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Article
Publication date: 13 October 2020

Maintenance strategy selection: a combined goal programming approach and BWM-TOPSIS for paper production industry

Soroush Avakh Darestani, Tahereh Palizban and Rana Imannezhad

Correct and well-planned maintenance based on modern global methods directly affects efficiency, quality, direct production costs, reliability and profitability. The…

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Abstract

Purpose

Correct and well-planned maintenance based on modern global methods directly affects efficiency, quality, direct production costs, reliability and profitability. The selection of an optimal policy for maintenance can be a good solution for industrial units. In fact, by managing constraints such as costs, working hours and human workforce causing sudden equipment failure, production and performance can increase.

Design/methodology/approach

Therefore, in this research a model was presented to select the best maintenance strategy at Kaghaz Kar Kasra Co of Iran. In this study, it was tried to integrate the two techniques of goal programming and the technique for order of preference by similarity to ideal solution (TOPSIS) to prioritize maintenance strategies. First, all factors affecting maintenance were identified, and based on the Best Worst Method (BWM) the degree of their importance was determined.

Findings

After the evaluation, only 14 criteria in the 4 dimensions of cost, added value, safety and feasibility were selected. The highest points were given to the criteria of equipment cost and depreciation, equipment and personnel performance, equipment installation time and technical feasibility, respectively. In the next stage, using the TOPSIS method the item of maintenance strategy was ranked, and the 3 strategies of preventive maintenance (PM), predictive maintenance (PDM) and corrective maintenance (CM) were chosen. Modeling was performed utilizing a goal programming approach to select the optimal maintenance strategy for 13 devices. All the technical specifications, cost limits and the device time were extracted. After the model was finished and solved the best item for each device was specified.

Originality/value

1. Developing a goal programming model and decision-making dashboard. 2. Identifying the criteria and factors affecting the selection of the maintenance strategy for paper production Industry

Details

Journal of Quality in Maintenance Engineering, vol. ahead-of-print no. ahead-of-print
Type: Research Article
DOI: https://doi.org/10.1108/JQME-03-2019-0022
ISSN: 1355-2511

Keywords

  • Maintenance
  • Maintenance strategy
  • Best Worst Method (BWM)
  • TOPSIS
  • Goal programming

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Article
Publication date: 7 May 2019

Fuzzy overall equipment effectiveness and line performance measurement using artificial neural network

Mahsa Fekri Sari and Soroush Avakh Darestani

The overall equipment effectiveness (OEE) is a powerful metric in production as well as one of the methods in evaluating function for measuring productivity in the…

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Abstract

Purpose

The overall equipment effectiveness (OEE) is a powerful metric in production as well as one of the methods in evaluating function for measuring productivity in the production process. In the existing method, measuring OEE is based on three main elements consisting availability, performance and quality. The purpose of this paper is to evaluate the recognized metrics of production: OEE and overall line effectiveness (OLE) by using smart systems techniques.

Design/methodology/approach

In this paper, to improve the calculative methods and productivity with three methods: measuring OEE using Mamdani fuzzy inference systems (FIS), measuring OEE using Sugeno FIS, and measuring OLE using FIS and artificial neural networks (ANNs) are proposed.

Findings

The proposed methodologies aim to decrease some weaknesses of OEE and OLE methods by exploiting intelligent system techniques, such as FIS and ANNs. In particular, this research will solve the following issues that occur in manual and automatic data gathering. This technique is an effective way of measuring OEE and OLE with regard to different weights of losses as well as difference in the weight of the machines. In addition, it allows the operator’s knowledge to take a part in the measurement using uncertain input and output with implementation of linguistic terms. The presented method is the details and capabilities of those methods in various tested scenarios, and the results have been fully analyzed.

Originality/value

In relation to other methodologies, it allows the operator’s knowledge to take part in the measurement using uncertain input and output with implementation of linguistic terms. The presented method is the details and capabilities of those methods in various tested scenarios, and the results have been fully analyzed.

