Stochastic joint replenishment problems: periodic review policies

Operations Managers of manufacturing systems, distribution systems, and supply chains address lot sizing and scheduling problems as part of their duties. These problems are concerned with decisions related to the size of orders and their schedule. In general, products share or compete for common resources and thus require coordination of their replenishment decisions whether replenishment involves manufacturing operations or not. This research is concerned with joint replenishment problems (JRPs) which are part of multi-item lot sizing and scheduling problems in manufacturing and distribution systems in single echelon/stage systems. The principal purpose of this research is to develop three new periodic review policies for stochastic joint replenishment problem. It also highlights the lack of research on joint replenishment problems with different demand classes (DSJRP). Therefore, periodic review policy is developed for this problem where the inventory system faces different demand classes that are deterministic demand and stochastic demand. Heuristic Algorithms have been developed to obtain (near) optimal parameters for the three policies as well as a heuristic algorithm has been developed for DSJRP. Numerical tests against literature benchmarks have been presented

q-Rung orthopair fuzzy information aggregation and their application towards material selection

Material selection is a complex process that involves selecting the best material for a given application. It is a critical process in engineering, and the importance of selecting the right material for the job cannot be overstated. Multi-criteria decision-making (MCDM) is an important tool that can be used to help engineers make informed decisions about material selection. The logistic function can be extended using the soft-max function, which is widely used in stochastic classification methods like neural nets, soft-max extrapolation, linear differential analysis, and Naïve Bayes detectors. This has inspired researchers to develop soft-max-based fuzzy aggregation operators (AOs) for q-rung orthopair fuzzy sets (q-ROPFS) and to propose an MCDM approach based on these AOs. To test the effectiveness of this approach, the researchers applied it to a practical problem using q-rung orthopair fuzzy data and conducted a numerical example to validate the suggested procedures

ASSESSMENT OF SUSTAINABLE WASTEWATER TREATMENT TECHNOLOGIES USING INTERVAL-VALUED INTUITIONISTIC FUZZY DISTANCE MEASURE-BASED MAIRCA METHOD

Effective wastewater treatment has significant effects on saving water and preventing unnecessary water scarcity. An appropriate wastewater treatment technology (WWTT) brings economic benefits through reuse in different sectors and benefits the society and environment. This study aims to develop a decision-making framework for evaluating the sustainable WWTTs under interval-valued intuitionistic fuzzy set (IVIFS) environment. The proposed MCDM framework is divided into two stages. First, a new Hellinger distance measure is developed to determine the degree of difference between IVIFSs and also discussed its desirable characteristics. Second, an interval-valued intuitionistic fuzzy extension of multi-attribute ideal-real comparative analysis (MAIRCA) model is developed using the proposed Hellinger distance measure-based weighting tool. Further, the proposed model is implemented on an empirical study of sustainable WWTTs evaluation problem. Sensitivity and comparative studies are made. The results indicate that odor impacts, sludge production, maintenance and operation are the most effective sustainable factors and Microbial fuel cell (MFC) technology is the best WWTT followed by natural treatment methods

Chaos Embed Marine Predator (CMPA) Algorithm for Feature Selection

Data mining applications are growing with the availability of large data; sometimes, handling large data is also a typical task. Segregation of the data for extracting useful information is inevitable for designing modern technologies. Considering this fact, the work proposes a chaos embed marine predator algorithm (CMPA) for feature selection. The optimization routine is designed with the aim of maximizing the classification accuracy with the optimal number of features selected. The well-known benchmark data sets have been chosen for validating the performance of the proposed algorithm. A comparative analysis of the performance with some well-known algorithms advocates the applicability of the proposed algorithm. Further, the analysis has been extended to some of the well-known chaotic algorithms; first, the binary versions of these algorithms are developed and then the comparative analysis of the performance has been conducted on the basis of mean features selected, classification accuracy obtained and fitness function values. Statistical significance tests have also been conducted to establish the significance of the proposed algorithm

Credit Policy Strategies for Green Product With Expiry Date Dependent Deterioration via Grey Wolf Optimizer

In a supply chain that consists of a supplier or manufacturer, a retailer, and a customer, the supplier regularly offers the retailer a pay in later facility in terms of $S$ periods, while the retailer then gives their client a pay in later facility in terms of $N$ periods to increase sales and decrease inventory. Offering trade credit benefits the seller’s sales and profits, but it also increases default risk. As a result, understanding the credit period is becoming widely acknowledged as a key tactic for boosting seller profitability. This study suggests an EOQ model in the perspective of retailer point of view for which: (a) both the supplier and the retailer supply up-stream pay in later facility; (b) downstream trade credit provided from the retailer to the buyer increases opportunity cost and default risk in addition to sales and profitability; (c) items that are degrading not only continue to degrade over time but also have an expiration date. We employed the well-known metaheuristic algorithm Grey Wolf Optimizer (GWO) to solve the optimisation problem because the objective function is high nonlinear nature. In addition, we have compared the results with some other metaheuristic algorithm. In order to highlight the problem and provide managerial advice, we conclude by using some numerical examples

