Display Omitted We give an overview of the fuzzy inventory models literature gathering 210 papers from 1987 onward.The current state of the literature is categorized into 13 areas under five main groups.We provide details to describe the applied methodologies.The research is concluded with future research directions. Over the years since the advancement of inventory management and fuzzy set theories, a vast number of studies have been published to integrate these concepts. Nonetheless, no comprehensive and systematic literature review can be found that analyzed the studies in this research stream. It motivated us to conduct this survey as a systematic and comprehensive review in the field of fuzzy inventory management to identify major achievements attained so far and shed light on future directions. First, the earlier review papers are presented to reveal the necessity of this study, and then methodology applied in collecting sample papers is described, followed by an in-depth analysis of the papers. Totally, a sample of 210 papers is identified and classified according to the common characteristics of the models. Several aspects of the models are assessed that led to identification of some areas overlooked by researchers so far.

Fuzzy inventory models

One of the most effective approaches to reduce carbon emissions is the integration of renewable energy sources into electrical power networks. Currently, wind turbines are the fastest growing among all renewable sources. With the integration of wind farms into electrical grids, the economic emission dispatch (EED) problem is becoming more complicated due to the stochastic availability of wind energy. In this study, a new approach is proposed to solve the EED problem incorporating wind farms. The problem is formulated as a chance-constrained problem to deal with the stochastic characteristic of wind power. A novel chaotic sine–cosine algorithm (CSCA) is proposed to provide the optimal generation schedule to minimise simultaneously the generation cost and emission. Some weakness has been encountered in exploitation and exploration capabilities in standard sine cosine algorithm (SCA). Hence, the chaos is integrated into the original SCA to improve its performance. In addition, a new mutation strategy is added to the SCA. In this study, the new algorithm is based on three mutually exclusive equations. The new technique is applied on the 69-bus ten-unit and 40-unit test systems with and without wind energy. The results performed by CSCA are compared with those generated by other recent techniques.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rpg.2019.1081

Chaotic sine–cosine algorithm for chance-constrained economic emission dispatch problem including wind energy

In this study, a fuzzy multi-item economic order quantity (EOQ) problem is solved by employing four different fuzzy ranking methods. All of the parameters of the multi-item EOQ problem are defined as triangular fuzzy numbers. Fuzzy ranking methods are used to rank the fuzzy objective values and to handle the constraints in the model. The results obtained by employing different fuzzy ranking methods are also compared.

Solution of a fully fuzzy multi-item economic order quantity problem by using fuzzy ranking functions

In this paper we have developed the traditional inventory control model (r,Q) taking into account a multi-item models with two objectives to minimize holding costs and shortage cost and also risk level under constraints including available budgetary, the least service

http://m-hikari.com/ams/ams-2012/ams-5-8-2012/lotfiAMS5-8-2012-1.pdf

Fuzzy-Chance Constrained Multi-Objective Programming Applications for Inventory Control Model

The field of chance constrained fractional programming (CCFP) has grown into a huge area over the last few years because of its applications in real life problems. Therefore, finding a solution technique to it is of paramount importance. The solution technique so far has been deriving deterministic equivalence of CCFP with random coefficients in the objective function and/or constraints and is possible only if random variable follows some specified distribution with known parameters. This paper presents a stochastic simulation-based genetic algorithm (GA) for solving CCFP problems, where random variables used can follow any continuous distribution. The solution procedure is tested on a few numerical examples. The results demonstrate that the suggested approach could provide researchers a promising way for solving various types of chance constrained programming (CCP) problems.

Stochastic simulation-based genetic algorithm for chance constrained fractional programming problem

ATMs are vulnerable to a wide variety of assaults and fraud because of the money and personal information available on it. In response, today’s ATMs feature enhanced hardware security systems are capable of identifying specific forms of fraud and manipulation. However, there is no defense in place for future attacks that can’t be anticipated during design. It shows how automated teller machines (ATMs) can be secured against theft without the need for extra hardware. The goal is to employ automatic techniques of model generation to learn normal behavior patterns from the status information of the standard de vices that make up an ATM, with a significant divergence from the taught behavior indicating a fraud attempt. Preprocessing, feature selection, and model training are all parts of the proposed method. Cleaning, integrating, and deduplicating data are all parts of data preprocessing. BOA is employed in feature selection and C-LSTM is used for model training. In C-LSTM, a LSTM recurrent neural network is used to obtain the sentence representation after CNN is used to extract a sequence of higher-level phrase representations. C-LSTM can learn the global and temporal sentence semantics in addition to the local aspects of phrases. When compared to LSTM and CNN, the proposed method fares very well. 

Intelligent System for ATM Fraud Detection System using C-LSTM Approach

Recruitment of persons for various assignments with required talents in an organization is an important feature, since it plays a vital role in the growth of the organization. To achieve the required expertise in recruitment, in this paper Linear Stochastic Programming (LSP) is applied along with cluster analysis technique. The aim of this paper is to obtain an optimal allocation of persons to different jobs, so that the time taken to complete all the jobs is minimum. The time taken for a person to complete a job is assumed to follow Weibull distribution. The parameters of Weibull distribution is obtained through Maximum Likelihood Estimator (MLE) approach, along with Cohen's iterative process.

An Application of Stochastic Programming with Weibull Distribution–Cluster Based Optimum Allocation of Recruitment in Manpower Planning

Frequent wastage or exit of personnel are common in many administrative and production oriented organizations. Once the accumulated number of exits from the organization reaches a certain threshold level, it could be viewed as a “breakdown point”. The time to attain breakdown point is an important characteristic for the management of the organization. In this paper, a shock model approach is proposed to obtain the expectation and variance of the time to attain the threshold level. Numerical and graphical illustrations are provided to illustrate the practical use of the model.

Expected Time for Attainment Threshold Level A Shock Model Approach

The growth of microblogging sites such as Biomedical, biomedical, defect, or bug databases makes it difficult for web users to share and express their context identification of sequential key phrases and their categories on text clustering applications. In the traditional document classification and clustering models, the features associated with TREC texts are more complex to analyze. Finding relevant feature-based key phrase patterns in the large collection of unstructured documents is becoming increasingly difficult, as the repository's size increases. The purpose of this study is to develop and implement a new hierarchical document clustering framework on a large TREC data repository. A document feature selection and clustered model are used to identify and extract MeSH related documents from TREC biomedical clinical benchmark datasets. Efficiencies of the proposed model are indicated in terms of computational memory, accuracy, and error rate, as demonstrated by experimental results.

A Novel Hierarchical Document Clustering Framework on Large TREC Biomedical Documents

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