Display Omitted A MCDM method as the extension of the fuzzy TOPSIS (FTOPSIS) is described.An approach uses ordered fuzzy numbers (OFNs).Orientations of fuzzy numbers distinguish the type of criteria (benefit, cost).Flow controller in a manufacturing system is presented as application of the method.A simulation of a manufacturing system model and comparison of other methods are presented. The aim of the paper is to present a practical solution to a particular real-life problem of discrete flow control in a manufacturing system. For this purpose a fuzzy Multi-Criteria Decision Making (MCDM) method, which is an extension of the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) approach using Ordered Fuzzy Numbers (OFNs), is used. The paper includes a case study of the application of this method as a flow controller for the transport trolley in a flexible manufacturing system. The immediate aim of control is to choose the transport trolleys destination (production line). A set of criteria (parameters) for the evaluation of the flows destination is suggested, related to time efficiency and equal workload of machines. The advantage of the proposed method is its ability to distinguish the type of criteria (benefit, cost) by using the orientation of ordered fuzzy numbers. An example of calculations using the presented method is shown and the results of the method, based on the simulation of a manufacturing system, are presented. The results thus obtained are compatible with flexible automation. In order to test the FTOPSIS method with OFNs the proposed flow control method is compared with the TOPSIS method and other simple control methods (dispatching rules). The proposed method is more efficient than the methods used for comparison and can be effective and efficient for diversified production and fast, automatic changeovers on production lines.

Fuzzy TOPSIS method with ordered fuzzy numbers for flow control in a manufacturing system

Production in small and medium enterprises (SMEs) makes a substantial contribution to the Gross Domestic Product directly and indirectly in developing economies including India. In the present time...

Analyzing Industry4.0 Implementation Barriers in Indian SMEs

The remaining useful life of electrolytic capacitors is most important to guarantee the safety and reliability of the electric systems The remaining useful life is a nonlinear function, which dramatically changes by different internal and external effects in the capacitors Soft computing approaches can be used as a powerful tool to analyze data and identify complex systems Neuro-fuzzy approach is one of the important and widely popular topics in soft computing that are used for nonlinear system identification of dynamic functions In this paper, an adaptive neuro fuzzy inference system based on subtractive clustering algorithm with 12 inputs is presented for system identification of remaining useful life in the electrolytic capacitors An experimental dataset is considered to model the remaining useful life, which was provided by the Prognostics Data Repository of NASA Comparing between simulations and experimental results has illustrated the accuracy of the purposed method to identify nonlinear systems According to the results, mean squared error of the training and test data are 3489∗10−4 and 1476∗10−2 respectively

Neuro-fuzzy system identification for remaining useful life of electrolytic capacitors

Li-ion batteries as secondary cells have important role in transfer energy in the technology world. These storage batteries have nonlinear behaviors against increasing their temperature. Therefore, thermal effect as one of important parameters is considered for modeling these accumulators. An artificial neural network is subset of computational intelligence approaches method that can identify a wide range of complex systems with the highest accuracy. In addition, this approach does not require much knowledge from the systems and is used as a black box model. In this paper, a dynamic artificial neural network approach based on Nonlinear Autoregressive eXogenous (NARX) model to analyze thermal behaviors of a li-ion battery is presented. The proposed network is trained by Levenberg-Marquardt backpropagation algorithm. In this method, discharge-load voltage and discharge-measured temperature of a li-ion is considered as input and output of the network respectively. The input and output data are provided from an experimental test that was carried out by one of the research centers of NASA. Simulation results show that the proposed approach is high accuracy to model the battery with MSE 2.032×10−2, 5.438×10−1 and 2.155×10−1 in order for training, validation, and test data. Also, regression R is measured the correlation between all outputs and targets about 9.674×10−1.

A dynamic artificial neural network approach to estimate thermal behaviors of li-ion batteries

In the mechanical product design, tolerance specification is a crucial process to select tolerance types to control the geometrical variation of the features on a workpiece. Tolerance specification is a kind of decision-making process which needs to consider many influential factors of the manufacturing cost and quality requirement of a workpiece. In this context, those factors are classified into two categories, that of static factors from the rules and standards, and that of dynamic factors based on the manufacturing site’s information. This paper presents a novel method for computer-aided tolerance specification to evaluate both types of factors while the traditional methods only take the static factors into considerations. Those factors are assessed by the developed MADM (multiple attributes decision-making) algorithm, assisted with the rule-based algorithm and axiomatic design algorithm. A case study is undertaken, and its result shows the effect of dynamic factors on the output of tolerance specification. The results adhere to the ISO standard.

/pdf/a-computer-aided-tolerance-specification-method-based-on-4orrl1pql3.pdf

A computer-aided tolerance specification method based on multiple attributes decision-making

Flexible manufacturing systems (FMSs) have received increasing attention in recent decades as they conduct manufacturing in most productive and effective way, but productive output of such a system is closely related to internal and external criteria. FMS scheduling is the most affected area by these criteria, therefore, selecting appropriate and suitable scheduling types or dispatching rules, with respect to system criteria, is a crucial decision point for decision makers. According to the literature, many papers and researches have published to handle this problem in different methods such as mathematical programming, simulation, heuristic, and other similar techniques, but utilization of most of these methods is generally time-consuming, complex with some problems while performing, especially at a condition that a system requires decision making by a group of experts with a range of varied criteria. According to these issues, in this paper, fuzzy multi attribute decision making approach is used to develop a combined fuzzy analytical hierarchical process and fuzzy technique for order of preference by similarity to ideal solution group decision making model. This model is developed in three main stages and 13 steps and considers a group of decision makers’ evaluations to select the most suitable dispatching rule among a set of alternatives, with respect to real time criteria. Applying this model is simple, fast and considers all decision criteria and prevents system tardiness and idle time.

A model for selecting suitable dispatching rule in FMS based on fuzzy multi attribute group decision making

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Mohammad Ali Kashfi

Author Tools

Chat about Author

Papers

A model for selecting suitable dispatching rule in FMS based on fuzzy multi attribute group decision making