An Intelligent Human Age and Gender Forecasting Framework Using Deep Learning Algorithms

Dental images are utilized to gather significant signs that are useful in disease diagnosis, treatment, and forensic examination. Many dental age and gender detection procedures have limitations, such as minimal accuracy and dependability. Gender identification techniques aren’t well studied, despite the fact that classification effectiveness and accuracy are low. The suggested approach takes into account the shortcomings of the current system. Deep learning techniques can successfully resolve issues that occurred in other classifiers. Human gender and age identification is a crucial process in the fields of forensics, anthropology, and bio archeology. The image preparation and feature extraction process are accomplished by deep learning algorithms. The performance of classification is improved by minimizing the occurrence of loss with the assistance of a spike neuron-based convolutional neural network (SN-CNN). The performance of SN-CNN is examined by comparing the performance metrics with the existing state-of-art techniques. SN-CNN-based classifier achieved 99.6% accuracy over existing techniques

Pricing Policy in an Inventory Model with Green Level Dependent Demand for a Deteriorating Item

The goal of this research is to investigate an inventory model for degrading commodities with linear selling prices and nonlinear green level-dependent demand for an item. The pre-payment option with a one-time flat reduction on the product’s selling price is considered here. The governing differential equations are used to mathematically define the model and solve numerically to optimize the model’s average profit. After that, the model is tested using a numerical example, and sensitivity analyses are run to see how changing inventory factors affects the best strategy. The concavity of the objective function is shown graphically with the help of MATLAB software. Finally, some applications of this approach and future scopes are discussed

Advertising and pricing strategies of an inventory model with product freshness-related demand and expiration date-related deterioration

Perishable products in Europe, the United States, and Canada typically have expiration dates or maximum lifetimes assigned to them. As these products approach their predetermined lifetimes , there is a gradual increase in the rate of decay, leading to a reduction in their freshness. To examine the impact of these two distinct attributes on a perishable product with a predetermined expiration date, we introduce two inventory models that utilize a demand function incorporating a linearly decreasing price dependence and a nonlinear increase that is influenced by advertising. These models consist of: (i) a zero-ending stock scenario and (ii) a partially backlogged shortage situation. Under certain conditions, the optimal cycle length for the stock zero-ending model and the optimal period with positive inventory for the model with shortages are obtained. The frequency of advertisement is a discrete and integer decision variable, we devise two algorithms for both models to determine the optimal frequency of advertisement that maximizes the profit. Finally, we suggest some strategies in order to increase the profit by studying marginal insights from a sensitivity analysis. It is recommended that the manager focuses on creating more impactful advertisements that are affordable to promote the product’s information through popular media channels

A Hybrid Approach Based on Principal Component Analysis for Power Quality Event Classification Using Support Vector Machines

Power quality has emerged as a sincere denominator in the planning and operation of a power system. Various events affect the quality of power at the distribution end of the system. Detection of these events has been a major thrust area in the last decade. This paper presents the application of Support Vector Machine (SVM) in classifying the power quality events. Well-known signal processing techniques, namely Hilbert transform and Wavelet transform, are employed to extract the potential features from the observation sets of voltages. Supervised architecture consisting of SVM has been constructed by tuning the parameters of SVM by various algorithms. It has been observed that Augmented Crow Search Algorithm (ACSA) yields the best accuracy compared to other contemporary optimizers. Further, Principal Component Analysis (PCA) is employed to choose the most significant features from the available features. On the basis of PCA, three different models of tuned SVMs are constructed. Comparative analysis of these three models, along with recently published approaches, is exhibited. Results are validated by the statistical one-way analysis of variance (ANOVA) method. It is observed that SVM, which contains attributes from both signal-processing techniques, gives satisfactory results

Local Grey Predictor Based on Cubic Polynomial Realization for Market Clearing Price Prediction

With the development of restructured power markets, the profit-making competitive business environment has emerged. With the help of different advanced technologies, generating companies are taking decisions regarding trading electricity with imperfect information about marketing operating conditions. The forecasting of the market clearing price (MCP) is a potential issue in these markets. Early information on the MCP can be a proven beneficial tool for accumulating profit. In this work, a local grey prediction model based on a cubic polynomial function is presented to estimate the MCP with the help of historical data. The mathematical framework of this grey model was established and evaluated for different market conditions and databases. The comparison between traditional grey models and some advanced grey models reveals that the proposed model yields accurate results