Journal of Applied Research on Industrial Engineering
Volume:8 Issue: 4, Autumn 2021

The purpose of this paper is to identify and classify the main factors implementing the Cloud Manufacturing Systems (CMS) in the internet service providers company by Fuzzy Cognitive Map (FCM) methodology. Through expert opinions, 20 main factors were identified and classified based on the importance and then a FCM approach was applied for obtaining the relationship between the factors, and all of the impact factors are outputs of expert opinion. The outcomes of the study highlighted those three factors, including customer scoring, R&D, and method development were the most important factors impressing the implementation CMSs. Present practices for implementation CMS are and the relationship between the main factors also impressing CMS by employing the FCM approach in the Iranian internet service provider company. The model obtained in this study guides the managers to identify and classify the important factors of the cloud manufacturing and finally implement it successfully.

In this paper, layerwise finite element analysis for the free vibration behavior of two-dimensional functionally graded sandwich plates with different boundary conditions is presented. The plates consist of three layers; a functionally graded layer embedded between ceramic and metal isotropic layers. The layerwise approach is based on the third order shear deformation theory for the middle layer, while the first order shear deformation theory is used to model both the upper and lower isotropic layers. Quadrilateral 8-noded element with 13-degrees of freedom per node is used for this purpose. The present results show very good agreements with the published analytical results of plates consist of a single functionally graded layer. Furthermore, for sandwich plates good agreements were obtained when the present results are compared with similar problems solved by other methods in literature. Parametric studies were investigated for various plate parameters including applied boundary conditions, volume fraction exponents and plate side to thickness ratio.

The Location Routing Problem (LRP), Automatic Guided Vehicle (AGV), and Uncertainty Planner Facility (UPF) in Facility Location Problems (FLP) have been critical. This research proposed the role of LRP in Intelligence AGV Location–Routing Problem (IALRP) and energy-consuming impact in CMS. The goal of problem minimization dispatching opening cost and the cost of AGV trucking. We set up multi-objective programming. To solve the model, we utilized and investigate the Imperialist Competitor Algorithm (ICA) with Variable Neighborhood Search (VNS). It is shown that the ICAVNS algorithm is high quality effects for the integrated LRP in AGVs and comparison, with the last researches, the sensitivity analysis, and numerical examples imply the validity and good convexity of the purposed model according to the cost minimization.

The purpose of this paper is to enhance the quality of private hospitals in Egypt. The health care field, especially in the private hospital in Egypt has acquired huge importance lately because of its great contribution to the fast handling of patients. Today, customers of the private hospitals complain from slow handling during registration process, which may lead to making the patient condition worse and may refers to bad performance of the hospital at all. In view of the importance of these complains, the researcher selected the (PATH) as a tool to improve the quality in emergency department at hospital. Using that tool structural and organizational changes such as: quality committees; multidisciplinary teams and technology investments. The study found that applying the (PATH) in the emergency department leads to decrease the patient waiting time and leads to improve the overall performance of the ( Sina  hospital).

Air traffic management is an important job and often faces various problems. One of the most common problems in this area is the issue of aircraft sequencing, which is a multi-dimensional problem due to the large number of flights and their different positional conditions. Previously proposed models were based on First Come, First Service (FCFS) have not considered the time factor, resulting in increased delay penalties. In this regard, this article proposes a model in which the time factor is one of the factors that is managed and additional costs due to delay will be eliminated. This paper proposed the multi-objective grey wolf optimization (MOGWO) algorithm to evaluate three objective functions such as the airport runway efficiency, the apron and parking costs, and the fuel consumption costs. The proposed algorithm compared with well- known NSGA-II (non–dominated Sorting Genetic Algorithm). The obtain results represented that in the case of using all the data for the first, second and third-objective function, MOGWO performs better than NSGA-II. The brilliant results demonstrated the superiority of the proposed model. In this study, using the proposed model, the data set of Shahid Hasheminejad International Airport in Mashhad was analyzed.

The move to standardize Indian Sign Language has created an opportunity for researchers to focus on solving local problems, to increase its reach. In this paper, a survey and assessment of the techniques applied to the recognition and conversion of Indian Sign Language are performed. An overview of the techniques used in sign language recognition for Indian Sign Language is provided to understand the status of research in this field. Following this, a comparison of techniques aimed at rendering a more detailed picture of the research results is presented. The challenges faced by researchers, the limitations of current techniques, and the need for improved research in this area are highlighted. With the intent of spurring more in-depth research, key areas within the approaches and techniques in need of improvement are summarized.

Clustering players based on their abilities, a new perspective and an important opportunity to meet needs that in the light of traditional talent identification and player science, which is held periodically and there is not enough time for them to appear. Early recognition of these abilities is a factor influencing the success of sports teams. Artificial Neural Network (ANN) is a new method of modelling and prediction. The aim of this study was to cluster basketball players based on their individual abilities. For this purpose, Self-Organizing Map (SOM) Neural Networks (NNs) were used. The data set used by 3000 NBA players for 2011 until 2018 is from the Basketball-Reference[1] site. Each player is assigned 30 attributes to reduce them using the Principal Component Analysis (PCA) method and the features for each player were reduced to 12 samples. In order to implement a SOM of features and functions in MATLAB software 65% of the data were used as the network training phase and the remaining 35% were used to the test phase. 12 players’ features as network input and output 9 clusters resulting from the combination of features. After simulation using SOM, accuracy parameter with the help of this system were obtained above 95%. The result of the study showed that the performance of the SOM in clustering basketball players was higher than the K-Means algorithm. The network implemented in this article has a faster speed in the training process and generalizability than similar cases.