Details

Journal of Quality in Maintenance Engineering, vol. 25 no. 2
Type: Research Article
DOI: https://doi.org/10.1108/JQME-12-2017-0085
ISSN: 1355-2511

Keywords

  • Fuzzy inference systems
  • Total productive maintenance (TPM)
  • Artificial neural networks
  • Overall effectiveness equipment
  • Overall line efficiency
  • Fuzzy inference

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Article
Publication date: 29 July 2014

Development of fuzzy U control chart for monitoring defects

Soroush Avakh Darestani, Azam Moradi Tadi, Somayeh Taheri and Maryam Raeiszadeh

Shewhart's control charts are the most important statistical process control tools that play a role in inspecting and producing quality control. The purpose of this paper…

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Abstract

Purpose

Shewhart's control charts are the most important statistical process control tools that play a role in inspecting and producing quality control. The purpose of this paper is to investigate the attributes of fuzzy U control chart.

Design/methodology/approach

If the data were uncertain, they were converted into trapezoidal fuzzy number and the fuzzy upper and lower control limits were trapezoidal fuzzy number calculated using fuzzy mode approach. The result was grouped into four categories (in control, out of control, rather in control, rather out of control). Finally, a case study was presented and the method coding was done in MATLAB software using design U control chart; then, the results were verified.

Findings

The definition of fuzzy numbers for each type of defect sensitivity and the unit can be classified into four groups: in-control and out-of-control, rather in-control and rather out-of-control which represent the actual quality of the products. It can be concluded that fuzzy control chart is more sensitive on recognition out of control patterns.

Originality/value

This paper studies the use of control charts, specifically the attributes of a fuzzy U control chart, for monitoring defects in the format of a case study.

Details

International Journal of Quality & Reliability Management, vol. 31 no. 7
Type: Research Article
DOI: https://doi.org/10.1108/IJQRM-03-2013-0048
ISSN: 0265-671X

Keywords

  • Fuzzy control chart
  • Fuzzy process control
  • Fuzzy U-chart
  • Linguistic data
  • Monitoring defects

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Article
Publication date: 31 December 2015

Statistical process control: Fuzzy -S control chart and process capability indices in normal data environment

Soroush Avakh Darestani and Mina Nasiri

In this context, process capability indices (PCI) reveal the process zones base on specification limits (SLs). Most of the research on control charts assumed certain data…

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Abstract

Purpose

In this context, process capability indices (PCI) reveal the process zones base on specification limits (SLs). Most of the research on control charts assumed certain data. However, to measure quality characteristic, practitioners sometimes face with uncertain and linguistic variables. Fuzzy theory is one of the most applicable tools which academia has employed to deal with uncertainty. The paper aims to discuss these issues.

Design/methodology/approach

In this investigation, first, fuzzy and S control chart has been developed and second, the fuzzy formulation of the PCIs such as C pm ,C pmu ,C pml , C pmk , P p , P pl , P pu , P pk are constructed when SLs and measurements are at both triangular fuzzy numbers (TFNs) and trapezoidal fuzzy numbers (TrFNs) stages.

Findings

The results show that using fuzzy make more flexibility and sense on recognition of out-of-control warnings.

Research limitations/implications

For further research, the PCIs for non-normal data can be conducted based on TFN and TrFN.

Practical implications

The application case is related to a piston company in Konya’s industry area.

Originality/value

In the previous researches, for calculating C p , C pk , C pm and C pmk indices, the base approach was calculate standard deviation for a short term variation. For calculating these indices, the variation between subgroups are being ignored. Therefore, P p and P pk indices solved this fault by mentioning long term and short term variations. Therefore these two indices calculate the actual process capability.

Details

International Journal of Quality & Reliability Management, vol. 33 no. 1
Type: Research Article
DOI: https://doi.org/10.1108/IJQRM-08-2013-0130
ISSN: 0265-671X

Keywords

  • Fuzzy
  • Statistical process control
  • Process capability indices
  • Quality control chart

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