered for various costs associated with the inventory system and Particle Swarm Optimization (PSO). Numerical example is also used to study the behaviour of the model. Cost minimization technique is used to get the expressions for total cost and other parameters.
Hazardous Substance Storage inventory model is developed for decaying items with ramp type demand and the effects of inflation using Particle Swarm Optimization. The Hazardous Substance Storage has unlimited capacity. Here, we assumed that the inventory holding cost in Hazardous Substance Storage is higher. Shortages in inventory are allowed and partially backlogged and it is assumed that the inventory deteriorates over time at a variable deterioration rate. Cost minimization technique is used to get the expressions for total cost using Particle Swarm Optimization and numerical example is also used to study the behavior of the model.
Bladder cancer represents a significant healthcare burden globally, necessitating innovative approaches to enhance early detection and personalized management strategies.The integration of mathematics and artificial intelligence (AI) into bladder cancer screening holds promise for revolutionizing diagnostic accuracy and treatment outcomes.This comprehensive review explores the latest advancements and emerging trends at the intersection of mathematics, AI, and bladder cancer screening.The epidemiology and clinical challenges of bladder cancer underscore the need for improved screening modalities to facilitate early diagnosis and timely intervention.Mathematical modeling and AI offer novel avenues for transforming bladder cancer screening through the development of predictive models and AIdriven technologies.These tools enable the integration of quantitative analyses, machine learning algorithms, and predictive analytics to identify individuals at high risk of bladder cancer development or recurrence based on demographic, clinical, and molecular data.
The impact of the Covid-19 pandemic on the inventory management of the Medicine supply chain is an essential part of inventory management in the area and has become an important concept for the overall profitability of the industrial scenario. It consists of several levels in which the material goes through different phases in order to reach the end customer. The impact of the Covid-19 pandemic on the inventory management of the three-tiered Medicine supply chain includes a Medicine Manufacturing sites, Medicine warehouse and medical centers that bear the costs. A coordinated approach between levels is necessary so that the chain is precisely tuned for the lowest inventory and minimum cost, and therefore, maximum profit. In this article, we consider a three-level coordinated impact of the Covid-19 pandemic on inventory management of the Medicine supply chain with a single Medicine collection point providing a single type of product to distribution centers individual Medicine, then to individual medical centers. A mathematical model is being developed for the coordinated effects of the Covid-19 pandemic on inventory management of the Medicine supply chain, which is solved by using the travelling salesman problem to optimize the ant colony for optimal values of decision variables and target functions. A numerical example is provided and the results obtained here are compared for these techniques.
Abstract Artificial intelligence and Deep learning techniques propose and provide effective mechanism for classification among several commodities like gender classification on basis of ridge count, spam mail classification and detection etc. Here we have proposed a hybrid module on basis of available AI and ML techniques by which we can achieve more than 90% accuracy for any provided test dataset. We have included over 50423 samples, out of which we use 66 percentage of data for training purpose of our model and 34% remaining sample we use as test dataset, with 8 fold we have achieve approximately 96% accuracy. For collection of dataset and processing of dataset we gone through several phases which include extraction of feature (EoF) using feature extraction technique, for cleaning of dataset we have use dimension reduction technique factor analysis(FA). In next phase for classification of flowers we have used classification techniques in which we found the accuracy score of j48 classifier is higher than Naïve Bayes and Jrip algorithm. Hence the choice of j48 classifier for classification is right.
In this manuscript, we portray the composition and availability analysis of leaf spring. In wheeled vehicles for the suspension, an elementary form of spring i.e. leaf spring is usually utilized. Particularly in industrial vehicles, Leaf springs are one of the widely recognized suspension segments they are frequently used. A method is presented that tests the availability of each part involved in the system as well as the system as a whole. Besides, the analysis demonstrates an optimization model that helps to boost the availability of the leaf spring production plant system. The system is divided into different subsystems considering various phases in the production of leaf spring. The framework of this system consists of four components i.e. shearing, punching, heating, and assembling. Using the Markov birth-death strategy we established the mathematical model of the plant. The matrix method is applied to simplify the differential equations and C programming survey the fluctuation of availability related to time. The numerical results for the different framework are given which are effective to enhance the maintenance policy of the system.
Abstract In the industrial society, the concept of green product research is becoming prevalent in many manufacturing industries. The green technology is implemented to develop an environmentally green product that brought attention to a growing technological field as it attains sustainable growth in a market. The green process includes logistics, manufacturing, and product design to reach a high level of sustainability. The utilization of green technologies in manufacturing industries reduces the major effect of industrial outcomes on environment. The product manufactured in the electronic industries is rapidly utilized by more population. This paper deals with the research issue faced during product manufacturing in electronic industries. Initially, the issues encountered during product development are air pollution, solid waste, and water pollution. The product is enriched with the characteristic features such as an increase in efficiency, reduction in toxic content, and must be eco-friendly. The developed green product should satisfy environmental standards. Environmental Conscious Manufacturing (ECM) accountability to satisfy the government rules and regulations for product development in the manufacturing industry and also deals with the customer’s expectation. The eco-friendly product includes eco-design, recycling, clean production, low cost, and reclamation of products. The eco-design is responsible for improving the environmental outcomes and also reliable for a reduction in long-term cost. The green product design and manufacturing focus mainly on energy conservation and the development of a product with less wastage. The research on green product manufacturing is mainly to save the environment by reducing carbon foot print and to make the product in less cost. The main consequence faced during the development of the green product is destroying the environment by utilizing natural resources. Thus, in this paper, the issues faced during the green product design and manufacturing of the electronic manufacturing industry are discussed.
In this paper a deterministic supply chain inventory model has been developed for deteriorating items having a ramp type demand with the effects of inflation with two-storage facilities and Economic Load Dispatch Problem Using Genetic Algorithm..The owned warehouse (OW) has a fixed capacity of W units; the rented warehouse (RW) has unlimited capacity.Here, we assumed that the inventory holding cost in RW is higher than those in OW and Economic Load Dispatch Problem Using Genetic Algorithm.Shortages in inventory are allowed and partially backlogged and it is assumed that the inventory deteriorates over time at a variable deterioration rate and Economic Load Dispatch Problem Using Genetic Algorithm.The effect of inflation has also been considered for various costs associated with the supply chain inventory system.Numerical example is also used to study the behavior of the model and Economic Load Dispatch Problem Using Genetic Algorithm.Cost minimization technique is used to get the expressions for total cost and other parameters and Economic Load Dispatch Problem Using Genetic Algorithm.
In this paper a deterministic Pharmaceutical drug inventory model for deteriorating items with two level of storage system and time dependent demand with partial backlogged shortages is developed. Stock is transferred RW to OW under bulk release pattern and the transportation cost is taken to be negligibleUnder FIFO dispatching policy Using Ant Colony Optimization for travelling salesman problem. The deterioration rates in both the warehouses are constant but different due to the different preservation proceduresUnder FIFO dispatching policy Using Ant Colony Optimization for travelling salesman problem. Holding cost is considered to be constant up to a definite time and is increases. Ant Colony Optimization for travelling salesman problemwith varying population size is used to solve the model. In this Ant Colony Optimization for travelling salesman problem a subset of better children is included with the parent population for next generation and size of this subset is a percentage of the size of its parent set.The numerical example is presented to demonstrate the development of mode land to validate it. Sensitivity analysis is performed separately for each parameter and Ant Colony Optimization for travelling salesman problem.
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